Open Science Framework, Protein Perceptions Study
Sarah Coffin, Social Psychology PhD Student, University of Colorado Boulder
November 3, 2022
#Data File
PP <- read.csv("Proteins_2022.03.28.csv", header = T, na.strings=c(".", "", " ", "NA", "-99"))
#Sample Size: Number of participants (rows)
nrow(PP)## [1] 1005Demographics
Age
#"How old are you?"
## Age Range, Descriptives, and Standard Deviation
range(PP$Dem_Age, na.rm = T)## [1] 13 83describe(PP$Demograph_Age)##
## NULLsd(PP$Dem_Age, na.rm = T)## [1] 15.1872Ethnicity
## Ethnicity: Which racial or ethnic group best describes you? (1 = Asian, Asian-American, 2 = Black, Black American, 3 = Hispanic/Latino-American, 4 = Native American, 5 = Native Pacific Islander, 6 = White/Caucasian-American, 7 = Other)
table(PP$Dem_Ethnicity)##
## 1 2 3 4 5 6 7
## 44 178 66 11 5 680 19PP$Ethnicity <- NA
PP$Ethnicity[PP$Dem_Ethnicity == 1] <- 'Asian'
PP$Ethnicity[PP$Dem_Ethnicity == 2] <- 'Black'
PP$Ethnicity[PP$Dem_Ethnicity == 3] <- 'Hispanic'
PP$Ethnicity[PP$Dem_Ethnicity == 4] <- 'Nat Amer'
PP$Ethnicity[PP$Dem_Ethnicity == 5] <- 'Nat Pac'
PP$Ethnicity[PP$Dem_Ethnicity == 6] <- 'White'
PP$Ethnicity[PP$Dem_Ethnicity == 7] <- 'Other'
describe(PP$Dem_Ethnicity)## PP$Dem_Ethnicity
## n missing distinct Info Mean Gmd
## 1003 2 7 0.682 4.865 1.718
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 44 178 66 11 5 680 19
## Proportion 0.044 0.177 0.066 0.011 0.005 0.678 0.019Education
# Education: Please indicate the highest level of education you have completed (1 = Elementary/Grammar School, 2 = Middle School, 3 = High School or Equivalent, 4 = Vocational/Technical School (2 years), 5 = Some College, 6 = College or University (4 years), 7 = Master's Degree (MS, MA, MBA, etc.), 8 = Doctoral Degree (PhD), 9 = Professional Degree (MD, JD, etc.).
PP$EdNum <- as.numeric(as.character(PP$Dem_Edu))
PP$EDU <- factor(PP$EdNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Elementary/Grammar School", "Middle School", "High School or Equivalent", "Vocational/Technical School (2 years)", "Some College", "College or University (4 years)", "Master's Degree (MS, MA, MBA, etc.)", "Doctoral Degree (PhD)", "Doctoral Degree (PhD)", "Other"))
table(PP$EDU)##
## Elementary/Grammar School Middle School
## 3 13
## High School or Equivalent Vocational/Technical School (2 years)
## 310 82
## Some College College or University (4 years)
## 296 191
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 80 23
## Other
## 5Income
# Please indicate your current household income in U.S. dollars. (Prefer Not to Say"; "Under $10,000"; "$10,000 - $19,999"; "$20,000 - $29,999"; "$30,000 - $39,999"; "$40,000 - $49,999"; "$50,000 - $74,999"; "$75,000 - $99,999"; "$100,000 - $149,999"; "$150,000 or More)
PP$SESNum <- as.numeric(as.character(PP$Dem_SES))
PP$SES <- factor(PP$SESNum, levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
labels = c("Prefer Not to Say", "Under $10,000", "$10,000 - $19,999", "$20,000 - $29,999", "$30,000 - $39,999", "$40,000 - $49,999", "$50,000 - $74,999", "$75,000 - $99,999", "$100,000 - $149,999", "$150,000 or More"))
table(PP$SES)##
## Prefer Not to Say Under $10,000 $10,000 - $19,999 $20,000 - $29,999
## 67 115 112 132
## $30,000 - $39,999 $40,000 - $49,999 $50,000 - $74,999 $75,000 - $99,999
## 135 101 156 83
## $100,000 - $149,999 $150,000 or More
## 65 37Type of Community/Living Environment
# Type of Community/Living Environment: Which of the following best describes the area you live in? (1 = Urban, 2 = Suburban, 3 = Rural)
PP$LivNum <- as.numeric(as.character(PP$Dem_Living))
PP$LIVING <- factor(PP$LivNum, levels = c(1, 2, 3),
labels = c("Urban", "Suburban", "Rural"))
table(PP$LIVING)##
## Urban Suburban Rural
## 316 425 262Political Identity
# Political Identity: Which of the following describes your political orientation? (1 = Strongly Conservative, 2 = Moderately Conservative, 3 = Slightly Conservative, 4 = Neither Conservative Nor Liberal, 5 = Slightly Liberal, 6 = Moderately Liberal, 7 = Strongly Liberal)
PP$polOR <- factor(PP$PI_Orientation, levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Strongly Conservative", "Moderately Conservative", "Slightly Conservative", "Neither Conservative Nor Liberal", "Slightly Liberal", "Moderately Liberal", "Strongly Liberal"))
table(PP$polOR)##
## Strongly Conservative Moderately Conservative
## 126 171
## Slightly Conservative Neither Conservative Nor Liberal
## 124 301
## Slightly Liberal Moderately Liberal
## 93 93
## Strongly Liberal
## 94# Political Orientation: Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
PP$Orientation = as.numeric(recode_factor(PP$PI_Orientation,'1'= "3",'2'= "2",'3'= "1",
'4'= "0",'5'= "-1", '6'= "-2", '7'= "-3"))
describe(PP$Orientation)## PP$Orientation
## n missing distinct Info Mean Gmd
## 1002 3 7 0.962 3.718 2.003
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 126 171 124 301 93 93 94
## Proportion 0.126 0.171 0.124 0.300 0.093 0.093 0.094hist(PP$Orientation , main = 'Political Orientation (Liberal to Conservative)')
# Political Party
##Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what? (1 = Republican, 2 = Democrat, 3 = Independent, 4 = Other (write-in), 5 = No Preference)
describe(PP$Party)##
## NULLPP$Party <- PP$Party
PP$DemStrength <- PP$DStrength
PP$RepStrength <- PP$RStrength
PP$PartyClose <- PP$Closerto
# Recode Party
PP$PartyFull <- NA
PP$PartyFull[PP$DemStrength == 1] <- -3
PP$PartyFull[PP$DemStrength == 2] <- -2
PP$PartyFull[PP$PartyClose == 1] <- -1
PP$PartyFull[PP$PartyClose == 3] <- 0
PP$PartyFull[PP$PartyClose == 2] <- 1
PP$PartyFull[PP$RepStrength == 2] <- 2
PP$PartyFull[PP$RepStrength == 1] <- 3
describe(PP$PartyFull)## PP$PartyFull
## n missing distinct Info Mean Gmd
## 996 9 7 0.967 -0.1797 2.495
##
## lowest : -3 -2 -1 0 1, highest: -1 0 1 2 3
##
## Value -3 -2 -1 0 1 2 3
## Frequency 227 136 66 212 65 95 195
## Proportion 0.228 0.137 0.066 0.213 0.065 0.095 0.196hist(PP$PartyFull , main = 'Party Identification')
#New Variable: Ideology
PP$Ideology <- rowMeans(PP[, c('PartyFull', 'Orientation')], na.rm=T)
describe(PP$Ideology)## PP$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 1003 2 14 0.949 1.785 1.155 -0.5 0.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.5 3.0 3.5
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 3.5 4.0 4.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
## Frequency 25 27 35 80 79 142 357 126 52 49 14
## Proportion 0.025 0.027 0.035 0.080 0.079 0.142 0.356 0.126 0.052 0.049 0.014
##
## Value 4.5 5.0 6.0
## Frequency 6 9 2
## Proportion 0.006 0.009 0.002hist(PP$Ideology)
Sex
# Frequencies: Sex (1 = female, 2 = male, 3 = other)
PP$Dem_Sex <- factor(PP$LivNum, levels = c(1, 2, 3),
labels = c("Female", "Male", "Other"))
table(PP$Dem_Sex)##
## Female Male Other
## 316 425 262PP$Dem_Sex <- as.numeric(as.character(PP$Dem_Gen))
describe(PP$Dem_Sex)## PP$Dem_Sex
## n missing distinct Info Mean Gmd
## 1003 2 3 0.714 1.393 0.4852
##
## Value 1 2 3
## Frequency 614 384 5
## Proportion 0.612 0.383 0.005Scales
Animal Welfare
# Animal Welfare: How much do you agree or disagree with the following statements?
## Item 1: It is important to me that my food is produced in a way that animals have not experienced pain.
## Item 2: It is important to me that my food is produced in a way that animals' rights have been respected.
#Descriptives
describe(PP$AW_1)## PP$AW_1
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 99 0.99 69.77 30.34 16 28
## .25 .50 .75 .90 .95
## 52 75 96 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$ATNS_1, na.rm=TRUE)## Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning Inf## Warning in max(x, na.rm = na.rm): no non-missing arguments to max; returning
## -Inf## [1] Inf -Infdescribe(PP$AW_2)## PP$AW_2
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 95 0.991 71.28 28.84 19.9 34.0
## .25 .50 .75 .90 .95
## 53.0 75.0 95.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$ATNS_2, na.rm=TRUE)## Warning in min(x, na.rm = na.rm): no non-missing arguments to min; returning Inf
## Warning in min(x, na.rm = na.rm): no non-missing arguments to max; returning
## -Inf## [1] Inf -Inf#Histograms
hist(PP$AW_1, main = 'It is important to me that my food is produced in a way that animals have not experienced pain.')
hist(PP$AW_2, main = 'It is important to me that my food is produced in a way that animals rights have been respected.')
#Cronbach's Alpha
PP$AW_Scale <- data.frame(PP$AW_1, PP$AW_2)
PP$AW_Score <- rowMeans(PP [, c("AW_1", "AW_2")], na.rm=TRUE)
describe(PP$AW_Score)## PP$AW_Score
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 172 0.995 70.53 27.43 25.0 39.5
## .25 .50 .75 .90 .95
## 52.0 73.5 92.5 100.0 100.0
##
## lowest : 0.0 1.0 2.0 2.5 3.0, highest: 98.0 98.5 99.0 99.5 100.0psych::alpha(PP$AW_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$AW_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.69 0.69 4.5 0.011 71 25 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.8 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.AW_1 0.73 0.69 0.48 0.69 2.3 NA 0 0.69
## PP.AW_2 0.66 0.69 0.48 0.69 2.3 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.AW_1 1001 0.92 0.92 0.77 0.69 70 27
## PP.AW_2 1000 0.92 0.92 0.77 0.69 71 26Aversion to Tampering with Nature
# Aversion to Tampering with Nature: How much do you agree or disagree with the following statements?
## Item 1: People who push for technological fixes to environmental problems are underestimating the risks.
## Item 2: People who say we shouldn’t tamper with nature are just being naïve.
## Item 3: Human beings have no right to meddle with the natural environment.
## Item 4: I would prefer to live in a world where humans leave nature alone.
## Item 5: Altering nature will be our downfall as a species.
# Item Definitions
PP$ATNS_1 <- as.numeric(as.character(PP$ATNS_36))
PP$ATNS_2 <- as.numeric(as.character(PP$ATNS_37))
PP$ATNS_3 <- as.numeric(as.character(PP$ATNS_38))
PP$ATNS_4 <- as.numeric(as.character(PP$ATNS_39))
PP$ATNS_5 <- as.numeric(as.character(PP$ATNS_40))
# Reverse Code Item 2
PP$ATNS_2R <- (100- PP$ATNS_2)
describe(PP$ATNS_2R)## PP$ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 999 6 101 0.999 48.8 35.98 0 8
## .25 .50 .75 .90 .95
## 24 47 75 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100# Descriptives
describe(PP$ATNS_1)## PP$ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 98 0.998 63.65 29.45 15 26
## .25 .50 .75 .90 .95
## 50 66 83 100 100
##
## lowest : 0 1 3 5 7, highest: 96 97 98 99 100range(PP$ATNS_1, na.rm=TRUE)## [1] 0 100describe(PP$ATNS_2)## PP$ATNS_2
## n missing distinct Info Mean Gmd .05 .10
## 999 6 101 0.999 51.2 35.98 0 1
## .25 .50 .75 .90 .95
## 25 53 76 92 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$ATNS_2, na.rm=TRUE)## [1] 0 100describe(PP$ATNS_3)## PP$ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 101 0.998 63.75 30.8 11.95 25.00
## .25 .50 .75 .90 .95
## 46.00 68.00 85.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$ATNS_3, na.rm=TRUE)## [1] 0 100describe(PP$ATNS_4)## PP$ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 98 0.995 67.9 29.41 17 30
## .25 .50 .75 .90 .95
## 52 72 88 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$ATNS_4, na.rm=TRUE)## [1] 0 100describe(PP$ATNS_5)## PP$ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 97 0.996 68.31 29.34 17 30
## .25 .50 .75 .90 .95
## 52 73 90 100 100
##
## lowest : 0 1 2 3 5, highest: 96 97 98 99 100range(PP$ATNS_5, na.rm=TRUE)## [1] 0 100#Aversion to Tampering with Nature Scale Histograms by Item (No reversed codes)
hist(PP$ATNS_1, main = 'ATNS #1: People who push for technological fixes to environmental problems are underestimating the risks.')
hist(PP$ATNS_2R, main = 'ATNS #2: People who say we shouldn’t tamper with nature are just being naïve.')
hist(PP$ATNS_3, main = 'ATNS #3: Human beings have no right to meddle with the natural environment.')
hist(PP$ATNS_4, main = 'ATNS #4: I would prefer to live in a world where humans leave nature alone.')
hist(PP$ATNS_5, main = 'ATNS #5: Altering nature will be our downfall as a species.')
#Cronbach's Alpha (Item 2 reverse coded)
PP$ATNS_Scale <- data.frame(PP$ATNS_1, PP$ATNS_2R, PP$ATNS_3, PP$ATNS_4, PP$ATNS_5)
PP$ATNS_Score <- rowMeans(PP [, c("ATNS_1", "ATNS_2R", "ATNS_3", "ATNS_4", "ATNS_5")], na.rm=TRUE)
psych::alpha(PP$ATNS_Scale)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(PP$ATNS_Scale): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.ATNS_2R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = PP$ATNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.62 0.65 0.65 0.27 1.8 0.019 62 17 0.39
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.62 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.ATNS_1 0.56 0.59 0.58 0.26 1.4 0.023 0.0722 0.25
## PP.ATNS_2R 0.77 0.77 0.72 0.46 3.3 0.012 0.0039 0.45
## PP.ATNS_3 0.47 0.50 0.50 0.20 1.0 0.028 0.0625 0.19
## PP.ATNS_4 0.49 0.52 0.53 0.22 1.1 0.027 0.0736 0.22
## PP.ATNS_5 0.48 0.51 0.52 0.21 1.0 0.027 0.0670 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.ATNS_1 1001 0.64 0.66 0.530 0.400 64 26
## PP.ATNS_2R 999 0.34 0.29 -0.014 -0.017 49 31
## PP.ATNS_3 1000 0.76 0.77 0.722 0.568 64 27
## PP.ATNS_4 1000 0.73 0.74 0.662 0.526 68 26
## PP.ATNS_5 1002 0.74 0.76 0.695 0.551 68 26describe(PP$ATNS_Scale)## PP$ATNS_Scale
##
## 5 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.ATNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 98 0.998 63.65 29.45 15 26
## .25 .50 .75 .90 .95
## 50 66 83 100 100
##
## lowest : 0 1 3 5 7, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_2R
## n missing distinct Info Mean Gmd .05 .10
## 999 6 101 0.999 48.8 35.98 0 8
## .25 .50 .75 .90 .95
## 24 47 75 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 101 0.998 63.75 30.8 11.95 25.00
## .25 .50 .75 .90 .95
## 46.00 68.00 85.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 98 0.995 67.9 29.41 17 30
## .25 .50 .75 .90 .95
## 52 72 88 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.ATNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 97 0.996 68.31 29.34 17 30
## .25 .50 .75 .90 .95
## 52 73 90 100 100
##
## lowest : 0 1 2 3 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------Benefit
# Benefit perception was measured with 3 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Benefit score calculated by averaging these items.
### Item 1: This is beneficial to my health.
### Item 2: This is beneficial to society.
### Item 3: This is beneficial to the environment.Grain-fed Feedlot Burger (GFFB)
#GFFB
PP$Benefit_1_GFFB <- PP$GFFB_Benefit_18
describe(PP$Benefit_1_GFFB)## PP$Benefit_1_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 95 0.998 56.94 33.7 0 15
## .25 .50 .75 .90 .95
## 36 59 80 100 100
##
## lowest : 0 1 2 3 5, highest: 95 97 98 99 100range(PP$Benefit_1_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_GFFB, main = 'GFFB - This is beneficial to my health.')
PP$Benefit_2_GFFB <- PP$GFFB_Benefit_40
describe(PP$Benefit_2_GFFB)## PP$Benefit_2_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 97 0.999 56.99 33.41 0 12
## .25 .50 .75 .90 .95
## 37 60 80 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_2_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_GFFB, main = 'GFFB - This is beneficial to society.')
PP$Benefit_3_GFFB <- PP$GFFB_Benefit_41
describe(PP$Benefit_3_GFFB)## PP$Benefit_3_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 495 510 101 0.999 54.58 33.36 0.0 11.4
## .25 .50 .75 .90 .95
## 33.0 55.0 76.5 96.6 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_3_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_GFFB, main = 'GFFB - This is beneficial to the environment.')
#GFFB Benefit Scale
PP$Ben_Score_GFFB <- rowMeans(PP [, c("Benefit_1_GFFB", "Benefit_2_GFFB", "Benefit_3_GFFB")], na.rm=TRUE)
describe(PP$Ben_Score_GFFB)## PP$Ben_Score_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 219 0.999 56.2 30.81 2.267 17.533
## .25 .50 .75 .90 .95
## 39.667 56.000 76.667 95.800 100.000
##
## lowest : 0.0000000 0.6666667 1.0000000 1.6666667 2.0000000
## highest: 98.3333333 99.0000000 99.3333333 99.6666667 100.0000000sd(PP$Ben_Score_GFFB, na.rm = TRUE)## [1] 27.07308PP$Ben_Scale_GFFB <- data.frame(PP$Benefit_1_GFFB, PP$Benefit_2_GFFB, PP$Benefit_3_GFFB)
#GFFB Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_GFFB, PP$Benefit_2_GFFB, PP$Benefit_3_GFFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_GFFB, PP$Benefit_2_GFFB,
## PP$Benefit_3_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.88 0.78 11 0.0046 56 27 0.78
##
## lower alpha upper 95% confidence boundaries
## 0.91 0.92 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_GFFB 0.89 0.89 0.79 0.79 7.7 0.0072 NA
## PP.Benefit_2_GFFB 0.87 0.87 0.78 0.78 7.0 0.0079 NA
## PP.Benefit_3_GFFB 0.88 0.88 0.78 0.78 7.1 0.0078 NA
## med.r
## PP.Benefit_1_GFFB 0.79
## PP.Benefit_2_GFFB 0.78
## PP.Benefit_3_GFFB 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_GFFB 497 0.92 0.92 0.86 0.82 57 29
## PP.Benefit_2_GFFB 497 0.93 0.93 0.87 0.83 57 29
## PP.Benefit_3_GFFB 495 0.93 0.93 0.87 0.83 55 29hist(PP$Ben_Score_GFFB, main = 'GFFB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_GFFB, labels = c('1','2', '3'), main = "Correlation Between GFFB Benefit Items")
Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$Benefit_1_GFPRB <- PP$GFPRB_Benefit_18
describe(PP$Benefit_1_GFPRB)## PP$Benefit_1_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 89 0.996 68.8 29.34 19 31
## .25 .50 .75 .90 .95
## 52 72 91 100 100
##
## lowest : 0 1 4 7 10, highest: 96 97 98 99 100range(PP$Benefit_1_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_GFPRB, main = 'GFPRB - This is beneficial to my health.')
PP$Benefit_2_GFPRB <- PP$GFPRB_Benefit_40
describe(PP$Benefit_2_GFPRB)## PP$Benefit_2_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 88 0.995 68.27 28.95 22.0 31.0
## .25 .50 .75 .90 .95
## 52.0 71.5 90.0 100.0 100.0
##
## lowest : 0 2 3 5 7, highest: 96 97 98 99 100range(PP$Benefit_2_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_GFPRB, main = 'GFPRB - This is beneficial to society.')
PP$Benefit_3_GFPRB <- PP$GFPRB_Benefit_41
describe(PP$Benefit_3_GFPRB)## PP$Benefit_3_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 94 0.996 64.86 31.85 13.6 24.0
## .25 .50 .75 .90 .95
## 49.0 68.0 89.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_3_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_GFPRB, main = 'GFPRB - This is beneficial to the environment.')
#GFPRB Benefit Scale
PP$Ben_Score_GFPRB <- rowMeans(PP [, c("Benefit_1_GFPRB", "Benefit_2_GFPRB", "Benefit_3_GFPRB")], na.rm=TRUE)
describe(PP$Ben_Score_GFPRB)## PP$Ben_Score_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 193 0.998 67.33 27.03 25.87 33.43
## .25 .50 .75 .90 .95
## 52.42 67.00 87.25 100.00 100.00
##
## lowest : 0.0000000 0.3333333 1.3333333 2.3333333 4.0000000
## highest: 98.6666667 99.0000000 99.3333333 99.6666667 100.0000000sd(PP$Ben_Score_GFPRB, na.rm = TRUE)## [1] 24.01472PP$Ben_Scale_GFPRB <- data.frame(PP$Benefit_1_GFPRB, PP$Benefit_2_GFPRB, PP$Benefit_3_GFPRB)
#GFPRB Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_GFPRB, PP$Benefit_2_GFPRB, PP$Benefit_3_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_GFPRB, PP$Benefit_2_GFPRB,
## PP$Benefit_3_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.83 0.71 7.5 0.0065 67 24 0.71
##
## lower alpha upper 95% confidence boundaries
## 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_GFPRB 0.83 0.83 0.71 0.71 4.9 0.0108 NA
## PP.Benefit_2_GFPRB 0.82 0.82 0.69 0.69 4.5 0.0115 NA
## PP.Benefit_3_GFPRB 0.85 0.85 0.74 0.74 5.7 0.0095 NA
## med.r
## PP.Benefit_1_GFPRB 0.71
## PP.Benefit_2_GFPRB 0.69
## PP.Benefit_3_GFPRB 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_GFPRB 514 0.9 0.90 0.83 0.77 69 26
## PP.Benefit_2_GFPRB 514 0.9 0.91 0.84 0.79 68 26
## PP.Benefit_3_GFPRB 513 0.9 0.89 0.80 0.75 65 28hist(PP$Ben_Score_GFPRB, main = 'GFPRB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_GFPRB, labels = c('1','2', '3'), main = "Correlation Between GFPRB Benefit Items")
Cultured beef burgers (CBB)
#CBB
PP$Benefit_1_CBB <- PP$CBB_Benefit_18
describe(PP$Benefit_1_CBB)## PP$Benefit_1_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 99 0.998 48.75 36.32 0.00 0.00
## .25 .50 .75 .90 .95
## 24.25 51.00 75.00 95.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_1_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_CBB, main = 'CBB - This is beneficial to my health.')
PP$Benefit_2_CBB <- PP$CBB_Benefit_40
describe(PP$Benefit_2_CBB)## PP$Benefit_2_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 97 0.999 53.39 34.83 0.0 4.0
## .25 .50 .75 .90 .95
## 32.0 54.0 78.5 95.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_2_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_CBB, main = 'CBB - This is beneficial to society.')
PP$Benefit_3_CBB <- PP$CBB_Benefit_41
describe(PP$Benefit_3_CBB)## PP$Benefit_3_CBB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 97 0.998 55.54 35.25 0.0 4.2
## .25 .50 .75 .90 .95
## 34.0 59.0 80.0 98.8 100.0
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100range(PP$Benefit_3_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_CBB, main = 'CBB - This is beneficial to the environment.')
#Benefit Scale
PP$Ben_Score_CBB <- rowMeans(PP [, c("Benefit_1_CBB", "Benefit_2_CBB", "Benefit_3_CBB")], na.rm=TRUE)
describe(PP$Ben_Score_CBB)## PP$Ben_Score_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 228 1 52.55 32.46 0.00 8.10
## .25 .50 .75 .90 .95
## 34.00 53.17 73.67 91.70 99.78
##
## lowest : 0.0000000 0.3333333 0.6666667 1.0000000 1.3333333
## highest: 98.3333333 99.0000000 99.3333333 99.6666667 100.0000000sd(PP$Ben_Score_CBB, na.rm = TRUE)## [1] 28.37883PP$Ben_Scale_CBB <- data.frame(PP$Benefit_1_CBB, PP$Benefit_2_CBB, PP$Benefit_3_CBB)
#Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_CBB, PP$Benefit_2_CBB, PP$Benefit_3_CBB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_CBB, PP$Benefit_2_CBB,
## PP$Benefit_3_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.87 0.77 9.9 0.005 53 28 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_CBB 0.89 0.89 0.80 0.80 8.0 0.0070 NA 0.80
## PP.Benefit_2_CBB 0.86 0.86 0.75 0.75 6.0 0.0091 NA 0.75
## PP.Benefit_3_CBB 0.86 0.86 0.76 0.76 6.2 0.0088 NA 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_CBB 514 0.91 0.91 0.83 0.79 49 32
## PP.Benefit_2_CBB 514 0.92 0.93 0.87 0.83 53 30
## PP.Benefit_3_CBB 513 0.92 0.92 0.87 0.83 56 31hist(PP$Ben_Score_CBB, main = 'CBB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_CBB, labels = c('1','2', '3'), main = "Correlation Between CBB Benefit Items")
Plant-based Protein Burger (PBPB)
#PBPB
PP$Benefit_1_PBPB <- PP$PBPB_Benefit_18
describe(PP$Benefit_1_PBPB)## PP$Benefit_1_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 520 485 93 0.998 61.32 32.7 0 18
## .25 .50 .75 .90 .95
## 42 66 84 100 100
##
## lowest : 0 1 3 4 6, highest: 96 97 98 99 100range(PP$Benefit_1_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_PBPB, main = 'PBPB - This is beneficial to my health.')
PP$Benefit_2_PBPB <- PP$PBPB_Benefit_40
describe(PP$Benefit_2_PBPB)## PP$Benefit_2_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 520 485 97 0.998 60.76 32.99 0.00 17.90
## .25 .50 .75 .90 .95
## 40.75 66.00 83.25 100.00 100.00
##
## lowest : 0 1 3 4 6, highest: 96 97 98 99 100range(PP$Benefit_2_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_PBPB, main = 'PBPB - This is beneficial to society.')
PP$Benefit_3_PBPB <- PP$PBPB_Benefit_41
describe(PP$Benefit_3_PBPB)## PP$Benefit_3_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 520 485 92 0.998 62.35 31.35 0 23
## .25 .50 .75 .90 .95
## 46 67 83 100 100
##
## lowest : 0 2 3 6 8, highest: 96 97 98 99 100range(PP$Benefit_3_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_PBPB, main = 'PBPB - This is beneficial to the environment.')
#Benefit Scale
PP$Ben_Score_PBPB <- rowMeans(PP [, c("Benefit_1_PBPB", "Benefit_2_PBPB", "Benefit_3_PBPB")], na.rm=TRUE)
describe(PP$Ben_Score_PBPB)## PP$Ben_Score_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 521 484 207 1 61.46 29.49 7.667 24.333
## .25 .50 .75 .90 .95
## 47.333 62.667 81.333 96.333 100.000
##
## lowest : 0.0000000 0.3333333 0.6666667 1.0000000 3.3333333
## highest: 97.3333333 98.6666667 99.0000000 99.3333333 100.0000000sd(PP$Ben_Score_PBPB, na.rm = TRUE)## [1] 26.1661PP$Ben_Scale_PBPB <- data.frame(PP$Benefit_1_PBPB, PP$Benefit_2_PBPB, PP$Benefit_3_PBPB)
#Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_PBPB, PP$Benefit_2_PBPB, PP$Benefit_3_PBPB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_PBPB, PP$Benefit_2_PBPB,
## PP$Benefit_3_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.86 0.76 9.3 0.0053 61 26 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_PBPB 0.87 0.87 0.76 0.76 6.5 0.0085 NA
## PP.Benefit_2_PBPB 0.84 0.84 0.73 0.73 5.4 0.0098 NA
## PP.Benefit_3_PBPB 0.87 0.87 0.78 0.78 6.9 0.0080 NA
## med.r
## PP.Benefit_1_PBPB 0.76
## PP.Benefit_2_PBPB 0.73
## PP.Benefit_3_PBPB 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_PBPB 520 0.91 0.91 0.84 0.80 61 29
## PP.Benefit_2_PBPB 520 0.93 0.92 0.87 0.83 61 29
## PP.Benefit_3_PBPB 520 0.91 0.91 0.83 0.79 62 28hist(PP$Ben_Score_PBPB, main = 'PBPB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_PBPB, labels = c('1','2', '3'), main = "Correlation Between PBPB Benefit Items")
Plant-based Fermentation Burger (PBFB)
#PBFB
PP$Benefit_1_PBFB <- PP$PBFB_Benefit_18
describe(PP$Benefit_1_PBFB)## PP$Benefit_1_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 97 0.998 55.03 36.29 0 0
## .25 .50 .75 .90 .95
## 33 57 81 97 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_1_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_PBFB, main = 'PBFB - This is beneficial to my health.')
PP$Benefit_2_PBFB <- PP$PBFB_Benefit_40
describe(PP$Benefit_2_PBFB)## PP$Benefit_2_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 97 0.998 57.11 34.94 0.0 2.8
## .25 .50 .75 .90 .95
## 36.0 61.0 82.0 97.0 100.0
##
## lowest : 0 1 2 3 5, highest: 95 96 97 99 100range(PP$Benefit_2_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_PBFB, main = 'PBFB - This is beneficial to society.')
PP$Benefit_3_PBFB <- PP$PBFB_Benefit_41
describe(PP$Benefit_3_PBFB)## PP$Benefit_3_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 93 0.998 59.31 33.82 0.0 4.8
## .25 .50 .75 .90 .95
## 39.0 64.0 82.5 99.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Benefit_3_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_PBFB, main = 'PBFB - This is beneficial to the environment.')
#Benefit Scale
PP$Ben_Score_PBFB <- rowMeans(PP [, c("Benefit_1_PBFB", "Benefit_2_PBFB", "Benefit_3_PBFB")], na.rm=TRUE)
describe(PP$Ben_Score_PBFB)## PP$Ben_Score_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 202 1 57.15 32.14 0.00 8.60
## .25 .50 .75 .90 .95
## 39.67 57.33 80.67 95.00 100.00
##
## lowest : 0.0000000 0.3333333 0.6666667 1.3333333 1.6666667
## highest: 98.0000000 98.3333333 99.0000000 99.6666667 100.0000000sd(PP$Ben_Score_PBFB, na.rm = TRUE)## [1] 28.36827PP$Ben_Scale_PBFB <- data.frame(PP$Benefit_1_PBFB, PP$Benefit_2_PBFB, PP$Benefit_3_PBFB)
#Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_PBFB, PP$Benefit_2_PBFB, PP$Benefit_3_PBFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_PBFB, PP$Benefit_2_PBFB,
## PP$Benefit_3_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.87 0.78 10 0.0048 57 28 0.76
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_PBFB 0.86 0.86 0.76 0.76 6.3 0.0086 NA
## PP.Benefit_2_PBFB 0.86 0.86 0.76 0.76 6.3 0.0086 NA
## PP.Benefit_3_PBFB 0.89 0.89 0.80 0.80 8.2 0.0068 NA
## med.r
## PP.Benefit_1_PBFB 0.76
## PP.Benefit_2_PBFB 0.76
## PP.Benefit_3_PBFB 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_PBFB 479 0.93 0.93 0.88 0.83 55 32
## PP.Benefit_2_PBFB 479 0.93 0.93 0.88 0.83 57 31
## PP.Benefit_3_PBFB 479 0.91 0.91 0.84 0.80 59 30hist(PP$Ben_Score_PBFB, main = 'PBFB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_PBFB, labels = c('1','2', '3'), main = "Correlation Between PBFB Benefit Items")
Veggie Burger (VB)
#VB
PP$Benefit_1_VB <- PP$VB_Benefit_18
describe(PP$Benefit_1_VB)## PP$Benefit_1_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 89 0.995 67.68 30.84 15.5 27.0
## .25 .50 .75 .90 .95
## 52.0 71.0 91.0 100.0 100.0
##
## lowest : 0 4 5 6 9, highest: 96 97 98 99 100range(PP$Benefit_1_VB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_1_VB, main = 'VB - This is beneficial to my health.')
PP$Benefit_2_VB <- PP$VB_Benefit_40
describe(PP$Benefit_2_VB)## PP$Benefit_2_VB
## n missing distinct Info Mean Gmd .05 .10
## 470 535 87 0.995 67.49 30.23 18.00 28.90
## .25 .50 .75 .90 .95
## 51.00 73.00 88.75 100.00 100.00
##
## lowest : 0 3 4 5 13, highest: 96 97 98 99 100range(PP$Benefit_2_VB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_2_VB, main = 'VB - This is beneficial to society.')
PP$Benefit_3_VB <- PP$VB_Benefit_41
describe(PP$Benefit_3_VB)## PP$Benefit_3_VB
## n missing distinct Info Mean Gmd .05 .10
## 470 535 90 0.995 68.14 29.79 17.45 31.80
## .25 .50 .75 .90 .95
## 52.00 72.00 90.00 100.00 100.00
##
## lowest : 0 3 4 5 12, highest: 96 97 98 99 100range(PP$Benefit_3_VB, na.rm=TRUE)## [1] 0 100hist(PP$Benefit_3_VB, main = 'VB - This is beneficial to the environment.')
#VB Benefit Scale
PP$Ben_Score_VB <- rowMeans(PP [, c("Benefit_1_VB", "Benefit_2_VB", "Benefit_3_VB")], na.rm=TRUE)
describe(PP$Ben_Score_VB)## PP$Ben_Score_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 189 0.999 67.74 27.66 23.83 35.33
## .25 .50 .75 .90 .95
## 52.00 70.00 87.00 100.00 100.00
##
## lowest : 0.000000 2.666667 11.000000 13.666667 19.000000
## highest: 98.333333 98.666667 99.333333 99.666667 100.000000sd(PP$Ben_Score_VB, na.rm = TRUE)## [1] 24.60428PP$Ben_Scale_VB <- data.frame(PP$Benefit_1_VB, PP$Benefit_2_VB, PP$Benefit_3_VB)
#Cronbach's alpha for benefit scale
psych::alpha(data.frame(PP$Benefit_1_VB, PP$Benefit_2_VB, PP$Benefit_3_VB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Benefit_1_VB, PP$Benefit_2_VB,
## PP$Benefit_3_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.85 0.74 8.5 0.0058 68 25 0.74
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_VB 0.85 0.85 0.75 0.75 5.9 0.0092 NA 0.75
## PP.Benefit_2_VB 0.84 0.84 0.73 0.73 5.4 0.0098 NA 0.73
## PP.Benefit_3_VB 0.85 0.85 0.74 0.74 5.7 0.0095 NA 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_VB 471 0.91 0.91 0.83 0.79 68 28
## PP.Benefit_2_VB 470 0.91 0.91 0.84 0.80 67 27
## PP.Benefit_3_VB 470 0.91 0.91 0.84 0.79 68 27hist(PP$Ben_Score_VB, main = 'VB Benefit Scale Score')
#Correlation
cor.plot(PP$Ben_Scale_VB, labels = c('1','2', '3'), main = "Correlation Between VB Benefit Items")
Climate Change Belief
# Climate Change Belief: How much do you agree or disagree with the following statements?
## Item #1: Climate change is happening.
## Item #2: Climate change poses a risk to human health, safety, and prosperity.
## Item #3: Human activity is largely responsible for recent climate change.
## Item #4: Reducing greenhouse gas emissions will reduce global warming and climate change.
## Item Definitions
PP$CCBelief_1 <- as.numeric(as.character(PP$CCB_48))
PP$CCBelief_2 <- as.numeric(as.character(PP$CCB_49))
PP$CCBelief_3 <- as.numeric(as.character(PP$CCB_50))
PP$CCBelief_4 <- as.numeric(as.character(PP$CCB_51))
#Climate Change Belief Descriptives
describe(PP$CCBelief_1)## PP$CCBelief_1
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 94 0.98 75.62 27.18 22 38
## .25 .50 .75 .90 .95
## 63 82 100 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$CCBelief_1, na.rm=TRUE)## [1] 0 100describe(PP$CCBelief_2)## PP$CCBelief_2
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 94 0.985 72.26 29.56 20 33
## .25 .50 .75 .90 .95
## 55 78 99 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(PP$CCBelief_2, na.rm=TRUE)## [1] 0 100describe(PP$CCBelief_3)## PP$CCBelief_3
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 94 0.986 73 28.95 18 34
## .25 .50 .75 .90 .95
## 58 78 98 100 100
##
## lowest : 0 1 2 3 5, highest: 96 97 98 99 100range(PP$CCBelief_3, na.rm=TRUE)## [1] 0 100describe(PP$CCBelief_4)## PP$CCBelief_4
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 97 0.994 69.18 29.37 19 32
## .25 .50 .75 .90 .95
## 52 73 93 100 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(PP$CCBelief_4, na.rm=TRUE)## [1] 0 100#Climate Change Belief Histograms
hist(PP$CCBelief_1, main = 'Climate Change Belief #1: Climate change is happening."')
hist(PP$CCBelief_2, main = 'Climate Change Belief #2:Climate change poses a risk to human health, safety, and prosperity."')
hist(PP$CCBelief_3, main = 'Climate Change Belief #3:Human activity is largely responsible for recent climate change."')
hist(PP$CCBelief_4, main = 'Climate Change Belief #4: Reducing greenhouse gas emissions will reduce global warming and climate change."')
PP$CCBelief_Score <- rowMeans(PP[, c('CCBelief_1', 'CCBelief_2', 'CCBelief_3','CCBelief_4')], na.rm=T)
describe(PP$CCBelief_Score)## PP$CCBelief_Score
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 275 0.997 72.51 25.73 31.75 44.75
## .25 .50 .75 .90 .95
## 56.00 75.25 93.25 100.00 100.00
##
## lowest : 0.00 0.50 0.75 1.00 1.25, highest: 99.00 99.25 99.50 99.75 100.00#Cronbach's Alpha
PP$CCB_Scale <- data.frame(PP$CCB_48, PP$CCB_49, PP$CCB_50, PP$CCB_51)
psych::alpha(PP$CCB_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.88 0.71 9.8 0.0048 73 23 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CCB_48 0.87 0.87 0.82 0.69 6.5 0.0072 0.0011 0.70
## PP.CCB_49 0.87 0.87 0.82 0.69 6.7 0.0072 0.0035 0.66
## PP.CCB_50 0.88 0.88 0.84 0.71 7.5 0.0065 0.0037 0.70
## PP.CCB_51 0.90 0.90 0.86 0.75 9.0 0.0055 0.0013 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CCB_48 1001 0.90 0.91 0.87 0.83 76 25
## PP.CCB_49 1001 0.90 0.90 0.86 0.82 72 27
## PP.CCB_50 1001 0.88 0.88 0.82 0.78 73 27
## PP.CCB_51 1001 0.85 0.85 0.77 0.73 69 26PP$CCBelief_Score <- rowMeans(PP[, c('CCBelief_1', 'CCBelief_2', 'CCBelief_3','CCBelief_4')], na.rm=T)
#Correlation CCB
cor(PP$CCB_Scale, use= "complete.obs")## PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51
## PP.CCB_48 1.0000000 0.7821006 0.7572658 0.6629838
## PP.CCB_49 0.7821006 1.0000000 0.7100200 0.6986891
## PP.CCB_50 0.7572658 0.7100200 1.0000000 0.6478465
## PP.CCB_51 0.6629838 0.6986891 0.6478465 1.0000000Connectedness to Nature
# Connectedness to Nature was measured with 5 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Connected to nature score was calculated by averaging these items.
## Item 1: I often feel a sense of oneness with the natural world around me.'
## Item 2: I think of the natural world as a community to which I belong.'
## Item 3: I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’.
## Item 4: My personal welfare is independent of the welfare of the natural world.
## Item 5: When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.#Connectedness to Nature Item Definitions
PP$CNS_1 <- as.numeric(as.character(PP$CNS_29))
PP$CNS_2 <- as.numeric(as.character(PP$CNS_30))
PP$CNS_3 <- as.numeric(as.character(PP$CNS_31))
PP$CNS_4 <- as.numeric(as.character(PP$CNS_32))
PP$CNS_5 <- as.numeric(as.character(PP$CNS_33))
#Descriptives
describe(PP$CNS_1)## PP$CNS_1
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 96 0.997 66.57 28.06 22.0 31.0
## .25 .50 .75 .90 .95
## 52.0 69.5 86.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$CNS_1, na.rm=TRUE)## [1] 0 100describe(PP$CNS_2)## PP$CNS_2
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 94 0.997 70.1 25.63 27.05 38.00
## .25 .50 .75 .90 .95
## 55.00 72.50 87.00 100.00 100.00
##
## lowest : 0 1 2 3 5, highest: 96 97 98 99 100range(PP$CNS_2, na.rm=TRUE)## [1] 0 100describe(PP$CNS_3)## PP$CNS_3
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 98 0.995 69.48 27.62 21 36
## .25 .50 .75 .90 .95
## 53 73 90 100 100
##
## lowest : 0 1 3 5 6, highest: 96 97 98 99 100range(PP$CNS_3, na.rm=TRUE)## [1] 0 100describe(PP$CNS_4)## PP$CNS_4
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 100 0.999 58.97 32.46 0 15
## .25 .50 .75 .90 .95
## 40 63 80 99 100
##
## lowest : 0 1 2 3 4, highest: 95 96 97 99 100range(PP$CNS_4, na.rm=TRUE)## [1] 0 100describe(PP$CNS_5)## PP$CNS_5
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 101 0.999 59.54 31.64 2.05 19.10
## .25 .50 .75 .90 .95
## 41.00 63.00 81.00 98.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$CNS_5, na.rm=TRUE)## [1] 0 100#Histograms
hist(PP$CNS_1, main = 'I often feel a sense of oneness with the natural world around me.')
hist(PP$CNS_2, main = 'I think of the natural world as a community to which I belong.')
hist(PP$CNS_3, main = 'I feel that all inhabitants of Earth, human, and nonhuman, share a common ‘life force’.')
hist(PP$CNS_4, main = 'My personal welfare is independent of the welfare of the natural world.')
hist(PP$CNS_5, main = 'When I think of my place on Earth, I consider myself to be a top member of a hierarchy that exists in nature.')
#Recode items 4 and 5
PP$CNS_4R <- (100 - PP$CNS_4)
PP$CNS_5R <- (100 - PP$CNS_5)
PP$CNS_Scale2 <- data.frame(PP$CNS_1, PP$CNS_2, PP$CNS_3, PP$CNS_4R, PP$CNS_5R)
psych::alpha(PP$CNS_Scale2)## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(PP$CNS_Scale2): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.CNS_4R PP.CNS_5R ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = PP$CNS_Scale2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.16 0.22 0.44 0.053 0.28 0.044 58 12 -0.2
##
## lower alpha upper 95% confidence boundaries
## 0.07 0.16 0.25
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CNS_1 -0.0065 0.004 0.27 0.001 0.004 0.054 0.14 -0.20
## PP.CNS_2 -0.0767 -0.070 0.21 -0.017 -0.065 0.058 0.14 -0.22
## PP.CNS_3 -0.1048 -0.094 0.21 -0.022 -0.086 0.060 0.15 -0.22
## PP.CNS_4R 0.3084 0.374 0.52 0.130 0.597 0.036 0.22 0.14
## PP.CNS_5R 0.3919 0.453 0.55 0.171 0.827 0.032 0.18 0.17
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CNS_1 1002 0.57 0.62 0.561 0.194 67 25
## PP.CNS_2 1002 0.60 0.66 0.645 0.272 70 23
## PP.CNS_3 1002 0.62 0.67 0.652 0.276 69 25
## PP.CNS_4R 1001 0.38 0.30 -0.052 -0.083 41 29
## PP.CNS_5R 1002 0.28 0.20 -0.171 -0.174 40 28#Drop reverse coded items
PP$CNS_Scale <- data.frame(PP$CNS_1, PP$CNS_2, PP$CNS_3)
psych::alpha(PP$CNS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$CNS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.71 0.55 3.7 0.012 69 20 0.55
##
## lower alpha upper 95% confidence boundaries
## 0.77 0.79 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CNS_1 0.73 0.73 0.58 0.58 2.8 0.017 NA 0.58
## PP.CNS_2 0.70 0.70 0.54 0.54 2.3 0.019 NA 0.54
## PP.CNS_3 0.71 0.71 0.55 0.55 2.4 0.018 NA 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CNS_1 1002 0.83 0.83 0.69 0.61 67 25
## PP.CNS_2 1002 0.84 0.85 0.73 0.64 70 23
## PP.CNS_3 1002 0.84 0.84 0.71 0.63 69 25PP$CNS_Score <- rowMeans(PP [, c("CNS_1", "CNS_2", "CNS_3", "CNS_4R", "CNS_5R")], na.rm=TRUE)
#Correlation CCB
cor(PP$CNS_Scale, use= "complete.obs")## PP.CNS_1 PP.CNS_2 PP.CNS_3
## PP.CNS_1 1.0000000 0.5501475 0.5350697
## PP.CNS_2 0.5501475 1.0000000 0.5794158
## PP.CNS_3 0.5350697 0.5794158 1.0000000Control
# Control was measured with 1 item on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree').
## Item #1: We have control over the processes in this method.#GFFB
PP$Control_GFFB <- PP$GFFB_Risk_34
length(PP$Control_GFFB)## [1] 1005describe(PP$Control_GFFB)## PP$Control_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 496 509 95 0.997 65.44 30.6 3.75 25.00
## .25 .50 .75 .90 .95
## 51.75 69.00 86.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Control_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Control_GFFB, main = 'GFFB - We have control over the processes in this method.')
#GFPRB
PP$Control_GFPRB <- PP$GFPRB_Risk_34
length(PP$Control_GFPRB)## [1] 1005describe(PP$Control_GFPRB)## PP$Control_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 512 493 90 0.996 67.08 30.19 13 26
## .25 .50 .75 .90 .95
## 51 72 88 100 100
##
## lowest : 0 1 8 9 11, highest: 96 97 98 99 100range(PP$Control_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Control_GFPRB, main = 'GFPRB - We have control over the processes in this method.')
#CBB
PP$Control_CBB <- PP$CBB_Risk_34
length(PP$Control_CBB)## [1] 1005describe(PP$Control_CBB)## PP$Control_CBB
## n missing distinct Info Mean Gmd .05 .10
## 516 489 94 0.998 61.67 32.83 0.75 18.50
## .25 .50 .75 .90 .95
## 43.00 67.00 85.00 100.00 100.00
##
## lowest : 0 1 3 4 5, highest: 95 96 98 99 100range(PP$Control_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Control_CBB, main = 'CBB - We have control over the processes in this method.')
#PBPB
PP$Control_PBPB <- PP$PBPB_Risk_34
length(PP$Control_PBPB)## [1] 1005describe(PP$Control_PBPB)## PP$Control_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 523 482 91 0.997 65.41 29.39 15.1 28.0
## .25 .50 .75 .90 .95
## 52.0 69.0 85.0 100.0 100.0
##
## lowest : 0 2 3 6 10, highest: 96 97 98 99 100range(PP$Control_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Control_PBPB, main = 'PBPB - We have control over the processes in this method.')
#PBFB
PP$Control_PBFB <- PP$PBFB_Risk_34
length(PP$Control_PBFB)## [1] 1005describe(PP$Control_PBFB)## PP$Control_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 481 524 93 0.998 63.87 31.74 4 20
## .25 .50 .75 .90 .95
## 49 70 85 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Control_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Control_PBFB, main = 'PBFB - We have control over the processes in this method.')
#VB
PP$Control_VB <- PP$VB_Risk_34
length(PP$Control_VB)## [1] 1005describe(PP$Control_VB)## PP$Control_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 93 0.996 65.91 30.96 13 25
## .25 .50 .75 .90 .95
## 51 70 89 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Control_VB, na.rm=TRUE)## [1] 0 100hist(PP$Control_VB, main = 'VB - We have control over the processes in this method.')
Disgust
# Disgust was measured with 1 item on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree').
## Item #1: This is disgusting.#GFFB
PP$Disgust_GFFB <- PP$GFFB_Risk_37
length(PP$Disgust_GFFB)## [1] 1005describe(PP$Disgust_GFFB)## PP$Disgust_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 496 509 98 0.997 49.5 38.87 0 0
## .25 .50 .75 .90 .95
## 20 51 79 100 100
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100range(PP$Disgust_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_GFFB, main = 'GFFB - This is disgusting.')
#GFPRB
PP$Disgust_GFPRB <- PP$GFPRB_Risk_37
length(PP$Disgust_GFPRB)## [1] 1005describe(PP$Disgust_GFPRB)## PP$Disgust_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 512 493 97 0.988 35.25 37.16 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 27.00 62.25 87.90 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100range(PP$Disgust_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_GFPRB, main = 'GFPRB - This is disgusting.')
#CBB
PP$Disgust_CBB <- PP$CBB_Risk_37
length(PP$Disgust_CBB)## [1] 1005describe(PP$Disgust_CBB)## PP$Disgust_CBB
## n missing distinct Info Mean Gmd .05 .10
## 512 493 97 0.996 54.62 39.24 0.00 1.00
## .25 .50 .75 .90 .95
## 23.75 58.50 85.25 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Disgust_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_CBB, main = 'CBB - This is disgusting.')
#PBPB
PP$Disgust_PBPB <- PP$PBPB_Risk_37
length(PP$Disgust_PBPB)## [1] 1005describe(PP$Disgust_PBPB)## PP$Disgust_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 525 480 98 0.998 48.87 38.39 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 50.0 77.0 99.6 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Disgust_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_PBPB, main = 'PBPB - This is disgusting.')
#PBFB
PP$Disgust_PBFB <- PP$PBFB_Risk_37
length(PP$Disgust_PBFB)## [1] 1005describe(PP$Disgust_PBFB)## PP$Disgust_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 481 524 98 0.997 53.33 39.31 0 3
## .25 .50 .75 .90 .95
## 22 56 83 100 100
##
## lowest : 0 1 2 3 5, highest: 96 97 98 99 100range(PP$Disgust_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_PBFB, main = 'PBFB - This is disgusting.')
#VB
PP$Disgust_VB <- PP$VB_Risk_37
length(PP$Disgust_VB)## [1] 1005describe(PP$Disgust_VB)## PP$Disgust_VB
## n missing distinct Info Mean Gmd .05 .10
## 470 535 98 0.997 44.34 37.49 0.00 0.00
## .25 .50 .75 .90 .95
## 16.00 43.50 72.75 92.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100range(PP$Disgust_VB, na.rm=TRUE)## [1] 0 100hist(PP$Disgust_VB, main = 'VB - This is disgusting.')
Disgust Sensitivity Scale
#DS-R Disgust Scale (Olantunji et al., 2007)
##Assesses three disgust domains (core, animal reminder, contamination) on a 0-100 scale.
##Item 1: If I see someone vomit, it makes me sick to my stomach.
##Item 2: It would not upset me at all to watch a person with a glass eye take the eye out of the socket. (reverse coded)
##Item 3: I never let any part of my body touch the toilet seat in a public washroom.
#Define Variables
PP$DS_1D <- as.numeric(as.character(PP$DS_1))
PP$DS_2D <- as.numeric(as.character(PP$DS_8))
PP$DS_2R <- (100- PP$DS_2D)
PP$DS_3D <- as.numeric(as.character(PP$DS_2))
PP$DS_Score <- rowMeans(PP[, c('DS_1D', 'DS_2R', 'DS_3D')], na.rm=T)
#Descriptives
describe(PP$DS_1)## PP$DS_1
## n missing distinct Info Mean Gmd .05 .10
## 1000 5 99 0.994 65.68 33.9 2 18
## .25 .50 .75 .90 .95
## 44 72 92 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$DS_1, na.rm=TRUE)## [1] 0 100describe(PP$DS_2)## PP$DS_2
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 100 0.994 58.14 38.51 0 3
## .25 .50 .75 .90 .95
## 30 63 89 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$DS_2, na.rm=TRUE)## [1] 0 100describe(PP$DS_3)## PP$DS_3
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 100 0.994 58.14 38.51 0 3
## .25 .50 .75 .90 .95
## 30 63 89 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$DS_3, na.rm=TRUE)## [1] 0 100#Histograms
hist(PP$DS_1, main = 'If I see someone vomit, it makes me sick to my stomach.')
hist(PP$DS_2, main = 'It would not upset me at all to watch a person with a glass eye take the eye out of the socket.')
hist(PP$DS_3, main = 'I never let any part of my body touch the toilet seat in a public washroom.')
PP$DS_Scale <- data.frame(PP$DS_1D, PP$DS_2R,PP$DS_3D)
psych::alpha(PP$DS_Scale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$DS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.27 0.28 0.24 0.12 0.39 0.04 58 21 0.12
##
## lower alpha upper 95% confidence boundaries
## 0.19 0.27 0.35
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.DS_1D -0.10 -0.10 -0.05 -0.05 -0.094 0.070 NA -0.05
## PP.DS_2R 0.43 0.43 0.27 0.27 0.750 0.036 NA 0.27
## PP.DS_3D 0.22 0.22 0.12 0.12 0.281 0.049 NA 0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.DS_1D 1000 0.70 0.73 0.52 0.29 66 30
## PP.DS_2R 1000 0.58 0.56 0.10 0.04 49 34
## PP.DS_3D 1001 0.65 0.64 0.32 0.14 58 34PP$DS_Score <- rowMeans(PP [, c("DS_1D", "DS_2R", "DS_3D")], na.rm=TRUE)
#Correlation CCB
cor(PP$DS_Scale, use= "complete.obs")## PP.DS_1D PP.DS_2R PP.DS_3D
## PP.DS_1D 1.0000000 0.1230523 0.2723974
## PP.DS_2R 0.1230523 1.0000000 -0.0495374
## PP.DS_3D 0.2723974 -0.0495374 1.0000000Familiarity
# Familiarity was measured with 1 item on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree').
## Item #1: This is familiar.Grain-fed Feedlot Burger (GFFB)
#GFFB
PP$Familiarity_GFFB <-PP$GFFB_Risk_31
length(PP$Familiarity_GFFB)## [1] 1005describe(PP$Familiarity_GFFB)## PP$Familiarity_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 493 512 97 0.997 62.79 34.17 3 17
## .25 .50 .75 .90 .95
## 41 68 89 100 100
##
## lowest : 0 2 3 4 5, highest: 96 97 98 99 100sd(PP$Familiarity_GFFB, na.rm = TRUE)## [1] 30.18567range(PP$Familiarity_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_GFFB, main = 'GFFB - This is familiar.')
Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$Familiarity_GFPRB <-PP$GFPRB_Risk_31
length(PP$Familiarity_GFPRB)## [1] 1005describe(PP$Familiarity_GFPRB)## PP$Familiarity_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 511 494 84 0.988 73.29 27.98 18.5 37.0
## .25 .50 .75 .90 .95
## 59.5 78.0 97.0 100.0 100.0
##
## lowest : 0 1 4 5 8, highest: 96 97 98 99 100sd(PP$Familiarity_GFPRB, na.rm = TRUE)## [1] 25.71012range(PP$Familiarity_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_GFPRB, main = 'GFPRB - This is familiar.')
Cultured beef burgers (CBB)
#CBB
PP$Familiarity_CBB <-PP$CBB_Risk_31
length(PP$Familiarity_CBB)## [1] 1005describe(PP$Familiarity_CBB)## PP$Familiarity_CBB
## n missing distinct Info Mean Gmd .05 .10
## 515 490 99 0.997 46.27 38.6 0.0 0.0
## .25 .50 .75 .90 .95
## 15.0 50.0 74.5 95.6 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Familiarity_CBB, na.rm = TRUE)## [1] 33.51951range(PP$Familiarity_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_CBB, main = 'CBB - This is familiar.')
Plant-based Protein Burger (PBPB)
#PBPB
PP$Familiarity_PBPB <-PP$PBPB_Risk_31
length(PP$Familiarity_PBPB)## [1] 1005describe(PP$Familiarity_PBPB)## PP$Familiarity_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 98 0.999 54.46 34.8 0 7
## .25 .50 .75 .90 .95
## 30 57 79 95 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Familiarity_PBPB, na.rm = TRUE)## [1] 30.2881range(PP$Familiarity_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_PBPB, main = 'PBPB - This is familiar.')
Plant-based Fermentation Burger (PBFB)
#PBFB
PP$Familiarity_PBFB <-PP$PBFB_Risk_31
length(PP$Familiarity_PBFB)## [1] 1005describe(PP$Familiarity_PBFB)## PP$Familiarity_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 481 524 99 0.998 48.06 37.95 0 0
## .25 .50 .75 .90 .95
## 18 51 76 93 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Familiarity_PBFB, na.rm = TRUE)## [1] 32.91287range(PP$Familiarity_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_PBFB, main = 'PBFB - This is familiar.')
Veggie Burger (VB)
#VB
PP$Familiarity_VB <-PP$VB_Risk_31
length(PP$Familiarity_VB)## [1] 1005describe(PP$Familiarity_VB)## PP$Familiarity_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 98 0.999 62.01 32.89 1.55 16.00
## .25 .50 .75 .90 .95
## 45.00 67.50 85.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Familiarity_VB, na.rm = TRUE)## [1] 29.15807range(PP$Familiarity_VB, na.rm=TRUE)## [1] 0 100hist(PP$Familiarity_VB, main = 'VB - This is familiar.')
Ideology
#Political Orientation
##Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
PP$Orientation = as.numeric(recode_factor(PP$PI_Orientation,'1'= "3",'2'= "2",'3'= "1",
'4'= "0",'5'= "-1", '6'= "-2", '7'= "-3"))
describe(PP$Orientation)## PP$Orientation
## n missing distinct Info Mean Gmd
## 1002 3 7 0.962 3.718 2.003
##
## lowest : 1 2 3 4 5, highest: 3 4 5 6 7
##
## Value 1 2 3 4 5 6 7
## Frequency 126 171 124 301 93 93 94
## Proportion 0.126 0.171 0.124 0.300 0.093 0.093 0.094hist(PP$Orientation , main = 'Political Orientation (Liberal to Conservative)')
#Political Party Identification
##Generally speaking, do you usually think of yourself as a Republican, a Democrat, an Independent, or what? (1 = Republican, 2 = Democrat, 3 = Independent, 4 = Other (write-in), 5 = No Preference)
describe(PP$Party)##
## NULLPP$Party <- PP$Party
PP$DemStrength <- PP$DStrength
PP$RepStrength <- PP$RStrength
PP$PartyClose <- PP$Closerto
# Recode Party
PP$PartyFull <- NA
PP$PartyFull[PP$DemStrength == 1] <- -3
PP$PartyFull[PP$DemStrength == 2] <- -2
PP$PartyFull[PP$PartyClose == 1] <- -1
PP$PartyFull[PP$PartyClose == 3] <- 0
PP$PartyFull[PP$PartyClose == 2] <- 1
PP$PartyFull[PP$RepStrength == 2] <- 2
PP$PartyFull[PP$RepStrength == 1] <- 3
describe(PP$PartyFull)## PP$PartyFull
## n missing distinct Info Mean Gmd
## 996 9 7 0.967 -0.1797 2.495
##
## lowest : -3 -2 -1 0 1, highest: -1 0 1 2 3
##
## Value -3 -2 -1 0 1 2 3
## Frequency 227 136 66 212 65 95 195
## Proportion 0.228 0.137 0.066 0.213 0.065 0.095 0.196hist(PP$PartyFull , main = 'Party Identification')
PP$PartyID <- NA
PP$PartyID[PP$PartyFull < 0] <- -0.5
PP$PartyID[PP$PartyFull == 0] <- 0
PP$PartyID[PP$PartyFull > 0] <- 0.5
#New Variable: Ideology
PP$Ideology <- rowMeans(PP[, c('PartyFull', 'Orientation')], na.rm=T)
describe(PP$Ideology)## PP$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 1003 2 14 0.949 1.785 1.155 -0.5 0.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.5 3.0 3.5
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 3.5 4.0 4.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
## Frequency 25 27 35 80 79 142 357 126 52 49 14
## Proportion 0.025 0.027 0.035 0.080 0.079 0.142 0.356 0.126 0.052 0.049 0.014
##
## Value 4.5 5.0 6.0
## Frequency 6 9 2
## Proportion 0.006 0.009 0.002hist(PP$Ideology)
Individualism/Collectivism
#Individualism and Collectivism Scale (Code adapted from J.Cole Collectivism Study)
#Individualism and collectivism were each measured with 4 items (for a total of 8 items) on a 1-7 scale of agreement (0 = 'Strongly disagree' to 100 = 'Strongly agree').
##Collectivism Items
### Item #3 (C): It is important to me to think of myself as a member of my religious, national, or ethnic group.
### Item #4 (C): Learning about the traditions, values, and beliefs of my family is important to me.
### Item #7 (C): In the end, a person feels closest to members of their own religious, national, or ethnic group.
### Item #8 (C): It is important to me to respect decisions made by my family.
##Individualism Items
### Item #1 (I): It is important to me to develop my own personal style.
### Item #2 (I): It is better for me to follow my own ideas than to follow those of anyone else.
### Item #5 (I): I enjoy being unique and different from others in many respects.
###I Item #6 (I): My personal achievements and accomplishments are very important to who I am.
#Individualism (Items 1,2,5,6)
PP$Ind_1 <- as.numeric(as.character(PP$Individualism_19))
PP$Ind_2 <- as.numeric(as.character(PP$Individualism_20))
PP$Ind_5 <- as.numeric(as.character(PP$Individualism_23))
PP$Ind_6 <- as.numeric(as.character(PP$Individualism_24))
PP$Individualism_Score <- rowMeans(PP[, c('Ind_1', 'Ind_2', 'Ind_5','Ind_6')], na.rm=T)
#Collectivism (Items 3,4,7,8)
PP$Ind_3 <- as.numeric(as.character(PP$Individualism_21))
PP$Ind_4 <- as.numeric(as.character(PP$Individualism_22))
PP$Ind_7 <- as.numeric(as.character(PP$Individualism_25))
PP$Ind_8 <- as.numeric(as.character(PP$Individualism_34))
PP$Collectivism_Score <- rowMeans(PP[, c('Ind_3', 'Ind_4', 'Ind_7','Ind_8')], na.rm=T)
#Individualism Alpha and Histogram (4 items)
psych::alpha(data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5,PP$Ind_6))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5, PP$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.75 0.5 3.9 0.01 74 18 0.49
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_1 0.72 0.72 0.63 0.46 2.5 0.015 0.00254 0.43
## PP.Ind_2 0.74 0.74 0.67 0.49 2.9 0.014 0.00574 0.47
## PP.Ind_5 0.74 0.74 0.66 0.49 2.8 0.014 0.00397 0.47
## PP.Ind_6 0.79 0.79 0.71 0.55 3.7 0.012 0.00089 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_1 1000 0.82 0.82 0.75 0.67 75 23
## PP.Ind_2 1000 0.79 0.79 0.69 0.62 74 23
## PP.Ind_5 1000 0.80 0.80 0.70 0.63 74 23
## PP.Ind_6 1000 0.74 0.74 0.59 0.53 72 23hist(PP$Individualism_Score , main = 'Individualism Score')
#Collectivism Alpha and Histogram (4 items)
psych::alpha(data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7, PP$Ind_8))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7, PP$Ind_8))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.73 0.45 3.3 0.012 67 20 0.42
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_3 0.69 0.69 0.61 0.43 2.2 0.017 0.0090 0.38
## PP.Ind_4 0.71 0.70 0.63 0.44 2.4 0.016 0.0138 0.38
## PP.Ind_7 0.70 0.72 0.64 0.46 2.5 0.016 0.0059 0.45
## PP.Ind_8 0.73 0.73 0.65 0.47 2.7 0.014 0.0102 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_3 998 0.82 0.79 0.69 0.61 60 31
## PP.Ind_4 1000 0.75 0.77 0.66 0.57 72 24
## PP.Ind_7 1000 0.77 0.76 0.64 0.57 64 27
## PP.Ind_8 1001 0.72 0.75 0.62 0.52 70 24hist(PP$Collectivism_Score , main = 'Collectivism Score')
#Cronbachs Alpha for Individualism and Collectivism scales
PP$IndScale <- data.frame(PP$Ind_1, PP$Ind_2, PP$Ind_5,PP$Ind_6)
psych::alpha(PP$IndScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$IndScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.75 0.5 3.9 0.01 74 18 0.49
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_1 0.72 0.72 0.63 0.46 2.5 0.015 0.00254 0.43
## PP.Ind_2 0.74 0.74 0.67 0.49 2.9 0.014 0.00574 0.47
## PP.Ind_5 0.74 0.74 0.66 0.49 2.8 0.014 0.00397 0.47
## PP.Ind_6 0.79 0.79 0.71 0.55 3.7 0.012 0.00089 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_1 1000 0.82 0.82 0.75 0.67 75 23
## PP.Ind_2 1000 0.79 0.79 0.69 0.62 74 23
## PP.Ind_5 1000 0.80 0.80 0.70 0.63 74 23
## PP.Ind_6 1000 0.74 0.74 0.59 0.53 72 23PP$CollScale <- data.frame(PP$Ind_3, PP$Ind_4, PP$Ind_7, PP$Ind_8)
psych::alpha(PP$CollScale)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.73 0.45 3.3 0.012 67 20 0.42
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_3 0.69 0.69 0.61 0.43 2.2 0.017 0.0090 0.38
## PP.Ind_4 0.71 0.70 0.63 0.44 2.4 0.016 0.0138 0.38
## PP.Ind_7 0.70 0.72 0.64 0.46 2.5 0.016 0.0059 0.45
## PP.Ind_8 0.73 0.73 0.65 0.47 2.7 0.014 0.0102 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_3 998 0.82 0.79 0.69 0.61 60 31
## PP.Ind_4 1000 0.75 0.77 0.66 0.57 72 24
## PP.Ind_7 1000 0.77 0.76 0.64 0.57 64 27
## PP.Ind_8 1001 0.72 0.75 0.62 0.52 70 24Meat Consumption
# Beef Consumption Frequency measured with the question, "In the average week, how often do you eat beef?" (1 = Never, 2 = Less than once a week, 3 = 1-2 times a week, 4 = 3-4 times a week, 5 = 5+ times a week)
describe(PP$Beef_Frequency)## PP$Beef_Frequency
## n missing distinct Info Mean Gmd
## 1005 0 5 0.902 3.046 1.091
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 60 219 430 207 89
## Proportion 0.060 0.218 0.428 0.206 0.089histogram(PP$Beef_Frequency)
# Non-Beef Consumption Frequency measured with the question, "In the average week, how often do you eat meat, not including beef? (i.e. poultry, fish, etc.)" (1 = Never, 2 = Less than once a week, 3 = 1-2 times a week, 4 = 3-4 times a week, 5 = 5+ times a week)
describe(PP$NonBeef_Frequency)## PP$NonBeef_Frequency
## n missing distinct Info Mean Gmd
## 1005 0 5 0.924 3.369 1.161
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 43 156 351 297 158
## Proportion 0.043 0.155 0.349 0.296 0.157histogram(PP$NonBeef_Frequency)
Naturalness
# Naturalness perception was measured with 4 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Naturalness score calculated by averaging these items.
### Item 1: This is natural.
### Item 2: This involves humans altering naturally occurring processes.
### Item 3: This relies on science-based technology.
### Item 4: This is artificial.Grain-fed Feedlot Burger (GFFB)
# Defines variables in the naturalness scale and reverse codes items 2, 3, and 4.
PP$Nat_1_GFFB <- PP$GFFB_Naturalness_30
PP$Nat_2R_GFFB <- (100-PP$GFFB_Naturalness_31)
PP$Nat_3R_GFFB <- (100-PP$GFFB_Naturalness_35)
PP$Nat_4R_GFFB <- (100-PP$GFFB_Naturalness_36)
# Histograms
hist(PP$Nat_1_GFFB)
hist(PP$Nat_2R_GFFB)
hist(PP$Nat_3R_GFFB)
hist(PP$Nat_4R_GFFB)
# Scales and Scores
PP$Naturalness_Score_GFFB_Tot <- rowMeans(PP [, c( "Nat_1_GFFB" , "Nat_2R_GFFB", "Nat_3R_GFFB", "Nat_4R_GFFB")], na.rm=TRUE)
describe(PP$Naturalness_Score_GFFB_Tot)## PP$Naturalness_Score_GFFB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 499 506 219 1 49.53 23.65 21.38 25.20
## .25 .50 .75 .90 .95
## 34.75 48.00 62.12 79.30 93.35
##
## lowest : 0.00 0.25 1.00 6.25 7.00, highest: 98.25 98.50 99.25 99.50 100.00sd(PP$Naturalness_Score_GFFB_Tot, na.rm = TRUE)## [1] 21.26861PP$Naturalness_Scale_GFFB_Tot <- data.frame(PP$Nat_1_GFFB , PP$Nat_4R_GFFB, PP$Nat_2R_GFFB , PP$Nat_3R_GFFB)
describe(PP$Naturalness_Scale_GFFB_Tot)## PP$Naturalness_Scale_GFFB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 94 0.998 58.65 34.6 0 13
## .25 .50 .75 .90 .95
## 35 61 84 100 100
##
## lowest : 0 1 4 5 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 495 510 100 0.998 50.37 36.65 0 6
## .25 .50 .75 .90 .95
## 26 48 79 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 496 509 96 0.998 42.95 35 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 39.0 66.0 92.5 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 498 507 97 0.999 46.71 34.94 0.00 6.00
## .25 .50 .75 .90 .95
## 23.00 44.50 68.75 97.30 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------Grain-fed Pasture Raised Burger (GFPRB)
# Defines Naturalness variables and reverse coding items 2, 3, and 4.
PP$Nat_1_GFPRB <- PP$GFPRB_Naturalness_30
PP$Nat_2R_GFPRB <- (100-PP$GFPRB_Naturalness_31)
PP$Nat_3R_GFPRB <- (100-PP$GFPRB_Naturalness_35)
PP$Nat_4R_GFPRB <- (100-PP$GFPRB_Naturalness_36)
# Histograms
hist(PP$Nat_1_GFPRB)
hist(PP$Nat_2R_GFPRB)
hist(PP$Nat_3R_GFPRB)
hist(PP$Nat_4R_GFPRB)
#### Score and Scale
PP$Naturalness_Score_GFPRB_Tot <- rowMeans(PP [, c( "Nat_1_GFPRB" , "Nat_4R_GFPRB", "Nat_2R_GFPRB" , "Nat_3R_GFPRB")], na.rm=TRUE)
describe(PP$Naturalness_Score_GFPRB_Tot)## PP$Naturalness_Score_GFPRB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 514 491 240 0.999 62.49 27.37 25.00 32.08
## .25 .50 .75 .90 .95
## 44.81 59.12 81.44 98.75 100.00
##
## lowest : 0.00 1.75 7.00 10.75 11.75, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Naturalness_Score_GFPRB_Tot, na.rm = TRUE)## [1] 23.85977PP$Naturalness_Scale_GFPRB_Tot <- data.frame(PP$Nat_1_GFPRB , PP$Nat_4R_GFPRB, PP$Nat_2R_GFPRB , PP$Nat_3R_GFPRB)Cultured beef burgers (CBB)
#Defines naturalness variables and reverse codes items 2, 3, and 4.
PP$Nat_1_CBB <- PP$CBB_Naturalness_30
PP$Nat_2R_CBB <- (100-PP$CBB_Naturalness_31)
PP$Nat_3R_CBB <- (100-PP$CBB_Naturalness_35)
PP$Nat_4R_CBB <- (100-PP$CBB_Naturalness_36)
# Histogram
hist(PP$Nat_1_CBB)
hist(PP$Nat_2R_CBB)
hist(PP$Nat_3R_CBB)
hist(PP$Nat_4R_CBB)
#### Score and Scale
PP$Naturalness_Score_CBB_Tot <- rowMeans(PP [, c( "Nat_1_CBB" , "Nat_4R_CBB", "Nat_2R_CBB" , "Nat_3R_CBB")], na.rm=TRUE)
describe(PP$Naturalness_Score_CBB_Tot)## PP$Naturalness_Score_CBB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 516 489 224 0.999 34.32 24.5 0.00 0.75
## .25 .50 .75 .90 .95
## 17.94 35.38 49.00 59.00 67.12
##
## lowest : 0.00 0.25 0.50 1.00 1.25, highest: 96.25 98.50 99.50 99.75 100.00sd(PP$Naturalness_Score_CBB_Tot, na.rm = TRUE)## [1] 21.71332PP$Naturalness_Scale_CBB_Tot <- data.frame(PP$Nat_1_CBB , PP$Nat_4R_CBB, PP$Nat_2R_CBB , PP$Nat_3R_CBB)
describe(PP$Naturalness_Scale_CBB_Tot)## PP$Naturalness_Scale_CBB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_CBB
## n missing distinct Info Mean Gmd .05 .10
## 515 490 96 0.996 45.56 39.24 0 0
## .25 .50 .75 .90 .95
## 13 47 75 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 93 0.991 32.77 33.56 0.00 0.00
## .25 .50 .75 .90 .95
## 5.25 25.00 49.00 81.70 98.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 88 0.987 29.75 30.74 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 25.00 47.00 73.00 85.35
##
## lowest : 0 1 2 3 4, highest: 91 94 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 89 0.987 29.07 30.45 0.00 0.00
## .25 .50 .75 .90 .95
## 2.00 24.00 47.00 69.70 84.35
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100
## --------------------------------------------------------------------------------Plant-based Protein Burger (PBPB)
#Defines naturalness variables and reverse codes items 2, 3 and 4.
PP$Nat_1_PBPB <- PP$PBPB_Naturalness_30
PP$Nat_2R_PBPB <- (100-PP$PBPB_Naturalness_31)
PP$Nat_3R_PBPB <- (100-PP$PBPB_Naturalness_35)
PP$Nat_4R_PBPB <- (100-PP$PBPB_Naturalness_36)
#Histograms
hist(PP$Nat_1_PBPB)
hist(PP$Nat_2R_PBPB)
hist(PP$Nat_3R_PBPB)
hist(PP$Nat_4R_PBPB)
#### Score and Scale
PP$Naturalness_Score_PBPB_Tot <- rowMeans(PP [, c( "Nat_1_PBPB" , "Nat_4R_PBPB", "Nat_2R_PBPB" , "Nat_3R_PBPB")], na.rm=TRUE)
describe(PP$Naturalness_Score_PBPB_Tot)## PP$Naturalness_Score_PBPB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 524 481 236 1 42.37 22.52 2.288 12.900
## .25 .50 .75 .90 .95
## 29.688 44.000 53.750 67.100 74.962
##
## lowest : 0.00 0.50 0.75 1.00 1.25, highest: 92.25 96.75 97.00 98.50 100.00sd(PP$Naturalness_Score_PBPB_Tot, na.rm = TRUE)## [1] 20.16823PP$Naturalness_Scale_PBPB_Tot <- data.frame(PP$Nat_1_PBPB , PP$Nat_4R_PBPB, PP$Nat_2R_PBPB , PP$Nat_3R_PBPB)
describe(PP$Naturalness_Scale_PBPB_Tot)## PP$Naturalness_Scale_PBPB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 99 0.998 53.99 36.26 0 3
## .25 .50 .75 .90 .95
## 29 58 79 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 97 0.998 43.33 34.89 0 0
## .25 .50 .75 .90 .95
## 20 39 68 87 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 96 0.998 39.82 33.02 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 35.0 61.0 83.9 97.0
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 88 0.996 32.13 28.98 0.0 0.0
## .25 .50 .75 .90 .95
## 11.0 29.0 48.0 70.9 82.0
##
## lowest : 0 1 2 3 4, highest: 94 97 98 99 100
## --------------------------------------------------------------------------------Plant-based Fermentation Burger (PBFB)
PP$Nat_1_PBFB <- PP$PBFB_Naturalness_30
PP$Nat_2R_PBFB <- (100-PP$PBFB_Naturalness_31)
PP$Nat_3R_PBFB <- (100-PP$PBFB_Naturalness_35)
PP$Nat_4R_PBFB <- (100-PP$PBFB_Naturalness_36)
#Define Variables
PP$Nat_1_PBFB <- PP$PBFB_Naturalness_30
PP$Nat_2R_PBFB <- PP$PBFB_Naturalness_31
PP$Nat_3R_PBFB <- PP$PBFB_Naturalness_35
PP$Nat_4R_PBFB <- PP$PBFB_Naturalness_36
# Histograms
hist(PP$Nat_1_PBFB)
hist(PP$Nat_2R_PBFB)
hist(PP$Nat_3R_PBFB)
hist(PP$Nat_4R_PBFB)
#### Scale and Score
PP$Naturalness_Score_PBFB_Tot <- rowMeans(PP [, c( "Nat_1_PBFB" , "Nat_4R_PBFB", "Nat_2R_PBFB" , "Nat_3R_PBFB")], na.rm=TRUE)
describe(PP$Naturalness_Score_PBFB_Tot)## PP$Naturalness_Score_PBFB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 481 524 217 1 62.27 19.97 29.00 41.50
## .25 .50 .75 .90 .95
## 52.00 63.25 75.00 83.25 93.75
##
## lowest : 0.00 0.25 2.75 4.25 7.50, highest: 97.50 98.50 99.00 99.75 100.00sd(PP$Naturalness_Score_PBFB_Tot, na.rm = TRUE)## [1] 18.3293PP$Naturalness_Scale_PBFB_Tot <- data.frame(PP$Nat_1_PBFB , PP$Nat_4R_PBFB, PP$Nat_2R_PBFB , PP$Nat_3R_PBFB)
describe(PP$Naturalness_Scale_PBFB_Tot)## PP$Naturalness_Scale_PBFB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 481 524 97 0.998 51.85 38.41 0 0
## .25 .50 .75 .90 .95
## 23 53 81 98 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 480 525 95 0.996 61.33 35.17 0 14
## .25 .50 .75 .90 .95
## 37 66 88 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 480 525 95 0.996 65.55 32.51 6.95 24.00
## .25 .50 .75 .90 .95
## 46.50 71.50 90.00 100.00 100.00
##
## lowest : 0 1 2 3 6, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 478 527 86 0.994 70.53 29.62 8.40 29.00
## .25 .50 .75 .90 .95
## 54.25 75.50 93.75 100.00 100.00
##
## lowest : 0 1 3 4 5, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------Veggie Burger (VB)
#Define variables
PP$Nat_1_VB <- PP$VB_Naturalness_30
PP$Nat_2R_VB <- (100-PP$VB_Naturalness_31)
PP$Nat_3R_VB <- (100-PP$VB_Naturalness_35)
PP$Nat_4R_VB <- (100-PP$VB_Naturalness_36)
# Histograms
hist(PP$Nat_1_VB)
hist(PP$Nat_2R_VB)
hist(PP$Nat_3R_VB)
hist(PP$Nat_4R_VB)
#### Scale and Score
PP$Naturalness_Score_VB_Tot <- rowMeans(PP [, c( "Nat_1_VB" , "Nat_4R_VB", "Nat_2R_VB" , "Nat_3R_VB")], na.rm=TRUE)
describe(PP$Naturalness_Score_VB_Tot)## PP$Naturalness_Score_VB_Tot
## n missing distinct Info Mean Gmd .05 .10
## 472 533 237 1 51.39 25.22 16.50 25.00
## .25 .50 .75 .90 .95
## 36.19 49.00 65.50 84.00 96.36
##
## lowest : 0.00 1.25 3.00 3.25 4.25, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Naturalness_Score_VB_Tot, na.rm = TRUE)## [1] 22.39438PP$Naturalness_Scale_VB_Tot <- data.frame(PP$Nat_1_VB , PP$Nat_4R_VB, PP$Nat_2R_VB , PP$Nat_3R_VB )
describe(PP$Naturalness_Scale_VB_Tot)## PP$Naturalness_Scale_VB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 93 0.996 65.13 32.6 4.55 21.00
## .25 .50 .75 .90 .95
## 50.00 71.00 89.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 96 0.998 49.87 37.8 0 4
## .25 .50 .75 .90 .95
## 21 48 80 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 99 0.998 49.76 36.59 0 6
## .25 .50 .75 .90 .95
## 24 48 78 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 91 0.999 40.8 33.64 0.00 2.00
## .25 .50 .75 .90 .95
## 19.00 34.00 61.25 91.80 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------#Scale Alphas
##Nat Items (ALL) - Reasonable alphas
psych::alpha(data.frame(PP$Nat_1_GFFB , PP$Nat_4R_GFFB, PP$Nat_2R_GFFB , PP$Nat_3R_GFFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_GFFB, PP$Nat_4R_GFFB, PP$Nat_2R_GFFB,
## PP$Nat_3R_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.62 0.65 0.29 1.7 0.019 50 21 0.31
##
## lower alpha upper 95% confidence boundaries
## 0.59 0.63 0.67
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_GFFB 0.76 0.76 0.69 0.52 3.19 0.013 0.0073 0.50
## PP.Nat_4R_GFFB 0.36 0.36 0.39 0.16 0.56 0.035 0.0875 0.18
## PP.Nat_2R_GFFB 0.40 0.39 0.44 0.18 0.64 0.033 0.1042 0.18
## PP.Nat_3R_GFFB 0.59 0.59 0.55 0.32 1.43 0.023 0.0614 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_GFFB 497 0.44 0.44 0.15 0.088 59 30
## PP.Nat_4R_GFFB 495 0.84 0.83 0.79 0.647 50 32
## PP.Nat_2R_GFFB 496 0.81 0.81 0.75 0.615 43 31
## PP.Nat_3R_GFFB 498 0.66 0.65 0.51 0.361 47 30psych::alpha(data.frame(PP$Nat_1_GFPRB , PP$Nat_4R_GFPRB, PP$Nat_2R_GFPRB , PP$Nat_3R_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_GFPRB, PP$Nat_4R_GFPRB,
## PP$Nat_2R_GFPRB, PP$Nat_3R_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.74 0.72 0.41 2.8 0.012 62 24 0.45
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.75 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_GFPRB 0.80 0.80 0.74 0.57 4.0 0.011 0.0088 0.52
## PP.Nat_4R_GFPRB 0.58 0.56 0.50 0.30 1.3 0.022 0.0351 0.25
## PP.Nat_2R_GFPRB 0.63 0.62 0.57 0.35 1.6 0.020 0.0368 0.38
## PP.Nat_3R_GFPRB 0.71 0.70 0.66 0.44 2.3 0.015 0.0470 0.38
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_GFPRB 513 0.55 0.59 0.37 0.31 74 27
## PP.Nat_4R_GFPRB 513 0.87 0.86 0.84 0.73 63 33
## PP.Nat_2R_GFPRB 514 0.83 0.81 0.76 0.65 59 33
## PP.Nat_3R_GFPRB 511 0.74 0.73 0.59 0.51 54 33psych::alpha(data.frame(PP$Nat_1_CBB , PP$Nat_4R_CBB, PP$Nat_2R_CBB , PP$Nat_3R_CBB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_CBB, PP$Nat_4R_CBB, PP$Nat_2R_CBB,
## PP$Nat_3R_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.72 0.7 0.39 2.5 0.016 34 22 0.38
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_CBB 0.80 0.81 0.74 0.58 4.1 0.011 0.005 0.62
## PP.Nat_4R_CBB 0.55 0.58 0.56 0.31 1.4 0.026 0.074 0.21
## PP.Nat_2R_CBB 0.53 0.55 0.49 0.29 1.2 0.026 0.040 0.26
## PP.Nat_3R_CBB 0.62 0.63 0.59 0.36 1.7 0.022 0.050 0.26
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_CBB 515 0.58 0.54 0.26 0.23 46 34
## PP.Nat_4R_CBB 514 0.81 0.81 0.73 0.61 33 30
## PP.Nat_2R_CBB 514 0.82 0.84 0.80 0.65 30 28
## PP.Nat_3R_CBB 514 0.73 0.76 0.66 0.51 29 28psych::alpha(data.frame(PP$Nat_1_PBPB , PP$Nat_4R_PBPB, PP$Nat_2R_PBPB , PP$Nat_3R_PBPB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_PBPB, PP$Nat_4R_PBPB, PP$Nat_2R_PBPB,
## PP$Nat_3R_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.63 0.63 0.61 0.3 1.7 0.019 42 20 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.59 0.63 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_PBPB 0.71 0.71 0.62 0.44 2.40 0.016 0.0023 0.45
## PP.Nat_4R_PBPB 0.43 0.45 0.42 0.21 0.80 0.031 0.0554 0.21
## PP.Nat_2R_PBPB 0.45 0.45 0.42 0.22 0.83 0.030 0.0460 0.28
## PP.Nat_3R_PBPB 0.59 0.59 0.52 0.32 1.44 0.023 0.0219 0.28
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_PBPB 524 0.57 0.53 0.27 0.20 54 32
## PP.Nat_4R_PBPB 524 0.79 0.79 0.69 0.56 43 30
## PP.Nat_2R_PBPB 522 0.77 0.78 0.69 0.54 40 29
## PP.Nat_3R_PBPB 522 0.63 0.66 0.51 0.36 32 26psych::alpha(data.frame(PP$Nat_1_PBFB , PP$Nat_4R_PBFB, PP$Nat_2R_PBFB , PP$Nat_3R_PBFB))## Number of categories should be increased in order to count frequencies.## Warning in psych::alpha(data.frame(PP$Nat_1_PBFB, PP$Nat_4R_PBFB, PP$Nat_2R_PBFB, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( PP.Nat_1_PBFB ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_PBFB, PP$Nat_4R_PBFB, PP$Nat_2R_PBFB,
## PP$Nat_3R_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.43 0.48 0.54 0.19 0.91 0.03 62 18 0.22
##
## lower alpha upper 95% confidence boundaries
## 0.37 0.43 0.49
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Nat_1_PBFB 0.742 0.74 0.66 0.493 2.91 0.014 0.0019
## PP.Nat_4R_PBFB 0.290 0.34 0.38 0.144 0.51 0.041 0.1167
## PP.Nat_2R_PBFB 0.102 0.16 0.28 0.061 0.19 0.051 0.1294
## PP.Nat_3R_PBFB 0.077 0.12 0.27 0.045 0.14 0.052 0.1581
## med.r
## PP.Nat_1_PBFB 0.495
## PP.Nat_4R_PBFB -0.005
## PP.Nat_2R_PBFB -0.005
## PP.Nat_3R_PBFB -0.098
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_PBFB 481 0.30 0.25 -0.15 -0.16 52 33
## PP.Nat_4R_PBFB 480 0.66 0.67 0.56 0.31 61 31
## PP.Nat_2R_PBFB 480 0.76 0.77 0.71 0.48 66 29
## PP.Nat_3R_PBFB 478 0.77 0.79 0.72 0.53 71 27psych::alpha(data.frame(PP$Nat_1_VB , PP$Nat_4R_VB, PP$Nat_2R_VB , PP$Nat_3R_VB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Nat_1_VB, PP$Nat_4R_VB, PP$Nat_2R_VB,
## PP$Nat_3R_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.69 0.67 0.36 2.2 0.015 51 22 0.37
##
## lower alpha upper 95% confidence boundaries
## 0.67 0.7 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Nat_1_VB 0.75 0.75 0.68 0.50 3.0 0.014 0.010 0.48
## PP.Nat_4R_VB 0.53 0.53 0.47 0.27 1.1 0.025 0.036 0.23
## PP.Nat_2R_VB 0.55 0.54 0.47 0.28 1.2 0.024 0.024 0.33
## PP.Nat_3R_VB 0.66 0.66 0.60 0.39 1.9 0.018 0.037 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Nat_1_VB 472 0.56 0.58 0.33 0.28 65 29
## PP.Nat_4R_VB 472 0.82 0.81 0.75 0.63 50 33
## PP.Nat_2R_VB 472 0.81 0.80 0.74 0.62 50 32
## PP.Nat_3R_VB 472 0.69 0.69 0.53 0.44 41 30Risk Perceptions
# Risk perception was measured with 2 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Risk score calculated by averaging these items.
### Item 1: This is risky to eat.
### Item 2: Producing this is risky for society.
### Item 3: Producing this is risky for the environment.
### Item 4: This is frightening.Grain-fed Feedlot Burger (GFFB)
#GFFB
PP$Risk_1_GFFB <- PP$GFFB_Risk_32
describe(PP$Risk_1_GFFB)## PP$Risk_1_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 498 507 95 0.998 46.96 36.42 0 0
## .25 .50 .75 .90 .95
## 19 51 74 90 100
##
## lowest : 0 1 2 3 4, highest: 94 95 96 99 100range(PP$Risk_1_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_GFFB, main = 'GFFB - This is risky to eat.')
PP$Risk_2_GFFB <- PP$GFFB_Risk_35
describe(PP$Risk_2_GFFB)## PP$Risk_2_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 496 509 94 0.998 46.85 36.51 0.0 0.0
## .25 .50 .75 .90 .95
## 20.0 50.0 72.0 89.5 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_2_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_GFFB, main = 'GFFB - Producing this is risky for society.')
PP$Risk_3_GFFB <- PP$GFFB_Risk_36
describe(PP$Risk_3_GFFB)## PP$Risk_3_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 99 0.999 49.89 36.57 0.0 1.6
## .25 .50 .75 .90 .95
## 24.0 52.0 76.0 94.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_3_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_GFFB, main = 'GFFB - Producing this is risky for the environment.')
PP$Risk_4_GFFB <- PP$GFFB_Risk_33
describe(PP$Risk_4_GFFB)## PP$Risk_4_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 495 510 95 0.997 51.28 38.74 0.0 0.0
## .25 .50 .75 .90 .95
## 22.5 53.0 81.0 99.0 100.0
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100range(PP$Risk_4_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_GFFB, main = 'GFFB - This is frightening.')
#GFFB Risk Scale
PP$Risk_Score_GFFB <- rowMeans(PP [, c("Risk_1_GFFB", "Risk_2_GFFB", "Risk_3_GFFB", "Risk_4_GFFB")], na.rm=TRUE)
PP$Risk_Scale_GFFB <- data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB, PP$Risk_3_GFFB, PP$Risk_4_GFFB)
describe(PP$Risk_Score_GFFB)## PP$Risk_Score_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 499 506 264 1 48.87 31.98 0.00 5.40
## .25 .50 .75 .90 .95
## 28.00 51.75 68.38 86.05 95.52
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 97.00 98.75 99.00 99.50 100.00sd(PP$Risk_Score_GFFB, na.rm = TRUE)## [1] 27.89227#GFFB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB, PP$Risk_3_GFFB, PP$Risk_4_GFFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_GFFB, PP$Risk_2_GFFB, PP$Risk_3_GFFB,
## PP$Risk_4_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.86 0.67 8 0.0059 49 28 0.66
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_GFFB 0.85 0.86 0.81 0.67 6.0 0.0080 0.0050 0.64
## PP.Risk_2_GFFB 0.83 0.83 0.77 0.63 5.1 0.0091 0.0024 0.61
## PP.Risk_3_GFFB 0.85 0.85 0.80 0.65 5.6 0.0085 0.0052 0.64
## PP.Risk_4_GFFB 0.88 0.88 0.84 0.72 7.7 0.0063 0.0011 0.73
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_GFFB 498 0.86 0.87 0.80 0.75 47 32
## PP.Risk_2_GFFB 496 0.90 0.90 0.87 0.81 47 32
## PP.Risk_3_GFFB 497 0.88 0.88 0.83 0.78 50 32
## PP.Risk_4_GFFB 495 0.83 0.82 0.72 0.68 51 34hist(PP$Risk_Score_GFFB, main = 'GFFB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_GFFB, labels = c('1','2', '3', '4'), main = "Correlation Between GFFB Risk Items")
Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$Risk_1_GFPRB <- PP$GFPRB_Risk_32
describe(PP$Risk_1_GFPRB)## PP$Risk_1_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 512 493 99 0.991 36.75 36.32 0.00 0.00
## .25 .50 .75 .90 .95
## 5.00 30.00 63.25 84.00 97.45
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_1_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_GFPRB, main = 'GFPRB - This is risky to eat.')
PP$Risk_2_GFPRB <- PP$GFPRB_Risk_35
describe(PP$Risk_2_GFPRB)## PP$Risk_2_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 511 494 97 0.996 39.91 36.48 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 36.0 66.5 88.0 99.0
##
## lowest : 0 1 2 3 4, highest: 94 97 98 99 100range(PP$Risk_2_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_GFPRB, main = 'GFPRB - Producing this is risky for society.')
PP$Risk_3_GFPRB <- PP$GFPRB_Risk_36
describe(PP$Risk_3_GFPRB)## PP$Risk_3_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 510 495 100 0.995 41.19 36.4 0 0
## .25 .50 .75 .90 .95
## 12 38 67 87 97
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_3_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_GFPRB, main = 'GFPRB - Producing this is risky for the environment.')
PP$Risk_4_GFPRB <- PP$GFPRB_Risk_33
describe(PP$Risk_4_GFPRB)## PP$Risk_4_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 511 494 99 0.992 37 37.4 0.0 0.0
## .25 .50 .75 .90 .95
## 4.0 29.0 63.5 88.0 98.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_4_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_GFPRB, main = 'GFPRB - This is frightening.')
#GFPRB Risk Scale
PP$Risk_Score_GFPRB <- rowMeans(PP [, c("Risk_1_GFPRB", "Risk_2_GFPRB", "Risk_3_GFPRB", "Risk_4_GFPRB")], na.rm=TRUE)
PP$Risk_Scale_GFPRB <- data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB, PP$Risk_3_GFPRB, PP$Risk_4_GFPRB)
describe(PP$Risk_Score_GFPRB)## PP$Risk_Score_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 256 0.999 38.72 31.88 0.00 0.25
## .25 .50 .75 .90 .95
## 14.00 38.50 57.50 77.70 88.55
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 97.00 97.50 98.50 99.75 100.00sd(PP$Risk_Score_GFPRB, na.rm = TRUE)## [1] 27.86542#GFPRB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB, PP$Risk_3_GFPRB, PP$Risk_4_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_GFPRB, PP$Risk_2_GFPRB,
## PP$Risk_3_GFPRB, PP$Risk_4_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.86 0.67 8 0.0058 39 28 0.67
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_GFPRB 0.85 0.85 0.80 0.66 5.9 0.0081 0.0049 0.63
## PP.Risk_2_GFPRB 0.85 0.85 0.79 0.65 5.6 0.0083 0.0012 0.66
## PP.Risk_3_GFPRB 0.85 0.85 0.80 0.66 5.9 0.0080 0.0007 0.68
## PP.Risk_4_GFPRB 0.87 0.87 0.82 0.69 6.8 0.0071 0.0018 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_GFPRB 512 0.87 0.87 0.81 0.76 37 32
## PP.Risk_2_GFPRB 511 0.88 0.88 0.83 0.78 40 32
## PP.Risk_3_GFPRB 510 0.87 0.87 0.81 0.76 41 32
## PP.Risk_4_GFPRB 511 0.85 0.84 0.76 0.72 37 33hist(PP$Risk_Score_GFPRB, main = 'GFPRB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_GFPRB, labels = c('1','2', '3', '4'), main = "Correlation Between GFPRB Risk Items")
Cultured Beef Burgers (CBB)
#CBB
PP$Risk_1_CBB <- PP$CBB_Risk_32
describe(PP$Risk_1_CBB)## PP$Risk_1_CBB
## n missing distinct Info Mean Gmd .05 .10
## 515 490 98 0.998 54.69 37.24 0 6
## .25 .50 .75 .90 .95
## 27 57 82 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_1_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_CBB, main = 'CBB - This is risky to eat.')
PP$Risk_2_CBB <- PP$CBB_Risk_35
describe(PP$Risk_2_CBB)## PP$Risk_2_CBB
## n missing distinct Info Mean Gmd .05 .10
## 515 490 96 0.998 55.18 36.39 0.0 5.8
## .25 .50 .75 .90 .95
## 30.0 59.0 81.0 100.0 100.0
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_2_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_CBB, main = 'CBB - Producing this is risky for society.')
PP$Risk_3_CBB <- PP$CBB_Risk_36
describe(PP$Risk_3_CBB)## PP$Risk_3_CBB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 97 0.999 50.98 36.4 0 5
## .25 .50 .75 .90 .95
## 26 51 77 97 100
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(PP$Risk_3_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_CBB, main = 'CBB - Producing this is risky for the environment.')
PP$Risk_4_CBB <- PP$CBB_Risk_33
describe(PP$Risk_4_CBB)## PP$Risk_4_CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 97 0.996 55.98 38.42 0 3
## .25 .50 .75 .90 .95
## 27 60 86 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_4_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_CBB, main = 'CBB - This is frightening.')
#CBB Risk Scale
PP$Risk_Score_CBB <- rowMeans(PP [, c("Risk_1_CBB", "Risk_2_CBB", "Risk_3_CBB", "Risk_4_CBB")], na.rm=TRUE)
PP$Risk_Scale_CBB <- data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB, PP$Risk_3_CBB, PP$Risk_4_CBB)
describe(PP$Risk_Score_CBB)## PP$Risk_Score_CBB
## n missing distinct Info Mean Gmd .05 .10
## 517 488 267 1 54.2 32.1 2.20 12.65
## .25 .50 .75 .90 .95
## 36.50 53.50 75.00 93.30 100.00
##
## lowest : 0.00 0.25 0.50 1.00 1.25, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Risk_Score_CBB, na.rm = TRUE)## [1] 28.05343#CBB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB, PP$Risk_3_CBB, PP$Risk_4_CBB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_CBB, PP$Risk_2_CBB, PP$Risk_3_CBB,
## PP$Risk_4_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.87 0.68 8.5 0.0055 54 28 0.69
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_CBB 0.85 0.85 0.80 0.66 5.8 0.0081 0.00312 0.67
## PP.Risk_2_CBB 0.86 0.86 0.80 0.66 5.9 0.0079 0.00449 0.66
## PP.Risk_3_CBB 0.88 0.88 0.83 0.71 7.2 0.0067 0.00081 0.71
## PP.Risk_4_CBB 0.87 0.87 0.82 0.69 6.8 0.0071 0.00068 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_CBB 515 0.89 0.89 0.84 0.80 55 32
## PP.Risk_2_CBB 515 0.88 0.89 0.84 0.79 55 32
## PP.Risk_3_CBB 513 0.85 0.85 0.77 0.73 51 32
## PP.Risk_4_CBB 514 0.86 0.86 0.80 0.75 56 33hist(PP$Risk_Score_CBB, main = 'CBB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_CBB, labels = c('1','2', '3', '4'), main = "Correlation Between CBB Risk Items")
Plant-based Protein Burger (PBPB)
#PBPB
PP$Risk_1_PBPB <- PP$PBPB_Risk_32
describe(PP$Risk_1_PBPB)## PP$Risk_1_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 523 482 96 0.998 43.28 36.03 0 0
## .25 .50 .75 .90 .95
## 15 43 68 88 100
##
## lowest : 0 1 2 3 4, highest: 94 95 97 99 100range(PP$Risk_1_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_PBPB, main = 'PBPB - This is risky to eat.')
PP$Risk_2_PBPB <- PP$PBPB_Risk_35
describe(PP$Risk_2_PBPB)## PP$Risk_2_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 100 0.998 43.06 35.68 0.00 0.00
## .25 .50 .75 .90 .95
## 15.00 41.00 68.00 86.00 98.85
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_2_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_PBPB, main = 'PBPB - Producing this is risky for society.')
PP$Risk_3_PBPB <- PP$PBPB_Risk_36
describe(PP$Risk_3_PBPB)## PP$Risk_3_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 98 0.998 40.95 34.56 0.00 0.00
## .25 .50 .75 .90 .95
## 16.00 38.00 64.00 85.00 96.85
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(PP$Risk_3_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_PBPB, main = 'PBPB - Producing this is risky for the environment.')
PP$Risk_4_PBPB <- PP$PBPB_Risk_33
describe(PP$Risk_4_PBPB)## PP$Risk_4_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 98 0.996 42.61 36.32 0.00 0.00
## .25 .50 .75 .90 .95
## 14.25 39.00 68.00 87.90 100.00
##
## lowest : 0 1 2 3 4, highest: 94 95 98 99 100range(PP$Risk_4_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_PBPB, main = 'PBPB - This is frightening.')
#PBPB Risk Scale
PP$Risk_Score_PBPB <- rowMeans(PP [, c("Risk_1_PBPB", "Risk_2_PBPB", "Risk_3_PBPB", "Risk_4_PBPB")], na.rm=TRUE)
PP$Risk_Scale_PBPB <- data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB, PP$Risk_3_PBPB, PP$Risk_4_PBPB)
describe(PP$Risk_Score_PBPB)## PP$Risk_Score_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 258 1 42.48 31.14 0.000 2.325
## .25 .50 .75 .90 .95
## 20.188 44.250 60.250 79.600 90.962
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.00 98.25 98.50 99.75 100.00sd(PP$Risk_Score_PBPB, na.rm = TRUE)## [1] 27.19177#PBPB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB, PP$Risk_3_PBPB, PP$Risk_4_PBPB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_PBPB, PP$Risk_2_PBPB, PP$Risk_3_PBPB,
## PP$Risk_4_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.69 8.9 0.0052 42 27 0.7
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_PBPB 0.87 0.87 0.82 0.68 6.5 0.0073 0.00180 0.69
## PP.Risk_2_PBPB 0.86 0.86 0.81 0.67 6.1 0.0077 0.00306 0.64
## PP.Risk_3_PBPB 0.88 0.88 0.83 0.71 7.4 0.0065 0.00046 0.71
## PP.Risk_4_PBPB 0.87 0.87 0.82 0.69 6.7 0.0071 0.00191 0.71
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_PBPB 523 0.88 0.88 0.83 0.78 43 31
## PP.Risk_2_PBPB 524 0.89 0.89 0.85 0.80 43 31
## PP.Risk_3_PBPB 524 0.85 0.86 0.78 0.74 41 30
## PP.Risk_4_PBPB 522 0.88 0.88 0.82 0.77 43 32hist(PP$Risk_Score_PBPB, main = 'PBPB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_PBPB, labels = c('1','2', '3', '4'), main = "Correlation Between PBPB Risk Items")
Plant-based Fermentation Burger (PBFB)
#PBFB
PP$Risk_1_PBFB <- PP$PBFB_Risk_32
describe(PP$Risk_1_PBFB)## PP$Risk_1_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 480 525 100 0.999 49.52 37.36 0.00 2.00
## .25 .50 .75 .90 .95
## 21.75 51.00 76.00 97.00 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 97 98 100range(PP$Risk_1_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_PBFB, main = 'PBFB - This is risky to eat.')
PP$Risk_2_PBFB <- PP$PBPB_Risk_35
describe(PP$Risk_2_PBFB)## PP$Risk_2_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 100 0.998 43.06 35.68 0.00 0.00
## .25 .50 .75 .90 .95
## 15.00 41.00 68.00 86.00 98.85
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$Risk_2_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_PBFB, main = 'PBFB - Producing this is risky for society.')
PP$Risk_3_PBFB <- PP$PBPB_Risk_36
describe(PP$Risk_3_PBFB)## PP$Risk_3_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 98 0.998 40.95 34.56 0.00 0.00
## .25 .50 .75 .90 .95
## 16.00 38.00 64.00 85.00 96.85
##
## lowest : 0 1 2 4 5, highest: 96 97 98 99 100range(PP$Risk_3_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_PBFB, main = 'PBFB - Producing this is risky for the environment.')
PP$Risk_4_PBFB <- PP$PBPB_Risk_33
describe(PP$Risk_4_PBFB)## PP$Risk_4_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 98 0.996 42.61 36.32 0.00 0.00
## .25 .50 .75 .90 .95
## 14.25 39.00 68.00 87.90 100.00
##
## lowest : 0 1 2 3 4, highest: 94 95 98 99 100range(PP$Risk_4_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_PBFB, main = 'PBFB - This is frightening.')
#PBFB Risk Scale
PP$Risk_Score_PBFB <- rowMeans(PP [, c("Risk_1_PBFB", "Risk_2_PBFB", "Risk_3_PBFB", "Risk_4_PBFB")], na.rm=TRUE)
PP$Risk_Scale_PBFB <- data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB, PP$Risk_3_PBFB, PP$Risk_4_PBFB)
describe(PP$Risk_Score_PBFB)## PP$Risk_Score_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 795 210 317 1 45.56 33.89 0.000 3.133
## .25 .50 .75 .90 .95
## 21.833 47.250 67.000 87.733 100.000
##
## lowest : 0.0000000 0.2500000 0.3333333 0.5000000 1.0000000
## highest: 97.0000000 97.6666667 98.0000000 99.6666667 100.0000000sd(PP$Risk_Score_PBFB, na.rm = TRUE)## [1] 29.45914#PBFB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB, PP$Risk_3_PBFB, PP$Risk_4_PBFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_PBFB, PP$Risk_2_PBFB, PP$Risk_3_PBFB,
## PP$Risk_4_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.82 0.58 5.5 0.0081 46 29 0.57
##
## lower alpha upper 95% confidence boundaries
## 0.83 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_PBFB 0.87 0.87 0.82 0.68 6.5 0.0073 0.0018 0.69
## PP.Risk_2_PBFB 0.77 0.77 0.70 0.52 3.3 0.0129 0.0107 0.49
## PP.Risk_3_PBFB 0.79 0.79 0.73 0.56 3.8 0.0116 0.0133 0.49
## PP.Risk_4_PBFB 0.78 0.79 0.74 0.55 3.7 0.0121 0.0227 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_PBFB 480 0.91 0.73 0.57 0.53 50 32
## PP.Risk_2_PBFB 524 0.90 0.88 0.84 0.77 43 31
## PP.Risk_3_PBFB 524 0.87 0.85 0.79 0.71 41 30
## PP.Risk_4_PBFB 522 0.87 0.85 0.79 0.73 43 32hist(PP$Risk_Score_PBFB, main = 'PBFB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_PBFB, labels = c('1','2', '3', '4'), main = "Correlation Between PBFB Risk Items")
Veggie Burger (VB)
#VB
PP$Risk_1_VB <- PP$VB_Risk_32
describe(PP$Risk_1_VB)## PP$Risk_1_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 92 0.997 35.1 33.26 0.0 0.0
## .25 .50 .75 .90 .95
## 9.0 29.0 56.5 79.0 88.0
##
## lowest : 0 1 2 3 4, highest: 91 92 96 99 100range(PP$Risk_1_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_1_VB, main = 'VB - This is risky to eat.')
PP$Risk_2_VB <- PP$VB_Risk_35
describe(PP$Risk_2_VB)## PP$Risk_2_VB
## n missing distinct Info Mean Gmd .05 .10
## 469 536 91 0.996 37.72 35.36 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 33.0 60.0 86.2 100.0
##
## lowest : 0 1 2 3 4, highest: 94 95 96 98 100range(PP$Risk_2_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_2_VB, main = 'VB - Producing this is risky for society.')
PP$Risk_3_VB <- PP$VB_Risk_36
describe(PP$Risk_3_VB)## PP$Risk_3_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 96 0.997 36.56 34.05 0.0 0.0
## .25 .50 .75 .90 .95
## 10.0 31.0 58.0 82.0 93.5
##
## lowest : 0 1 2 3 4, highest: 94 95 97 98 100range(PP$Risk_3_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_3_VB, main = 'VB - Producing this is risky for the environment.')
PP$Risk_4_VB <- PP$VB_Risk_33
describe(PP$Risk_4_VB)## PP$Risk_4_VB
## n missing distinct Info Mean Gmd .05 .10
## 470 535 95 0.996 36.27 35.59 0.0 0.0
## .25 .50 .75 .90 .95
## 8.0 28.5 62.0 82.1 95.1
##
## lowest : 0 1 2 3 4, highest: 94 96 98 99 100range(PP$Risk_4_VB, na.rm=TRUE)## [1] 0 100hist(PP$Risk_4_VB, main = 'VB - This is frightening.')
#VB Risk Scale
PP$Risk_Score_VB <- rowMeans(PP [, c("Risk_1_VB", "Risk_2_VB", "Risk_3_VB", "Risk_4_VB")], na.rm=TRUE)
PP$Risk_Scale_VB <- data.frame(PP$Risk_1_VB, PP$Risk_2_VB, PP$Risk_3_VB, PP$Risk_4_VB)
describe(PP$Risk_Score_VB)## PP$Risk_Score_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 247 1 36.41 30.29 0.00 1.25
## .25 .50 .75 .90 .95
## 13.62 32.75 55.00 73.00 84.88
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 95.00 96.50 97.25 99.00 100.00sd(PP$Risk_Score_VB, na.rm = TRUE)## [1] 26.56997#VB Cronbach's alpha for risk scale
psych::alpha(data.frame(PP$Risk_1_VB, PP$Risk_2_VB, PP$Risk_3_VB, PP$Risk_4_VB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$Risk_1_VB, PP$Risk_2_VB, PP$Risk_3_VB,
## PP$Risk_4_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.87 0.68 8.5 0.0055 36 27 0.68
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Risk_1_VB 0.86 0.86 0.81 0.67 6.1 0.0078 0.00301 0.65
## PP.Risk_2_VB 0.86 0.86 0.81 0.67 6.2 0.0077 0.00198 0.69
## PP.Risk_3_VB 0.86 0.86 0.81 0.68 6.4 0.0075 0.00072 0.68
## PP.Risk_4_VB 0.87 0.87 0.82 0.70 6.9 0.0069 0.00076 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Risk_1_VB 471 0.88 0.88 0.83 0.78 35 29
## PP.Risk_2_VB 469 0.88 0.88 0.82 0.78 38 31
## PP.Risk_3_VB 471 0.87 0.87 0.81 0.77 37 30
## PP.Risk_4_VB 470 0.86 0.86 0.78 0.74 36 31hist(PP$Risk_Score_VB, main = 'VB Risk Scale Score')
#Correlation
cor.plot(PP$Risk_Scale_VB, labels = c('1','2', '3', '4'), main = "Correlation Between VB Risk Items")
Support
# Willingness to support was measured with 4 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Support score calculated by averaging these items.
### Item 1: I support this.
### Item 2: I would purchase this product.
### Item 3: Society should support this.
### Item 4: Society should purchase this product.Grain-fed Feedlot Burger (GFFB)
# Behavioral Intent (SUPPORT) Scales and Scores
### Rename Variables
PP$BehavInt1_GFFB <- PP$GFFB_BehavIntent_29
PP$BehavInt2_GFFB <- PP$GFFB_BehavIntent_28
PP$BehavInt3_GFFB <- PP$GFFB_BehavIntent_27
PP$BehavInt4_GFFB <- PP$GFFB_BehavIntent_26
#Histograms
hist(PP$BehavInt1_GFFB)
hist(PP$BehavInt2_GFFB)
hist(PP$BehavInt3_GFFB)
hist(PP$BehavInt4_GFFB)
#Support Score
PP$Behav_Score_GFFB <- rowMeans(PP [, c("BehavInt1_GFFB", "BehavInt2_GFFB", "BehavInt3_GFFB", "BehavInt4_GFFB")], na.rm=TRUE)
PP$Behav_Scale_GFFB <- data.frame(PP$BehavInt1_GFFB, PP$BehavInt2_GFFB, PP$BehavInt3_GFFB, PP$BehavInt4_GFFB)
describe(PP$Behav_Score_GFFB)## PP$Behav_Score_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 498 507 242 0.999 57.91 33.2 0.425 11.425
## .25 .50 .75 .90 .95
## 41.500 59.250 80.750 99.250 100.000
##
## lowest : 0.00 0.50 0.75 1.25 1.75, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Behav_Score_GFFB, na.rm= TRUE)## [1] 29.21738Grain-fed Pasture Raised Burger (GFPRB)
##GFPRB
PP$BehavInt1_GFPRB <- PP$PBPB_BehavIntent_29
PP$BehavInt2_GFPRB <- PP$PBPB_BehavIntent_28
PP$BehavInt3_GFPRB <- PP$PBPB_BehavIntent_27
PP$BehavInt4_GFPRB <- PP$PBPB_BehavIntent_26
# Histograms
hist(PP$BehavInt1_GFPRB)
hist(PP$BehavInt2_GFPRB)
hist(PP$BehavInt3_GFPRB)
hist(PP$BehavInt4_GFPRB)
PP$Behav_Score_GFPRB <- rowMeans(PP [, c("BehavInt1_GFPRB", "BehavInt2_GFPRB", "BehavInt3_GFPRB", "BehavInt4_GFPRB")], na.rm=TRUE)
PP$Behav_Scale_GFPRB <- data.frame(PP$BehavInt1_GFPRB, PP$BehavInt2_GFPRB, PP$BehavInt3_GFPRB, PP$BehavInt4_GFPRB)
describe(PP$Behav_Score_GFPRB)## PP$Behav_Score_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 252 0.999 57.53 33.44 0.5125 9.8000
## .25 .50 .75 .90 .95
## 37.8750 60.0000 80.5000 97.1250 100.0000
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.50 98.75 99.25 99.75 100.00sd(PP$Behav_Score_GFPRB, na.rm= TRUE)## [1] 29.36235Cultured Beef Burgers (CBB)
##CBB
PP$BehavInt1_CBB <- PP$CBB_BehavIntent_29
PP$BehavInt2_CBB <- PP$CBB_BehavIntent_28
PP$BehavInt3_CBB <- PP$CBB_BehavIntent_27
PP$BehavInt4_CBB <- PP$CBB_BehavIntent_26
# Histograms
hist(PP$BehavInt1_CBB)
hist(PP$BehavInt2_CBB)
hist(PP$BehavInt3_CBB)
hist(PP$BehavInt4_CBB)
# Scales
PP$Behav_Score_CBB <- rowMeans(PP [, c("BehavInt1_CBB", "BehavInt2_CBB", "BehavInt3_CBB", "BehavInt4_CBB")], na.rm=TRUE)
PP$Behav_Scale_CBB <- data.frame(PP$BehavInt1_CBB, PP$BehavInt2_CBB, PP$BehavInt3_CBB, PP$BehavInt4_CBB)
describe(PP$Behav_Score_CBB)## PP$Behav_Score_CBB
## n missing distinct Info Mean Gmd .05 .10
## 516 489 249 0.999 49.36 36.5 0.00 0.25
## .25 .50 .75 .90 .95
## 21.44 52.50 74.81 94.25 100.00
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Behav_Score_CBB, na.rm= TRUE)## [1] 31.7638Plant-based Protein Burger (PBPB)
##PBPB
PP$BehavInt1_PBPB <- PP$PBPB_BehavIntent_29
PP$BehavInt2_PBPB <- PP$PBPB_BehavIntent_28
PP$BehavInt3_PBPB <- PP$PBPB_BehavIntent_27
PP$BehavInt4_PBPB <- PP$PBPB_BehavIntent_26
# Histograms
hist(PP$BehavInt1_PBPB)
hist(PP$BehavInt2_PBPB)
hist(PP$BehavInt3_PBPB)
hist(PP$BehavInt4_PBPB)
PP$Behav_Score_PBPB <- rowMeans(PP [, c("BehavInt1_PBPB", "BehavInt2_PBPB", "BehavInt3_PBPB", "BehavInt4_PBPB")], na.rm=TRUE)
PP$Behav_Scale_PBPB <- data.frame(PP$BehavInt1_PBPB, PP$BehavInt2_PBPB, PP$BehavInt3_PBPB, PP$BehavInt4_PBPB)
describe(PP$Behav_Score_PBPB)## PP$Behav_Score_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 252 0.999 57.53 33.44 0.5125 9.8000
## .25 .50 .75 .90 .95
## 37.8750 60.0000 80.5000 97.1250 100.0000
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.50 98.75 99.25 99.75 100.00sd(PP$Behav_Score_PBPB, na.rm= TRUE)## [1] 29.36235Plant-based Fermentation Burger (PBFB)
##PBFB
PP$BehavInt1_PBFB <- PP$PBFB_BehavIntent_29
PP$BehavInt2_PBFB <- PP$PBFB_BehavIntent_28
PP$BehavInt3_PBFB <- PP$PBFB_BehavIntent_27
PP$BehavInt4_PBFB <- PP$PBFB_BehavIntent_26
# Histograms
hist(PP$BehavInt1_PBFB)
hist(PP$BehavInt2_PBFB)
hist(PP$BehavInt3_PBFB)
hist(PP$BehavInt4_PBFB)
PP$Behav_Score_PBFB <- rowMeans(PP [, c("BehavInt1_PBFB", "BehavInt2_PBFB", "BehavInt3_PBFB", "BehavInt4_PBFB")], na.rm=TRUE)
PP$Behav_Scale_PBFB <- data.frame(PP$BehavInt1_PBFB, PP$BehavInt2_PBFB, PP$BehavInt3_PBFB, PP$BehavInt4_PBFB)
describe(PP$Behav_Score_PBFB)## PP$Behav_Score_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 257 1 52.69 35.61 0.00 1.95
## .25 .50 .75 .90 .95
## 29.88 54.25 77.62 93.65 99.75
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 98.25 99.00 99.25 99.75 100.00sd(PP$Behav_Score_PBFB, na.rm= TRUE)## [1] 31.0349Veggie Burger (VB)
##VB
PP$BehavInt1_VB <- PP$VB_BehavIntent_29
PP$BehavInt2_VB <- PP$VB_BehavIntent_28
PP$BehavInt3_VB <- PP$VB_BehavIntent_27
PP$BehavInt4_VB <- PP$VB_BehavIntent_26
# Histograms
hist(PP$BehavInt1_VB)
hist(PP$BehavInt2_VB)
hist(PP$BehavInt3_VB)
hist(PP$BehavInt4_VB)
PP$Behav_Score_VB <- rowMeans(PP [, c("BehavInt1_VB", "BehavInt2_VB", "BehavInt3_VB", "BehavInt4_VB")], na.rm=TRUE)
PP$Behav_Scale_VB <- data.frame(PP$BehavInt1_VB, PP$BehavInt2_VB, PP$BehavInt3_VB, PP$BehavInt4_VB)
describe(PP$Behav_Score_VB)## PP$Behav_Score_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 238 0.999 62.93 30.95 8.375 22.250
## .25 .50 .75 .90 .95
## 47.750 66.000 84.500 99.000 100.000
##
## lowest : 0.00 0.25 0.75 1.25 1.50, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Behav_Score_VB, na.rm= TRUE)## [1] 27.38456#Scale Alphas
##Behav Items (ALL) - Very good alphas!
psych::alpha(data.frame(PP$BehavInt1_GFFB, PP$BehavInt2_GFFB, PP$BehavInt3_GFFB, PP$BehavInt4_GFFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_GFFB, PP$BehavInt2_GFFB,
## PP$BehavInt3_GFFB, PP$BehavInt4_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.94 0.84 21 0.0024 58 29 0.85
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_GFFB 0.93 0.93 0.91 0.82 14 0.0038 2.7e-03
## PP.BehavInt2_GFFB 0.95 0.95 0.93 0.87 21 0.0025 4.3e-05
## PP.BehavInt3_GFFB 0.93 0.93 0.91 0.83 14 0.0036 1.8e-03
## PP.BehavInt4_GFFB 0.94 0.94 0.91 0.83 15 0.0034 1.3e-03
## med.r
## PP.BehavInt1_GFFB 0.80
## PP.BehavInt2_GFFB 0.87
## PP.BehavInt3_GFFB 0.83
## PP.BehavInt4_GFFB 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_GFFB 498 0.95 0.95 0.93 0.91 58 32
## PP.BehavInt2_GFFB 498 0.91 0.91 0.86 0.84 60 32
## PP.BehavInt3_GFFB 497 0.95 0.95 0.93 0.90 57 31
## PP.BehavInt4_GFFB 497 0.94 0.94 0.92 0.89 57 31psych::alpha(data.frame(PP$BehavInt1_GFPRB, PP$BehavInt2_GFPRB, PP$BehavInt3_GFPRB, PP$BehavInt4_GFPRB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_GFPRB, PP$BehavInt2_GFPRB,
## PP$BehavInt3_GFPRB, PP$BehavInt4_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.94 0.83 20 0.0025 58 29 0.84
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_GFPRB 0.93 0.93 0.91 0.83 14 0.0038 0.00231
## PP.BehavInt2_GFPRB 0.95 0.95 0.93 0.86 19 0.0028 0.00032
## PP.BehavInt3_GFPRB 0.93 0.93 0.91 0.83 14 0.0036 0.00039
## PP.BehavInt4_GFPRB 0.93 0.93 0.91 0.83 14 0.0037 0.00124
## med.r
## PP.BehavInt1_GFPRB 0.81
## PP.BehavInt2_GFPRB 0.86
## PP.BehavInt3_GFPRB 0.83
## PP.BehavInt4_GFPRB 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_GFPRB 522 0.94 0.94 0.92 0.90 57 32
## PP.BehavInt2_GFPRB 521 0.92 0.91 0.87 0.85 54 34
## PP.BehavInt3_GFPRB 521 0.94 0.94 0.92 0.90 60 29
## PP.BehavInt4_GFPRB 521 0.94 0.94 0.92 0.89 59 30psych::alpha(data.frame(PP$BehavInt1_CBB, PP$BehavInt2_CBB, PP$BehavInt3_CBB, PP$BehavInt4_CBB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_CBB, PP$BehavInt2_CBB,
## PP$BehavInt3_CBB, PP$BehavInt4_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.96 0.96 0.95 0.86 25 0.002 49 32 0.87
##
## lower alpha upper 95% confidence boundaries
## 0.96 0.96 0.97
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_CBB 0.94 0.94 0.92 0.85 17 0.0031 0.00040
## PP.BehavInt2_CBB 0.95 0.95 0.93 0.87 21 0.0025 0.00017
## PP.BehavInt3_CBB 0.95 0.95 0.93 0.86 19 0.0028 0.00077
## PP.BehavInt4_CBB 0.95 0.95 0.93 0.87 21 0.0026 0.00066
## med.r
## PP.BehavInt1_CBB 0.84
## PP.BehavInt2_CBB 0.87
## PP.BehavInt3_CBB 0.86
## PP.BehavInt4_CBB 0.89
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_CBB 516 0.96 0.96 0.95 0.93 49 34
## PP.BehavInt2_CBB 515 0.94 0.94 0.91 0.89 47 35
## PP.BehavInt3_CBB 515 0.95 0.95 0.93 0.91 51 33
## PP.BehavInt4_CBB 516 0.94 0.94 0.91 0.89 50 32psych::alpha(data.frame(PP$BehavInt1_PBPB, PP$BehavInt2_PBPB, PP$BehavInt3_PBPB, PP$BehavInt4_PBPB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_PBPB, PP$BehavInt2_PBPB,
## PP$BehavInt3_PBPB, PP$BehavInt4_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.94 0.83 20 0.0025 58 29 0.84
##
## lower alpha upper 95% confidence boundaries
## 0.95 0.95 0.96
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_PBPB 0.93 0.93 0.91 0.83 14 0.0038 0.00231
## PP.BehavInt2_PBPB 0.95 0.95 0.93 0.86 19 0.0028 0.00032
## PP.BehavInt3_PBPB 0.93 0.93 0.91 0.83 14 0.0036 0.00039
## PP.BehavInt4_PBPB 0.93 0.93 0.91 0.83 14 0.0037 0.00124
## med.r
## PP.BehavInt1_PBPB 0.81
## PP.BehavInt2_PBPB 0.86
## PP.BehavInt3_PBPB 0.83
## PP.BehavInt4_PBPB 0.83
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_PBPB 522 0.94 0.94 0.92 0.90 57 32
## PP.BehavInt2_PBPB 521 0.92 0.91 0.87 0.85 54 34
## PP.BehavInt3_PBPB 521 0.94 0.94 0.92 0.90 60 29
## PP.BehavInt4_PBPB 521 0.94 0.94 0.92 0.89 59 30psych::alpha(data.frame(PP$BehavInt1_PBFB, PP$BehavInt2_PBFB, PP$BehavInt3_PBFB, PP$BehavInt4_PBFB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_PBFB, PP$BehavInt2_PBFB,
## PP$BehavInt3_PBFB, PP$BehavInt4_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.95 0.95 0.94 0.83 20 0.0026 53 31 0.82
##
## lower alpha upper 95% confidence boundaries
## 0.94 0.95 0.95
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_PBFB 0.93 0.94 0.91 0.83 14 0.0038 3.7e-03
## PP.BehavInt2_PBFB 0.95 0.95 0.93 0.86 18 0.0029 1.2e-03
## PP.BehavInt3_PBFB 0.93 0.93 0.90 0.82 13 0.0038 7.1e-05
## PP.BehavInt4_PBFB 0.93 0.93 0.90 0.82 13 0.0039 1.4e-03
## med.r
## PP.BehavInt1_PBFB 0.81
## PP.BehavInt2_PBFB 0.85
## PP.BehavInt3_PBFB 0.82
## PP.BehavInt4_PBFB 0.82
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_PBFB 479 0.94 0.94 0.91 0.88 53 34
## PP.BehavInt2_PBFB 478 0.92 0.91 0.86 0.84 48 36
## PP.BehavInt3_PBFB 478 0.94 0.94 0.93 0.90 56 32
## PP.BehavInt4_PBFB 479 0.94 0.94 0.93 0.90 54 32psych::alpha(data.frame(PP$BehavInt1_VB, PP$BehavInt2_VB, PP$BehavInt3_VB, PP$BehavInt4_VB))## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = data.frame(PP$BehavInt1_VB, PP$BehavInt2_VB,
## PP$BehavInt3_VB, PP$BehavInt4_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.93 0.93 0.92 0.77 13 0.0038 63 27 0.77
##
## lower alpha upper 95% confidence boundaries
## 0.92 0.93 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.BehavInt1_VB 0.89 0.90 0.87 0.74 8.6 0.0062 0.00841
## PP.BehavInt2_VB 0.94 0.94 0.91 0.83 14.7 0.0035 0.00089
## PP.BehavInt3_VB 0.90 0.91 0.88 0.77 9.8 0.0053 0.00554
## PP.BehavInt4_VB 0.89 0.90 0.86 0.74 8.5 0.0059 0.00360
## med.r
## PP.BehavInt1_VB 0.70
## PP.BehavInt2_VB 0.85
## PP.BehavInt3_VB 0.75
## PP.BehavInt4_VB 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.BehavInt1_VB 470 0.93 0.93 0.91 0.87 64 30
## PP.BehavInt2_VB 471 0.87 0.86 0.78 0.75 59 33
## PP.BehavInt3_VB 471 0.91 0.91 0.88 0.84 65 29
## PP.BehavInt4_VB 471 0.93 0.93 0.92 0.88 64 28Understanding
# Understanding was measured with 1 item on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree').
### Item 1: I understand how this works.Grain-fed Feedlot Burger (GFFB)
PP$Understanding_GFFB <- PP$GFFB_Risk_30
length(PP$Understanding_GFFB)## [1] 1005describe(PP$Understanding_GFFB)## PP$Understanding_GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 94 0.994 69.54 29.72 16 29
## .25 .50 .75 .90 .95
## 53 74 93 100 100
##
## lowest : 0 1 2 4 6, highest: 96 97 98 99 100sd(PP$Understanding_GFFB, na.rm=TRUE)## [1] 26.81107range(PP$Understanding_GFFB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_GFFB, main = 'GFFB - I understand how this works.')
Grain-fed Pasture Raised Burger (GFPRB)
PP$Understanding_GFPRB <- PP$GFPRB_Risk_30
length(PP$Understanding_GFPRB)## [1] 1005describe(PP$Understanding_GFPRB)## PP$Understanding_GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 512 493 89 0.987 72.88 29.17 19.0 32.1
## .25 .50 .75 .90 .95
## 56.0 79.0 98.0 100.0 100.0
##
## lowest : 0 1 2 5 8, highest: 96 97 98 99 100sd(PP$Understanding_GFPRB, na.rm=TRUE)## [1] 26.7755range(PP$Understanding_GFPRB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_GFPRB, main = 'GFPRB - I understand how this works.')
Cultured Beef Burgers (CBB)
PP$Understanding_CBB <- PP$CBB_Risk_30
length(PP$Understanding_CBB)## [1] 1005describe(PP$Understanding_CBB)## PP$Understanding_CBB
## n missing distinct Info Mean Gmd .05 .10
## 515 490 99 0.998 57.91 35.57 0 10
## .25 .50 .75 .90 .95
## 33 62 84 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Understanding_CBB, na.rm=TRUE)## [1] 31.06378range(PP$Understanding_CBB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_CBB, main = 'CBB - I understand how this works.')
Plant-based Protein Burger (PBPB)
PP$Understanding_PBPB <- PP$PBPB_Risk_30
length(PP$Understanding_PBPB)## [1] 1005describe(PP$Understanding_PBPB)## PP$Understanding_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 90 0.997 63.28 31.66 10.00 24.00
## .25 .50 .75 .90 .95
## 44.75 67.00 86.00 100.00 100.00
##
## lowest : 0 1 3 4 5, highest: 95 97 98 99 100sd(PP$Understanding_PBPB, na.rm=TRUE)## [1] 27.92296range(PP$Understanding_PBPB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_PBPB, main = 'PBPB - I understand how this works.')
Plant-based Fermentation Burger (PBFB)
PP$Understanding_PBFB <- PP$PBFB_Risk_30
length(PP$Understanding_PBFB)## [1] 1005describe(PP$Understanding_PBFB)## PP$Understanding_PBFB
## n missing distinct Info Mean Gmd .05 .10
## 480 525 97 0.998 57.91 35.23 0.00 10.00
## .25 .50 .75 .90 .95
## 33.75 64.00 82.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100sd(PP$Understanding_PBFB, na.rm=TRUE)## [1] 30.87369range(PP$Understanding_PBFB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_PBFB, main = 'PBFB - I understand how this works.')
#VB
PP$Understanding_VB <- PP$VB_Risk_30
length(PP$Understanding_VB)## [1] 1005describe(PP$Understanding_VB)## PP$Understanding_VB
## n missing distinct Info Mean Gmd .05 .10
## 471 534 90 0.993 68.59 30.32 17 27
## .25 .50 .75 .90 .95
## 52 75 90 100 100
##
## lowest : 0 1 3 5 8, highest: 96 97 98 99 100sd(PP$Understanding_VB, na.rm=TRUE)## [1] 27.18276range(PP$Understanding_VB, na.rm=TRUE)## [1] 0 100hist(PP$Understanding_VB, main = 'VB - I understand how this works.')
write.csv(PP, "PP_Ag.csv")Difference Benefit/Risk Scores
#Difference Score
PP$BRDiff.GFFB <- (PP$Ben_Score_GFFB - PP$Risk_Score_GFFB)
PP$BRDiff.GFPRB <- (PP$Ben_Score_GFPRB - PP$Risk_Score_GFPRB)
PP$BRDiff.CBB <- (PP$Ben_Score_CBB - PP$Risk_Score_CBB)
PP$BRDiff.PBPB <- (PP$Ben_Score_PBPB - PP$Risk_Score_PBPB)
PP$BRDiff.PBFB <- (PP$Ben_Score_PBFB - PP$Risk_Score_PBFB)
PP$BRDiff.VB <- (PP$Ben_Score_VB - PP$Risk_Score_VB)
#Descriptives
describe(PP$BRDiff.GFFB)## PP$BRDiff.GFFB
## n missing distinct Info Mean Gmd .05 .10
## 497 508 408 1 7.513 45.2 -73.467 -41.500
## .25 .50 .75 .90 .95
## -9.167 3.417 26.667 66.017 91.367
##
## lowest : -100.00000 -99.33333 -94.50000 -94.08333 -92.25000
## highest: 97.50000 98.75000 99.50000 99.75000 100.00000describe(PP$BRDiff.GFPRB)## PP$BRDiff.GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 419 1 28.56 45.18 -25.0333 -11.9000
## .25 .50 .75 .90 .95
## 0.1667 18.0833 62.0833 92.8000 99.8500
##
## lowest : -100.00000 -97.66667 -97.16667 -95.75000 -82.25000
## highest: 99.25000 99.33333 99.50000 99.75000 100.00000describe(PP$BRDiff.CBB)## PP$BRDiff.CBB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 430 1 -1.617 47.28 -81.7792 -68.8583
## .25 .50 .75 .90 .95
## -19.6667 -0.3333 17.4167 56.9333 77.4042
##
## lowest : -100.00000 -99.33333 -99.00000 -97.00000 -96.00000
## highest: 97.75000 98.00000 99.50000 99.75000 100.00000describe(PP$BRDiff.PBPB)## PP$BRDiff.PBPB
## n missing distinct Info Mean Gmd .05 .10
## 520 485 434 1 19.09 47.23 -57.13 -25.43
## .25 .50 .75 .90 .95
## -4.25 10.21 47.23 80.68 95.00
##
## lowest : -100.00000 -99.75000 -98.00000 -95.00000 -92.33333
## highest: 99.00000 99.25000 99.50000 99.75000 100.00000describe(PP$BRDiff.PBFB)## PP$BRDiff.PBFB
## n missing distinct Info Mean Gmd .05 .10
## 479 526 382 1 9.299 53.27 -83.367 -48.867
## .25 .50 .75 .90 .95
## -12.083 4.333 42.875 76.467 91.467
##
## lowest : -100.00000 -99.66667 -99.33333 -98.66667 -98.33333
## highest: 98.75000 99.00000 99.16667 99.75000 100.00000describe(PP$BRDiff.VB)## PP$BRDiff.VB
## n missing distinct Info Mean Gmd .05 .10
## 470 535 385 1 31.52 44.86 -21.758 -8.508
## .25 .50 .75 .90 .95
## 0.000 24.875 63.250 91.050 99.500
##
## lowest : -100.00000 -75.25000 -75.00000 -73.00000 -60.08333
## highest: 98.83333 99.25000 99.50000 99.66667 100.00000#Histograms
hist(PP$BRDiff.GFFB)
hist(PP$BRDiff.GFPRB)
hist(PP$BRDiff.CBB)
hist(PP$BRDiff.PBPB)
hist(PP$BRDiff.PBFB)
hist(PP$BRDiff.VB)
#SD
sd(PP$BRDiff.GFFB, na.rm = TRUE)## [1] 41.7335sd(PP$BRDiff.GFPRB, na.rm = TRUE)## [1] 40.47018sd(PP$BRDiff.CBB, na.rm = TRUE)## [1] 43.23648sd(PP$BRDiff.PBPB, na.rm = TRUE)## [1] 42.67171sd(PP$BRDiff.PBFB, na.rm = TRUE)## [1] 47.76765sd(PP$BRDiff.VB, na.rm = TRUE)## [1] 39.7248Combo Fam/Risk Mean Scores
#Combo Mean Score
PP$FR.GFFB <- rowMeans(PP [, c("Familiarity_GFFB", "Understanding_GFFB")], na.rm=TRUE)
PP$FR.GFPRB <- rowMeans(PP [, c("Familiarity_GFPRB", "Understanding_GFPRB")], na.rm=TRUE)
PP$FR.CBB <- rowMeans(PP [, c("Familiarity_CBB", "Understanding_CBB")], na.rm=TRUE)
PP$FR.PBPB <- rowMeans(PP [, c("Familiarity_PBPB", "Understanding_PBPB")], na.rm=TRUE)
PP$FR.PBFB <- rowMeans(PP [, c("Familiarity_PBFB", "Understanding_PBFB")], na.rm=TRUE)
PP$FR.VB <- rowMeans(PP [, c("Familiarity_VB", "Understanding_VB")], na.rm=TRUE)
#Descriptives
describe(PP$FR.GFFB)## PP$FR.GFFB
## n missing distinct Info Mean Gmd .05 .10
## 498 507 156 0.998 66.18 28.02 18.85 33.35
## .25 .50 .75 .90 .95
## 50.00 67.25 86.50 100.00 100.00
##
## lowest : 0.0 1.5 7.0 8.0 10.0, highest: 98.0 98.5 99.0 99.5 100.0describe(PP$FR.GFPRB)## PP$FR.GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 513 492 141 0.996 73.05 26.21 31.3 44.6
## .25 .50 .75 .90 .95
## 54.0 77.5 93.5 100.0 100.0
##
## lowest : 0.0 4.5 9.0 13.0 14.0, highest: 98.0 98.5 99.0 99.5 100.0describe(PP$FR.CBB)## PP$FR.CBB
## n missing distinct Info Mean Gmd .05 .10
## 516 489 175 1 52.09 32.26 1.375 11.500
## .25 .50 .75 .90 .95
## 33.875 51.750 73.125 93.500 100.000
##
## lowest : 0.0 0.5 1.0 1.5 2.5, highest: 98.0 98.5 99.0 99.5 100.0describe(PP$FR.PBPB)## PP$FR.PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 160 1 58.87 27.62 17.65 25.50
## .25 .50 .75 .90 .95
## 45.88 57.50 77.62 91.85 100.00
##
## lowest : 0.0 0.5 1.5 2.5 3.5, highest: 96.0 96.5 98.0 99.5 100.0describe(PP$FR.PBFB)## PP$FR.PBFB
## n missing distinct Info Mean Gmd .05 .10
## 481 524 165 1 52.95 32.09 0.5 10.5
## .25 .50 .75 .90 .95
## 33.5 53.0 76.0 90.0 95.5
##
## lowest : 0.0 0.5 1.0 1.5 3.0, highest: 95.0 95.5 96.5 99.0 100.0describe(PP$FR.VB)## PP$FR.VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 155 0.999 65.33 26.92 22.05 36.50
## .25 .50 .75 .90 .95
## 50.00 66.50 84.12 99.00 100.00
##
## lowest : 0.0 1.0 2.5 6.0 7.5, highest: 98.0 98.5 99.0 99.5 100.0#SD
sd(PP$FR.GFFB, na.rm = TRUE)## [1] 24.79544sd(PP$FR.GFPRB, na.rm = TRUE)## [1] 23.67885sd(PP$FR.CBB, na.rm = TRUE)## [1] 28.1976sd(PP$FR.PBPB, na.rm = TRUE)## [1] 24.35105sd(PP$FR.PBFB, na.rm = TRUE)## [1] 28.00067sd(PP$FR.VB, na.rm = TRUE)## [1] 23.84396#Histograms
hist(PP$FR.GFFB)
hist(PP$FR.GFPRB)
hist(PP$FR.CBB)
hist(PP$FR.PBPB)
hist(PP$FR.PBFB)
hist(PP$FR.VB)
#Scales
PP$FR_Scale_GFFB <- data.frame(PP$Familiarity_GFFB, PP$Understanding_GFFB)
PP$FR_Scale_GFPRB <- data.frame(PP$Familiarity_GFPRB, PP$Understanding_GFPRB)
PP$FR_Scale_CBB <- data.frame(PP$Familiarity_CBB, PP$Understanding_CBB)
PP$FR_Scale_PBPB <- data.frame(PP$Familiarity_PBPB, PP$Understanding_PBPB)
PP$FR_Scale_PBFB <- data.frame(PP$Familiarity_PBFB, PP$Understanding_PBFB)
PP$FR_Scale_VB <- data.frame(PP$Familiarity_VB, PP$Understanding_VB)
#Alphas
psych::alpha(PP$FR_Scale_GFFB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_GFFB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.66 0.5 0.5 2 0.021 66 25 0.5
##
## lower alpha upper 95% confidence boundaries
## 0.62 0.66 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_GFFB 0.56 0.5 0.25 0.5 0.98 NA 0
## PP.Understanding_GFFB 0.44 0.5 0.25 0.5 0.98 NA 0
## med.r
## PP.Familiarity_GFFB 0.5
## PP.Understanding_GFFB 0.5
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_GFFB 493 0.88 0.86 0.61 0.5 63 30
## PP.Understanding_GFFB 497 0.85 0.86 0.61 0.5 70 27psych::alpha(PP$FR_Scale_GFPRB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_GFPRB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.62 0.62 3.2 0.015 73 24 0.62
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_GFPRB 0.59 0.62 0.38 0.62 1.6 NA 0
## PP.Understanding_GFPRB 0.64 0.62 0.38 0.62 1.6 NA 0
## med.r
## PP.Familiarity_GFPRB 0.62
## PP.Understanding_GFPRB 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_GFPRB 511 0.9 0.9 0.71 0.62 73 26
## PP.Understanding_GFPRB 512 0.9 0.9 0.71 0.62 73 27psych::alpha(PP$FR_Scale_CBB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_CBB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.53 0.53 2.2 0.019 52 28 0.53
##
## lower alpha upper 95% confidence boundaries
## 0.65 0.69 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_CBB 0.57 0.53 0.28 0.53 1.1 NA 0
## PP.Understanding_CBB 0.49 0.53 0.28 0.53 1.1 NA 0
## med.r
## PP.Familiarity_CBB 0.53
## PP.Understanding_CBB 0.53
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_CBB 515 0.88 0.87 0.64 0.53 46 34
## PP.Understanding_CBB 515 0.86 0.87 0.64 0.53 58 31psych::alpha(PP$FR_Scale_PBPB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_PBPB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.57 0.57 0.4 0.4 1.3 0.027 59 24 0.4
##
## lower alpha upper 95% confidence boundaries
## 0.52 0.57 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_PBPB 0.43 0.4 0.16 0.4 0.66 NA 0
## PP.Understanding_PBPB 0.37 0.4 0.16 0.4 0.66 NA 0
## med.r
## PP.Familiarity_PBPB 0.4
## PP.Understanding_PBPB 0.4
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_PBPB 524 0.85 0.84 0.53 0.4 54 30
## PP.Understanding_PBPB 524 0.82 0.84 0.53 0.4 63 28psych::alpha(PP$FR_Scale_PBFB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_PBFB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.7 0.54 0.54 2.3 0.019 53 28 0.54
##
## lower alpha upper 95% confidence boundaries
## 0.66 0.7 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_PBFB 0.58 0.54 0.29 0.54 1.2 NA 0
## PP.Understanding_PBFB 0.51 0.54 0.29 0.54 1.2 NA 0
## med.r
## PP.Familiarity_PBFB 0.54
## PP.Understanding_PBFB 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_PBFB 481 0.89 0.88 0.64 0.54 48 33
## PP.Understanding_PBFB 480 0.87 0.88 0.64 0.54 58 31psych::alpha(PP$FR_Scale_VB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_VB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.6 0.43 0.43 1.5 0.025 65 24 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.6 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_VB 0.46 0.43 0.18 0.43 0.75 NA 0
## PP.Understanding_VB 0.40 0.43 0.18 0.43 0.75 NA 0
## med.r
## PP.Familiarity_VB 0.43
## PP.Understanding_VB 0.43
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_VB 472 0.86 0.85 0.55 0.43 62 29
## PP.Understanding_VB 471 0.83 0.85 0.55 0.43 69 27Correlations
Scale Correlations
#Individual Differences
PP$corID <- data.frame(PP$AW_Scale, PP$ATNS_Scale,PP$CCB_Scale, PP$CNS_Scale, PP$DS_Scale, PP$IndScale, PP$CollScale, PP$PI_Orientation, PP$PP_Party)
mydata.cor9 = cor(PP$corID, use = "pairwise.complete.obs")
head(round(mydata.cor9,2))## PP.AW_1 PP.AW_2 PP.ATNS_1 PP.ATNS_2R PP.ATNS_3 PP.ATNS_4 PP.ATNS_5
## PP.AW_1 1.00 0.69 0.14 0.00 0.19 0.33 0.21
## PP.AW_2 0.69 1.00 0.18 -0.02 0.26 0.33 0.21
## PP.ATNS_1 0.14 0.18 1.00 -0.10 0.43 0.36 0.43
## PP.ATNS_2R 0.00 -0.02 -0.10 1.00 0.01 0.02 0.01
## PP.ATNS_3 0.19 0.26 0.43 0.01 1.00 0.52 0.52
## PP.ATNS_4 0.33 0.33 0.36 0.02 0.52 1.00 0.48
## PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51 PP.CNS_1 PP.CNS_2 PP.CNS_3
## PP.AW_1 0.32 0.33 0.33 0.35 0.33 0.38 0.37
## PP.AW_2 0.38 0.38 0.39 0.41 0.38 0.42 0.42
## PP.ATNS_1 0.10 0.12 0.11 0.09 0.31 0.30 0.26
## PP.ATNS_2R 0.03 0.03 0.00 -0.03 -0.06 -0.01 0.01
## PP.ATNS_3 0.13 0.17 0.18 0.15 0.33 0.30 0.24
## PP.ATNS_4 0.28 0.26 0.31 0.24 0.35 0.37 0.35
## PP.DS_1D PP.DS_2R PP.DS_3D PP.Ind_1 PP.Ind_2 PP.Ind_5 PP.Ind_6
## PP.AW_1 0.11 -0.12 0.14 0.30 0.26 0.27 0.23
## PP.AW_2 0.12 -0.08 0.13 0.34 0.29 0.33 0.27
## PP.ATNS_1 0.11 -0.12 0.19 0.18 0.17 0.18 0.22
## PP.ATNS_2R -0.05 0.21 -0.13 0.07 0.07 0.02 0.03
## PP.ATNS_3 0.16 -0.06 0.24 0.20 0.22 0.21 0.23
## PP.ATNS_4 0.17 -0.08 0.16 0.25 0.21 0.24 0.20
## PP.Ind_3 PP.Ind_4 PP.Ind_7 PP.Ind_8 PP.PI_Orientation PP.PP_Party
## PP.AW_1 0.14 0.26 0.20 0.22 0.07 0.03
## PP.AW_2 0.13 0.20 0.17 0.22 0.15 0.08
## PP.ATNS_1 0.22 0.23 0.23 0.21 -0.08 -0.07
## PP.ATNS_2R -0.13 0.00 -0.14 -0.03 0.00 0.03
## PP.ATNS_3 0.24 0.23 0.22 0.23 -0.01 -0.06
## PP.ATNS_4 0.15 0.21 0.20 0.18 0.07 0.00library("Hmisc")
mydata.rcorr9 = rcorr(as.matrix(mydata.cor9))
mydata.rcorr9## PP.AW_1 PP.AW_2 PP.ATNS_1 PP.ATNS_2R PP.ATNS_3 PP.ATNS_4
## PP.AW_1 1.00 0.91 0.13 -0.26 0.18 0.42
## PP.AW_2 0.91 1.00 0.14 -0.29 0.19 0.42
## PP.ATNS_1 0.13 0.14 1.00 -0.40 0.70 0.57
## PP.ATNS_2R -0.26 -0.29 -0.40 1.00 -0.28 -0.26
## PP.ATNS_3 0.18 0.19 0.70 -0.28 1.00 0.73
## PP.ATNS_4 0.42 0.42 0.57 -0.26 0.73 1.00
## PP.ATNS_5 0.20 0.18 0.69 -0.27 0.78 0.71
## PP.CCB_48 0.43 0.51 -0.13 -0.15 -0.10 0.20
## PP.CCB_49 0.44 0.53 -0.09 -0.15 -0.06 0.21
## PP.CCB_50 0.46 0.55 -0.08 -0.19 -0.03 0.27
## PP.CCB_51 0.50 0.59 -0.08 -0.23 -0.04 0.23
## PP.CNS_1 0.53 0.56 0.47 -0.37 0.45 0.53
## PP.CNS_2 0.58 0.61 0.42 -0.31 0.37 0.52
## PP.CNS_3 0.59 0.63 0.32 -0.28 0.27 0.47
## PP.DS_1D -0.09 -0.13 0.05 -0.26 0.08 0.02
## PP.DS_2R -0.50 -0.49 -0.44 0.41 -0.40 -0.48
## PP.DS_3D 0.02 -0.02 0.30 -0.46 0.31 0.13
## PP.Ind_1 0.39 0.41 0.16 -0.14 0.13 0.20
## PP.Ind_2 0.35 0.36 0.19 -0.12 0.18 0.20
## PP.Ind_5 0.37 0.40 0.19 -0.23 0.16 0.21
## PP.Ind_6 0.26 0.27 0.27 -0.22 0.21 0.16
## PP.Ind_3 0.04 -0.03 0.38 -0.46 0.31 0.11
## PP.Ind_4 0.23 0.13 0.35 -0.28 0.28 0.17
## PP.Ind_7 0.16 0.08 0.39 -0.50 0.31 0.19
## PP.Ind_8 0.21 0.15 0.32 -0.32 0.26 0.14
## PP.PI_Orientation 0.04 0.16 -0.41 -0.02 -0.34 -0.11
## PP.PP_Party -0.14 -0.07 -0.41 0.05 -0.41 -0.27
## PP.ATNS_5 PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51 PP.CNS_1
## PP.AW_1 0.20 0.43 0.44 0.46 0.50 0.53
## PP.AW_2 0.18 0.51 0.53 0.55 0.59 0.56
## PP.ATNS_1 0.69 -0.13 -0.09 -0.08 -0.08 0.47
## PP.ATNS_2R -0.27 -0.15 -0.15 -0.19 -0.23 -0.37
## PP.ATNS_3 0.78 -0.10 -0.06 -0.03 -0.04 0.45
## PP.ATNS_4 0.71 0.20 0.21 0.27 0.23 0.53
## PP.ATNS_5 1.00 0.02 0.06 0.07 0.07 0.44
## PP.CCB_48 0.02 1.00 0.97 0.96 0.91 0.30
## PP.CCB_49 0.06 0.97 1.00 0.94 0.93 0.33
## PP.CCB_50 0.07 0.96 0.94 1.00 0.91 0.37
## PP.CCB_51 0.07 0.91 0.93 0.91 1.00 0.38
## PP.CNS_1 0.44 0.30 0.33 0.37 0.38 1.00
## PP.CNS_2 0.40 0.40 0.44 0.43 0.46 0.83
## PP.CNS_3 0.32 0.49 0.52 0.54 0.55 0.80
## PP.DS_1D 0.03 -0.23 -0.27 -0.24 -0.22 -0.19
## PP.DS_2R -0.42 -0.34 -0.37 -0.40 -0.39 -0.64
## PP.DS_3D 0.22 -0.23 -0.23 -0.22 -0.15 0.12
## PP.Ind_1 0.16 0.26 0.28 0.27 0.26 0.49
## PP.Ind_2 0.17 0.17 0.20 0.18 0.16 0.48
## PP.Ind_5 0.16 0.23 0.25 0.26 0.23 0.49
## PP.Ind_6 0.20 0.09 0.10 0.09 0.09 0.41
## PP.Ind_3 0.27 -0.32 -0.31 -0.27 -0.24 0.28
## PP.Ind_4 0.31 -0.08 -0.07 -0.06 -0.04 0.43
## PP.Ind_7 0.33 -0.16 -0.15 -0.12 -0.10 0.31
## PP.Ind_8 0.25 -0.05 -0.04 -0.02 -0.03 0.35
## PP.PI_Orientation -0.33 0.53 0.52 0.51 0.48 -0.11
## PP.PP_Party -0.38 0.20 0.18 0.17 0.12 -0.34
## PP.CNS_2 PP.CNS_3 PP.DS_1D PP.DS_2R PP.DS_3D PP.Ind_1
## PP.AW_1 0.58 0.59 -0.09 -0.50 0.02 0.39
## PP.AW_2 0.61 0.63 -0.13 -0.49 -0.02 0.41
## PP.ATNS_1 0.42 0.32 0.05 -0.44 0.30 0.16
## PP.ATNS_2R -0.31 -0.28 -0.26 0.41 -0.46 -0.14
## PP.ATNS_3 0.37 0.27 0.08 -0.40 0.31 0.13
## PP.ATNS_4 0.52 0.47 0.02 -0.48 0.13 0.20
## PP.ATNS_5 0.40 0.32 0.03 -0.42 0.22 0.16
## PP.CCB_48 0.40 0.49 -0.23 -0.34 -0.23 0.26
## PP.CCB_49 0.44 0.52 -0.27 -0.37 -0.23 0.28
## PP.CCB_50 0.43 0.54 -0.24 -0.40 -0.22 0.27
## PP.CCB_51 0.46 0.55 -0.22 -0.39 -0.15 0.26
## PP.CNS_1 0.83 0.80 -0.19 -0.64 0.12 0.49
## PP.CNS_2 1.00 0.84 -0.19 -0.59 0.01 0.49
## PP.CNS_3 0.84 1.00 -0.20 -0.57 0.02 0.56
## PP.DS_1D -0.19 -0.20 1.00 0.10 0.39 -0.06
## PP.DS_2R -0.59 -0.57 0.10 1.00 -0.25 -0.36
## PP.DS_3D 0.01 0.02 0.39 -0.25 1.00 -0.03
## PP.Ind_1 0.49 0.56 -0.06 -0.36 -0.03 1.00
## PP.Ind_2 0.47 0.51 -0.10 -0.39 0.03 0.81
## PP.Ind_5 0.50 0.51 -0.07 -0.40 0.04 0.83
## PP.Ind_6 0.42 0.42 0.05 -0.32 0.13 0.70
## PP.Ind_3 0.18 0.12 0.23 -0.30 0.47 0.21
## PP.Ind_4 0.41 0.36 0.08 -0.26 0.21 0.55
## PP.Ind_7 0.29 0.21 0.18 -0.40 0.37 0.32
## PP.Ind_8 0.38 0.34 0.10 -0.32 0.24 0.53
## PP.PI_Orientation -0.03 0.05 -0.23 -0.11 -0.37 -0.16
## PP.PP_Party -0.32 -0.28 -0.27 0.02 -0.32 -0.32
## PP.Ind_2 PP.Ind_5 PP.Ind_6 PP.Ind_3 PP.Ind_4 PP.Ind_7
## PP.AW_1 0.35 0.37 0.26 0.04 0.23 0.16
## PP.AW_2 0.36 0.40 0.27 -0.03 0.13 0.08
## PP.ATNS_1 0.19 0.19 0.27 0.38 0.35 0.39
## PP.ATNS_2R -0.12 -0.23 -0.22 -0.46 -0.28 -0.50
## PP.ATNS_3 0.18 0.16 0.21 0.31 0.28 0.31
## PP.ATNS_4 0.20 0.21 0.16 0.11 0.17 0.19
## PP.ATNS_5 0.17 0.16 0.20 0.27 0.31 0.33
## PP.CCB_48 0.17 0.23 0.09 -0.32 -0.08 -0.16
## PP.CCB_49 0.20 0.25 0.10 -0.31 -0.07 -0.15
## PP.CCB_50 0.18 0.26 0.09 -0.27 -0.06 -0.12
## PP.CCB_51 0.16 0.23 0.09 -0.24 -0.04 -0.10
## PP.CNS_1 0.48 0.49 0.41 0.28 0.43 0.31
## PP.CNS_2 0.47 0.50 0.42 0.18 0.41 0.29
## PP.CNS_3 0.51 0.51 0.42 0.12 0.36 0.21
## PP.DS_1D -0.10 -0.07 0.05 0.23 0.08 0.18
## PP.DS_2R -0.39 -0.40 -0.32 -0.30 -0.26 -0.40
## PP.DS_3D 0.03 0.04 0.13 0.47 0.21 0.37
## PP.Ind_1 0.81 0.83 0.70 0.21 0.55 0.32
## PP.Ind_2 1.00 0.78 0.66 0.16 0.45 0.28
## PP.Ind_5 0.78 1.00 0.66 0.23 0.49 0.34
## PP.Ind_6 0.66 0.66 1.00 0.38 0.60 0.46
## PP.Ind_3 0.16 0.23 0.38 1.00 0.68 0.84
## PP.Ind_4 0.45 0.49 0.60 0.68 1.00 0.63
## PP.Ind_7 0.28 0.34 0.46 0.84 0.63 1.00
## PP.Ind_8 0.47 0.53 0.62 0.62 0.81 0.62
## PP.PI_Orientation -0.14 -0.12 -0.28 -0.56 -0.52 -0.47
## PP.PP_Party -0.36 -0.30 -0.42 -0.48 -0.57 -0.44
## PP.Ind_8 PP.PI_Orientation PP.PP_Party
## PP.AW_1 0.21 0.04 -0.14
## PP.AW_2 0.15 0.16 -0.07
## PP.ATNS_1 0.32 -0.41 -0.41
## PP.ATNS_2R -0.32 -0.02 0.05
## PP.ATNS_3 0.26 -0.34 -0.41
## PP.ATNS_4 0.14 -0.11 -0.27
## PP.ATNS_5 0.25 -0.33 -0.38
## PP.CCB_48 -0.05 0.53 0.20
## PP.CCB_49 -0.04 0.52 0.18
## PP.CCB_50 -0.02 0.51 0.17
## PP.CCB_51 -0.03 0.48 0.12
## PP.CNS_1 0.35 -0.11 -0.34
## PP.CNS_2 0.38 -0.03 -0.32
## PP.CNS_3 0.34 0.05 -0.28
## PP.DS_1D 0.10 -0.23 -0.27
## PP.DS_2R -0.32 -0.11 0.02
## PP.DS_3D 0.24 -0.37 -0.32
## PP.Ind_1 0.53 -0.16 -0.32
## PP.Ind_2 0.47 -0.14 -0.36
## PP.Ind_5 0.53 -0.12 -0.30
## PP.Ind_6 0.62 -0.28 -0.42
## PP.Ind_3 0.62 -0.56 -0.48
## PP.Ind_4 0.81 -0.52 -0.57
## PP.Ind_7 0.62 -0.47 -0.44
## PP.Ind_8 1.00 -0.45 -0.49
## PP.PI_Orientation -0.45 1.00 0.42
## PP.PP_Party -0.49 0.42 1.00
##
## n= 27
##
##
## P
## PP.AW_1 PP.AW_2 PP.ATNS_1 PP.ATNS_2R PP.ATNS_3 PP.ATNS_4
## PP.AW_1 0.0000 0.5131 0.1849 0.3807 0.0284
## PP.AW_2 0.0000 0.4969 0.1382 0.3392 0.0300
## PP.ATNS_1 0.5131 0.4969 0.0381 0.0000 0.0020
## PP.ATNS_2R 0.1849 0.1382 0.0381 0.1635 0.1928
## PP.ATNS_3 0.3807 0.3392 0.0000 0.1635 0.0000
## PP.ATNS_4 0.0284 0.0300 0.0020 0.1928 0.0000
## PP.ATNS_5 0.3082 0.3646 0.0000 0.1769 0.0000 0.0000
## PP.CCB_48 0.0267 0.0061 0.5160 0.4619 0.6069 0.3128
## PP.CCB_49 0.0206 0.0046 0.6450 0.4409 0.7698 0.2843
## PP.CCB_50 0.0159 0.0030 0.6814 0.3398 0.8772 0.1801
## PP.CCB_51 0.0076 0.0013 0.6881 0.2482 0.8549 0.2551
## PP.CNS_1 0.0042 0.0022 0.0125 0.0604 0.0183 0.0044
## PP.CNS_2 0.0014 0.0007 0.0300 0.1162 0.0553 0.0050
## PP.CNS_3 0.0013 0.0004 0.0983 0.1549 0.1694 0.0130
## PP.DS_1D 0.6389 0.5143 0.7897 0.1867 0.6796 0.9138
## PP.DS_2R 0.0084 0.0092 0.0203 0.0345 0.0413 0.0119
## PP.DS_3D 0.9101 0.9098 0.1309 0.0156 0.1117 0.5103
## PP.Ind_1 0.0437 0.0328 0.4128 0.4778 0.5090 0.3086
## PP.Ind_2 0.0767 0.0639 0.3332 0.5541 0.3569 0.3209
## PP.Ind_5 0.0606 0.0387 0.3480 0.2582 0.4179 0.2982
## PP.Ind_6 0.1842 0.1763 0.1776 0.2602 0.2851 0.4354
## PP.Ind_3 0.8304 0.8689 0.0528 0.0154 0.1115 0.5838
## PP.Ind_4 0.2427 0.5037 0.0729 0.1524 0.1631 0.3917
## PP.Ind_7 0.4324 0.7079 0.0440 0.0083 0.1179 0.3483
## PP.Ind_8 0.2856 0.4434 0.1065 0.1016 0.1898 0.4813
## PP.PI_Orientation 0.8438 0.4395 0.0347 0.9090 0.0810 0.5794
## PP.PP_Party 0.5007 0.7390 0.0326 0.7909 0.0333 0.1690
## PP.ATNS_5 PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51 PP.CNS_1
## PP.AW_1 0.3082 0.0267 0.0206 0.0159 0.0076 0.0042
## PP.AW_2 0.3646 0.0061 0.0046 0.0030 0.0013 0.0022
## PP.ATNS_1 0.0000 0.5160 0.6450 0.6814 0.6881 0.0125
## PP.ATNS_2R 0.1769 0.4619 0.4409 0.3398 0.2482 0.0604
## PP.ATNS_3 0.0000 0.6069 0.7698 0.8772 0.8549 0.0183
## PP.ATNS_4 0.0000 0.3128 0.2843 0.1801 0.2551 0.0044
## PP.ATNS_5 0.9318 0.7807 0.7446 0.7166 0.0228
## PP.CCB_48 0.9318 0.0000 0.0000 0.0000 0.1295
## PP.CCB_49 0.7807 0.0000 0.0000 0.0000 0.0898
## PP.CCB_50 0.7446 0.0000 0.0000 0.0000 0.0590
## PP.CCB_51 0.7166 0.0000 0.0000 0.0000 0.0515
## PP.CNS_1 0.0228 0.1295 0.0898 0.0590 0.0515
## PP.CNS_2 0.0406 0.0381 0.0229 0.0237 0.0169 0.0000
## PP.CNS_3 0.1006 0.0094 0.0051 0.0035 0.0031 0.0000
## PP.DS_1D 0.8976 0.2530 0.1749 0.2344 0.2685 0.3497
## PP.DS_2R 0.0280 0.0786 0.0586 0.0394 0.0423 0.0003
## PP.DS_3D 0.2654 0.2397 0.2397 0.2687 0.4427 0.5641
## PP.Ind_1 0.4221 0.1922 0.1586 0.1696 0.1925 0.0092
## PP.Ind_2 0.4031 0.3901 0.3213 0.3618 0.4210 0.0107
## PP.Ind_5 0.4147 0.2435 0.2152 0.1950 0.2509 0.0092
## PP.Ind_6 0.3167 0.6701 0.6064 0.6384 0.6404 0.0348
## PP.Ind_3 0.1682 0.0986 0.1097 0.1726 0.2312 0.1564
## PP.Ind_4 0.1181 0.6857 0.7109 0.7772 0.8251 0.0244
## PP.Ind_7 0.0887 0.4209 0.4462 0.5376 0.6156 0.1210
## PP.Ind_8 0.2156 0.8023 0.8246 0.9121 0.8801 0.0728
## PP.PI_Orientation 0.0963 0.0044 0.0058 0.0060 0.0113 0.5736
## PP.PP_Party 0.0533 0.3110 0.3598 0.3860 0.5554 0.0862
## PP.CNS_2 PP.CNS_3 PP.DS_1D PP.DS_2R PP.DS_3D PP.Ind_1
## PP.AW_1 0.0014 0.0013 0.6389 0.0084 0.9101 0.0437
## PP.AW_2 0.0007 0.0004 0.5143 0.0092 0.9098 0.0328
## PP.ATNS_1 0.0300 0.0983 0.7897 0.0203 0.1309 0.4128
## PP.ATNS_2R 0.1162 0.1549 0.1867 0.0345 0.0156 0.4778
## PP.ATNS_3 0.0553 0.1694 0.6796 0.0413 0.1117 0.5090
## PP.ATNS_4 0.0050 0.0130 0.9138 0.0119 0.5103 0.3086
## PP.ATNS_5 0.0406 0.1006 0.8976 0.0280 0.2654 0.4221
## PP.CCB_48 0.0381 0.0094 0.2530 0.0786 0.2397 0.1922
## PP.CCB_49 0.0229 0.0051 0.1749 0.0586 0.2397 0.1586
## PP.CCB_50 0.0237 0.0035 0.2344 0.0394 0.2687 0.1696
## PP.CCB_51 0.0169 0.0031 0.2685 0.0423 0.4427 0.1925
## PP.CNS_1 0.0000 0.0000 0.3497 0.0003 0.5641 0.0092
## PP.CNS_2 0.0000 0.3359 0.0011 0.9684 0.0096
## PP.CNS_3 0.0000 0.3206 0.0018 0.9232 0.0024
## PP.DS_1D 0.3359 0.3206 0.6111 0.0452 0.7833
## PP.DS_2R 0.0011 0.0018 0.6111 0.2126 0.0673
## PP.DS_3D 0.9684 0.9232 0.0452 0.2126 0.8926
## PP.Ind_1 0.0096 0.0024 0.7833 0.0673 0.8926
## PP.Ind_2 0.0136 0.0070 0.6214 0.0455 0.8713 0.0000
## PP.Ind_5 0.0076 0.0060 0.7164 0.0406 0.8499 0.0000
## PP.Ind_6 0.0282 0.0303 0.7967 0.0982 0.5121 0.0000
## PP.Ind_3 0.3618 0.5460 0.2552 0.1245 0.0133 0.2871
## PP.Ind_4 0.0347 0.0652 0.7005 0.1933 0.2890 0.0032
## PP.Ind_7 0.1443 0.2970 0.3760 0.0394 0.0584 0.1018
## PP.Ind_8 0.0531 0.0848 0.6372 0.1005 0.2258 0.0046
## PP.PI_Orientation 0.8902 0.8181 0.2410 0.5687 0.0546 0.4148
## PP.PP_Party 0.0987 0.1520 0.1753 0.9202 0.1088 0.0990
## PP.Ind_2 PP.Ind_5 PP.Ind_6 PP.Ind_3 PP.Ind_4 PP.Ind_7
## PP.AW_1 0.0767 0.0606 0.1842 0.8304 0.2427 0.4324
## PP.AW_2 0.0639 0.0387 0.1763 0.8689 0.5037 0.7079
## PP.ATNS_1 0.3332 0.3480 0.1776 0.0528 0.0729 0.0440
## PP.ATNS_2R 0.5541 0.2582 0.2602 0.0154 0.1524 0.0083
## PP.ATNS_3 0.3569 0.4179 0.2851 0.1115 0.1631 0.1179
## PP.ATNS_4 0.3209 0.2982 0.4354 0.5838 0.3917 0.3483
## PP.ATNS_5 0.4031 0.4147 0.3167 0.1682 0.1181 0.0887
## PP.CCB_48 0.3901 0.2435 0.6701 0.0986 0.6857 0.4209
## PP.CCB_49 0.3213 0.2152 0.6064 0.1097 0.7109 0.4462
## PP.CCB_50 0.3618 0.1950 0.6384 0.1726 0.7772 0.5376
## PP.CCB_51 0.4210 0.2509 0.6404 0.2312 0.8251 0.6156
## PP.CNS_1 0.0107 0.0092 0.0348 0.1564 0.0244 0.1210
## PP.CNS_2 0.0136 0.0076 0.0282 0.3618 0.0347 0.1443
## PP.CNS_3 0.0070 0.0060 0.0303 0.5460 0.0652 0.2970
## PP.DS_1D 0.6214 0.7164 0.7967 0.2552 0.7005 0.3760
## PP.DS_2R 0.0455 0.0406 0.0982 0.1245 0.1933 0.0394
## PP.DS_3D 0.8713 0.8499 0.5121 0.0133 0.2890 0.0584
## PP.Ind_1 0.0000 0.0000 0.0000 0.2871 0.0032 0.1018
## PP.Ind_2 0.0000 0.0002 0.4251 0.0196 0.1552
## PP.Ind_5 0.0000 0.0002 0.2383 0.0095 0.0817
## PP.Ind_6 0.0002 0.0002 0.0537 0.0008 0.0162
## PP.Ind_3 0.4251 0.2383 0.0537 0.0000 0.0000
## PP.Ind_4 0.0196 0.0095 0.0008 0.0000 0.0004
## PP.Ind_7 0.1552 0.0817 0.0162 0.0000 0.0004
## PP.Ind_8 0.0144 0.0047 0.0006 0.0005 0.0000 0.0006
## PP.PI_Orientation 0.4796 0.5375 0.1616 0.0023 0.0058 0.0128
## PP.PP_Party 0.0681 0.1344 0.0287 0.0114 0.0019 0.0204
## PP.Ind_8 PP.PI_Orientation PP.PP_Party
## PP.AW_1 0.2856 0.8438 0.5007
## PP.AW_2 0.4434 0.4395 0.7390
## PP.ATNS_1 0.1065 0.0347 0.0326
## PP.ATNS_2R 0.1016 0.9090 0.7909
## PP.ATNS_3 0.1898 0.0810 0.0333
## PP.ATNS_4 0.4813 0.5794 0.1690
## PP.ATNS_5 0.2156 0.0963 0.0533
## PP.CCB_48 0.8023 0.0044 0.3110
## PP.CCB_49 0.8246 0.0058 0.3598
## PP.CCB_50 0.9121 0.0060 0.3860
## PP.CCB_51 0.8801 0.0113 0.5554
## PP.CNS_1 0.0728 0.5736 0.0862
## PP.CNS_2 0.0531 0.8902 0.0987
## PP.CNS_3 0.0848 0.8181 0.1520
## PP.DS_1D 0.6372 0.2410 0.1753
## PP.DS_2R 0.1005 0.5687 0.9202
## PP.DS_3D 0.2258 0.0546 0.1088
## PP.Ind_1 0.0046 0.4148 0.0990
## PP.Ind_2 0.0144 0.4796 0.0681
## PP.Ind_5 0.0047 0.5375 0.1344
## PP.Ind_6 0.0006 0.1616 0.0287
## PP.Ind_3 0.0005 0.0023 0.0114
## PP.Ind_4 0.0000 0.0058 0.0019
## PP.Ind_7 0.0006 0.0128 0.0204
## PP.Ind_8 0.0190 0.0092
## PP.PI_Orientation 0.0190 0.0302
## PP.PP_Party 0.0092 0.0302library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor9, method="color")
corrplot(mydata.cor9, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Environmental Measures
PP$corID <- data.frame(PP$AW_Score, PP$ATNS_Score, PP$CCBelief_Score, PP$CNS_Score, PP$Ideology)
mydata.corID = cor(PP$corID, use = "pairwise.complete.obs")
head(round(mydata.corID,2))## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 1.00 0.30 0.44 0.31
## PP.ATNS_Score 0.30 1.00 0.26 0.29
## PP.CCBelief_Score 0.44 0.26 1.00 0.38
## PP.CNS_Score 0.31 0.29 0.38 1.00
## PP.Ideology 0.00 0.03 -0.04 0.05
## PP.Ideology
## PP.AW_Score 0.00
## PP.ATNS_Score 0.03
## PP.CCBelief_Score -0.04
## PP.CNS_Score 0.05
## PP.Ideology 1.00library("Hmisc")
mydata.rcorrID = rcorr(as.matrix(mydata.corID))
mydata.rcorrID## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 1.00 0.06 0.44 0.07
## PP.ATNS_Score 0.06 1.00 -0.02 0.01
## PP.CCBelief_Score 0.44 -0.02 1.00 0.26
## PP.CNS_Score 0.07 0.01 0.26 1.00
## PP.Ideology -0.65 -0.50 -0.71 -0.52
## PP.Ideology
## PP.AW_Score -0.65
## PP.ATNS_Score -0.50
## PP.CCBelief_Score -0.71
## PP.CNS_Score -0.52
## PP.Ideology 1.00
##
## n= 5
##
##
## P
## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 0.9214 0.4595 0.9075
## PP.ATNS_Score 0.9214 0.9780 0.9871
## PP.CCBelief_Score 0.4595 0.9780 0.6785
## PP.CNS_Score 0.9075 0.9871 0.6785
## PP.Ideology 0.2382 0.3930 0.1820 0.3726
## PP.Ideology
## PP.AW_Score 0.2382
## PP.ATNS_Score 0.3930
## PP.CCBelief_Score 0.1820
## PP.CNS_Score 0.3726
## PP.Ideologylibrary(corrplot)
corrplot(mydata.corID, method="color")
corrplot(mydata.corID, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Naturalness Scales (TOTAL SCALE)
PP$corNScales <- data.frame(PP$Naturalness_Scale_GFFB_Tot, PP$Naturalness_Scale_GFPRB_Tot, PP$Naturalness_Scale_CBB_Tot, PP$Naturalness_Scale_PBPB_Tot, PP$Naturalness_Scale_PBFB_Tot, PP$Naturalness_Scale_VB_Tot)
mydata.cor5 = cor(PP$corNScales, use = "pairwise.complete.obs")
head(round(mydata.cor5,2))## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 0.18 0.18 -0.15
## PP.Nat_4R_GFFB 0.18 1.00 0.61 0.50
## PP.Nat_2R_GFFB 0.18 0.61 1.00 0.44
## PP.Nat_3R_GFFB -0.15 0.50 0.44 1.00
## PP.Nat_1_GFPRB 0.42 0.15 0.07 0.01
## PP.Nat_4R_GFPRB 0.04 0.47 0.21 0.33
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB
## PP.Nat_1_GFFB 0.42 0.04 -0.03 -0.04
## PP.Nat_4R_GFFB 0.15 0.47 0.49 0.38
## PP.Nat_2R_GFFB 0.07 0.21 0.29 0.17
## PP.Nat_3R_GFFB 0.01 0.33 0.34 0.49
## PP.Nat_1_GFPRB 1.00 0.38 0.25 0.14
## PP.Nat_4R_GFPRB 0.38 1.00 0.68 0.52
## PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB
## PP.Nat_1_GFFB 0.35 -0.01 0.04 0.00
## PP.Nat_4R_GFFB -0.36 0.20 0.14 0.05
## PP.Nat_2R_GFFB -0.32 0.13 0.22 0.21
## PP.Nat_3R_GFFB -0.41 0.08 0.07 0.01
## PP.Nat_1_GFPRB -0.10 -0.05 -0.13 -0.13
## PP.Nat_4R_GFPRB -0.34 0.11 -0.06 -0.06
## PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB
## PP.Nat_1_GFFB 0.14 -0.21 -0.26 -0.17
## PP.Nat_4R_GFFB -0.23 0.08 0.00 -0.04
## PP.Nat_2R_GFFB -0.27 0.15 0.04 0.06
## PP.Nat_3R_GFFB -0.36 0.09 0.14 0.10
## PP.Nat_1_GFPRB -0.04 0.03 0.05 -0.33
## PP.Nat_4R_GFPRB -0.23 0.20 0.11 -0.12
## PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB
## PP.Nat_1_GFFB 0.17 0.24 0.28 0.29
## PP.Nat_4R_GFFB -0.35 -0.07 0.03 0.05
## PP.Nat_2R_GFFB -0.33 0.05 -0.11 -0.07
## PP.Nat_3R_GFFB -0.37 -0.11 -0.11 -0.15
## PP.Nat_1_GFPRB -0.06 0.23 0.20 0.28
## PP.Nat_4R_GFPRB -0.35 -0.01 -0.04 0.05
## PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Nat_1_GFFB 0.09 -0.21 -0.22 -0.24
## PP.Nat_4R_GFFB -0.13 0.25 0.13 0.05
## PP.Nat_2R_GFFB -0.09 0.15 0.12 0.12
## PP.Nat_3R_GFFB -0.04 0.27 0.22 0.20
## PP.Nat_1_GFPRB 0.09 0.02 0.07 0.06
## PP.Nat_4R_GFPRB -0.11 0.32 0.39 0.25library("Hmisc")
mydata.rcorr5 = rcorr(as.matrix(mydata.cor5))
mydata.rcorr5## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 -0.08 -0.10 -0.45
## PP.Nat_4R_GFFB -0.08 1.00 0.86 0.81
## PP.Nat_2R_GFFB -0.10 0.86 1.00 0.76
## PP.Nat_3R_GFFB -0.45 0.81 0.76 1.00
## PP.Nat_1_GFPRB 0.50 0.24 0.08 0.02
## PP.Nat_4R_GFPRB -0.24 0.76 0.54 0.70
## PP.Nat_2R_GFPRB -0.21 0.79 0.58 0.72
## PP.Nat_3R_GFPRB -0.30 0.74 0.55 0.81
## PP.Nat_1_CBB 0.47 -0.71 -0.64 -0.81
## PP.Nat_4R_CBB -0.21 0.08 0.15 0.03
## PP.Nat_2R_CBB -0.17 0.03 0.21 0.03
## PP.Nat_3R_CBB -0.18 0.01 0.21 0.02
## PP.Nat_1_PBPB 0.14 -0.70 -0.69 -0.72
## PP.Nat_4R_PBPB -0.65 0.01 0.08 0.17
## PP.Nat_2R_PBPB -0.69 -0.05 0.07 0.20
## PP.Nat_3R_PBPB -0.63 -0.05 0.14 0.23
## PP.Nat_1_PBFB 0.21 -0.78 -0.72 -0.77
## PP.Nat_4R_PBFB 0.51 0.03 0.01 -0.14
## PP.Nat_2R_PBFB 0.61 -0.06 -0.22 -0.29
## PP.Nat_3R_PBFB 0.63 -0.03 -0.20 -0.32
## PP.Nat_1_VB 0.03 -0.50 -0.49 -0.39
## PP.Nat_4R_VB -0.68 0.30 0.24 0.50
## PP.Nat_2R_VB -0.68 0.33 0.28 0.55
## PP.Nat_3R_VB -0.66 0.27 0.29 0.54
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB
## PP.Nat_1_GFFB 0.50 -0.24 -0.21 -0.30
## PP.Nat_4R_GFFB 0.24 0.76 0.79 0.74
## PP.Nat_2R_GFFB 0.08 0.54 0.58 0.55
## PP.Nat_3R_GFFB 0.02 0.70 0.72 0.81
## PP.Nat_1_GFPRB 1.00 0.47 0.42 0.25
## PP.Nat_4R_GFPRB 0.47 1.00 0.94 0.85
## PP.Nat_2R_GFPRB 0.42 0.94 1.00 0.86
## PP.Nat_3R_GFPRB 0.25 0.85 0.86 1.00
## PP.Nat_1_CBB -0.23 -0.77 -0.78 -0.81
## PP.Nat_4R_CBB -0.41 -0.10 -0.18 -0.16
## PP.Nat_2R_CBB -0.49 -0.23 -0.27 -0.20
## PP.Nat_3R_CBB -0.48 -0.22 -0.26 -0.14
## PP.Nat_1_PBPB -0.24 -0.66 -0.72 -0.74
## PP.Nat_4R_PBPB -0.32 0.12 -0.04 -0.05
## PP.Nat_2R_PBPB -0.37 0.05 -0.09 -0.03
## PP.Nat_3R_PBPB -0.62 -0.09 -0.18 0.02
## PP.Nat_1_PBFB -0.28 -0.76 -0.81 -0.78
## PP.Nat_4R_PBFB 0.52 0.04 0.16 0.07
## PP.Nat_2R_PBFB 0.50 -0.07 0.05 -0.06
## PP.Nat_3R_PBFB 0.57 -0.02 0.07 -0.09
## PP.Nat_1_VB -0.04 -0.38 -0.43 -0.47
## PP.Nat_4R_VB -0.13 0.48 0.38 0.40
## PP.Nat_2R_VB -0.03 0.60 0.53 0.54
## PP.Nat_3R_VB -0.09 0.50 0.47 0.51
## PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB
## PP.Nat_1_GFFB 0.47 -0.21 -0.17 -0.18
## PP.Nat_4R_GFFB -0.71 0.08 0.03 0.01
## PP.Nat_2R_GFFB -0.64 0.15 0.21 0.21
## PP.Nat_3R_GFFB -0.81 0.03 0.03 0.02
## PP.Nat_1_GFPRB -0.23 -0.41 -0.49 -0.48
## PP.Nat_4R_GFPRB -0.77 -0.10 -0.23 -0.22
## PP.Nat_2R_GFPRB -0.78 -0.18 -0.27 -0.26
## PP.Nat_3R_GFPRB -0.81 -0.16 -0.20 -0.14
## PP.Nat_1_CBB 1.00 0.29 0.28 0.22
## PP.Nat_4R_CBB 0.29 1.00 0.89 0.81
## PP.Nat_2R_CBB 0.28 0.89 1.00 0.92
## PP.Nat_3R_CBB 0.22 0.81 0.92 1.00
## PP.Nat_1_PBPB 0.71 0.08 0.03 -0.02
## PP.Nat_4R_PBPB -0.18 0.30 0.21 0.14
## PP.Nat_2R_PBPB -0.19 0.39 0.45 0.41
## PP.Nat_3R_PBPB -0.17 0.30 0.41 0.41
## PP.Nat_1_PBFB 0.83 0.19 0.16 0.14
## PP.Nat_4R_PBFB -0.13 -0.58 -0.50 -0.44
## PP.Nat_2R_PBFB 0.08 -0.64 -0.65 -0.63
## PP.Nat_3R_PBFB 0.09 -0.49 -0.56 -0.53
## PP.Nat_1_VB 0.34 -0.24 -0.31 -0.34
## PP.Nat_4R_VB -0.54 0.03 -0.11 -0.13
## PP.Nat_2R_VB -0.68 -0.04 -0.12 -0.13
## PP.Nat_3R_VB -0.61 -0.08 -0.12 -0.13
## PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB
## PP.Nat_1_GFFB 0.14 -0.65 -0.69 -0.63
## PP.Nat_4R_GFFB -0.70 0.01 -0.05 -0.05
## PP.Nat_2R_GFFB -0.69 0.08 0.07 0.14
## PP.Nat_3R_GFFB -0.72 0.17 0.20 0.23
## PP.Nat_1_GFPRB -0.24 -0.32 -0.37 -0.62
## PP.Nat_4R_GFPRB -0.66 0.12 0.05 -0.09
## PP.Nat_2R_GFPRB -0.72 -0.04 -0.09 -0.18
## PP.Nat_3R_GFPRB -0.74 -0.05 -0.03 0.02
## PP.Nat_1_CBB 0.71 -0.18 -0.19 -0.17
## PP.Nat_4R_CBB 0.08 0.30 0.39 0.30
## PP.Nat_2R_CBB 0.03 0.21 0.45 0.41
## PP.Nat_3R_CBB -0.02 0.14 0.41 0.41
## PP.Nat_1_PBPB 1.00 0.25 0.14 -0.02
## PP.Nat_4R_PBPB 0.25 1.00 0.79 0.66
## PP.Nat_2R_PBPB 0.14 0.79 1.00 0.77
## PP.Nat_3R_PBPB -0.02 0.66 0.77 1.00
## PP.Nat_1_PBFB 0.91 0.06 0.05 -0.04
## PP.Nat_4R_PBFB -0.38 -0.67 -0.63 -0.58
## PP.Nat_2R_PBFB -0.04 -0.63 -0.80 -0.76
## PP.Nat_3R_PBFB -0.01 -0.59 -0.74 -0.86
## PP.Nat_1_VB 0.74 0.21 0.03 -0.12
## PP.Nat_4R_VB -0.04 0.70 0.56 0.46
## PP.Nat_2R_VB -0.29 0.48 0.49 0.40
## PP.Nat_3R_VB -0.39 0.44 0.43 0.56
## PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB
## PP.Nat_1_GFFB 0.21 0.51 0.61 0.63
## PP.Nat_4R_GFFB -0.78 0.03 -0.06 -0.03
## PP.Nat_2R_GFFB -0.72 0.01 -0.22 -0.20
## PP.Nat_3R_GFFB -0.77 -0.14 -0.29 -0.32
## PP.Nat_1_GFPRB -0.28 0.52 0.50 0.57
## PP.Nat_4R_GFPRB -0.76 0.04 -0.07 -0.02
## PP.Nat_2R_GFPRB -0.81 0.16 0.05 0.07
## PP.Nat_3R_GFPRB -0.78 0.07 -0.06 -0.09
## PP.Nat_1_CBB 0.83 -0.13 0.08 0.09
## PP.Nat_4R_CBB 0.19 -0.58 -0.64 -0.49
## PP.Nat_2R_CBB 0.16 -0.50 -0.65 -0.56
## PP.Nat_3R_CBB 0.14 -0.44 -0.63 -0.53
## PP.Nat_1_PBPB 0.91 -0.38 -0.04 -0.01
## PP.Nat_4R_PBPB 0.06 -0.67 -0.63 -0.59
## PP.Nat_2R_PBPB 0.05 -0.63 -0.80 -0.74
## PP.Nat_3R_PBPB -0.04 -0.58 -0.76 -0.86
## PP.Nat_1_PBFB 1.00 -0.36 -0.09 -0.04
## PP.Nat_4R_PBFB -0.36 1.00 0.81 0.77
## PP.Nat_2R_PBFB -0.09 0.81 1.00 0.89
## PP.Nat_3R_PBFB -0.04 0.77 0.89 1.00
## PP.Nat_1_VB 0.67 -0.31 0.02 0.02
## PP.Nat_4R_VB -0.20 -0.58 -0.57 -0.52
## PP.Nat_2R_VB -0.35 -0.39 -0.51 -0.48
## PP.Nat_3R_VB -0.41 -0.36 -0.50 -0.59
## PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Nat_1_GFFB 0.03 -0.68 -0.68 -0.66
## PP.Nat_4R_GFFB -0.50 0.30 0.33 0.27
## PP.Nat_2R_GFFB -0.49 0.24 0.28 0.29
## PP.Nat_3R_GFFB -0.39 0.50 0.55 0.54
## PP.Nat_1_GFPRB -0.04 -0.13 -0.03 -0.09
## PP.Nat_4R_GFPRB -0.38 0.48 0.60 0.50
## PP.Nat_2R_GFPRB -0.43 0.38 0.53 0.47
## PP.Nat_3R_GFPRB -0.47 0.40 0.54 0.51
## PP.Nat_1_CBB 0.34 -0.54 -0.68 -0.61
## PP.Nat_4R_CBB -0.24 0.03 -0.04 -0.08
## PP.Nat_2R_CBB -0.31 -0.11 -0.12 -0.12
## PP.Nat_3R_CBB -0.34 -0.13 -0.13 -0.13
## PP.Nat_1_PBPB 0.74 -0.04 -0.29 -0.39
## PP.Nat_4R_PBPB 0.21 0.70 0.48 0.44
## PP.Nat_2R_PBPB 0.03 0.56 0.49 0.43
## PP.Nat_3R_PBPB -0.12 0.46 0.40 0.56
## PP.Nat_1_PBFB 0.67 -0.20 -0.35 -0.41
## PP.Nat_4R_PBFB -0.31 -0.58 -0.39 -0.36
## PP.Nat_2R_PBFB 0.02 -0.57 -0.51 -0.50
## PP.Nat_3R_PBFB 0.02 -0.52 -0.48 -0.59
## PP.Nat_1_VB 1.00 0.27 0.11 -0.02
## PP.Nat_4R_VB 0.27 1.00 0.88 0.74
## PP.Nat_2R_VB 0.11 0.88 1.00 0.83
## PP.Nat_3R_VB -0.02 0.74 0.83 1.00
##
## n= 24
##
##
## P
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 0.7143 0.6268 0.0272
## PP.Nat_4R_GFFB 0.7143 0.0000 0.0000
## PP.Nat_2R_GFFB 0.6268 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0272 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0123 0.2510 0.7250 0.9127
## PP.Nat_4R_GFPRB 0.2573 0.0000 0.0064 0.0001
## PP.Nat_2R_GFPRB 0.3209 0.0000 0.0029 0.0000
## PP.Nat_3R_GFPRB 0.1555 0.0000 0.0056 0.0000
## PP.Nat_1_CBB 0.0212 0.0001 0.0008 0.0000
## PP.Nat_4R_CBB 0.3174 0.7213 0.4809 0.8717
## PP.Nat_2R_CBB 0.4345 0.8920 0.3142 0.8936
## PP.Nat_3R_CBB 0.4118 0.9786 0.3193 0.9251
## PP.Nat_1_PBPB 0.5045 0.0002 0.0002 0.0000
## PP.Nat_4R_PBPB 0.0006 0.9735 0.7138 0.4183
## PP.Nat_2R_PBPB 0.0002 0.8025 0.7423 0.3375
## PP.Nat_3R_PBPB 0.0009 0.8024 0.5044 0.2765
## PP.Nat_1_PBFB 0.3200 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0104 0.9044 0.9695 0.5111
## PP.Nat_2R_PBFB 0.0015 0.7727 0.3059 0.1745
## PP.Nat_3R_PBFB 0.0010 0.8836 0.3478 0.1321
## PP.Nat_1_VB 0.9003 0.0131 0.0140 0.0564
## PP.Nat_4R_VB 0.0003 0.1478 0.2496 0.0129
## PP.Nat_2R_VB 0.0003 0.1127 0.1771 0.0051
## PP.Nat_3R_VB 0.0004 0.2013 0.1629 0.0064
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB
## PP.Nat_1_GFFB 0.0123 0.2573 0.3209 0.1555
## PP.Nat_4R_GFFB 0.2510 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.7250 0.0064 0.0029 0.0056
## PP.Nat_3R_GFFB 0.9127 0.0001 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0220 0.0400 0.2314
## PP.Nat_4R_GFPRB 0.0220 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0400 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.2314 0.0000 0.0000
## PP.Nat_1_CBB 0.2716 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0464 0.6545 0.3930 0.4410
## PP.Nat_2R_CBB 0.0156 0.2854 0.1986 0.3388
## PP.Nat_3R_CBB 0.0180 0.3051 0.2256 0.5111
## PP.Nat_1_PBPB 0.2618 0.0005 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1301 0.5838 0.8461 0.8048
## PP.Nat_2R_PBPB 0.0781 0.8204 0.6922 0.8861
## PP.Nat_3R_PBPB 0.0011 0.6804 0.3948 0.9416
## PP.Nat_1_PBFB 0.1809 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0092 0.8393 0.4665 0.7390
## PP.Nat_2R_PBFB 0.0120 0.7449 0.8058 0.7634
## PP.Nat_3R_PBFB 0.0033 0.9304 0.7524 0.6762
## PP.Nat_1_VB 0.8623 0.0670 0.0359 0.0201
## PP.Nat_4R_VB 0.5410 0.0179 0.0686 0.0557
## PP.Nat_2R_VB 0.9038 0.0018 0.0074 0.0069
## PP.Nat_3R_VB 0.6801 0.0121 0.0220 0.0106
## PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB
## PP.Nat_1_GFFB 0.0212 0.3174 0.4345 0.4118
## PP.Nat_4R_GFFB 0.0001 0.7213 0.8920 0.9786
## PP.Nat_2R_GFFB 0.0008 0.4809 0.3142 0.3193
## PP.Nat_3R_GFFB 0.0000 0.8717 0.8936 0.9251
## PP.Nat_1_GFPRB 0.2716 0.0464 0.0156 0.0180
## PP.Nat_4R_GFPRB 0.0000 0.6545 0.2854 0.3051
## PP.Nat_2R_GFPRB 0.0000 0.3930 0.1986 0.2256
## PP.Nat_3R_GFPRB 0.0000 0.4410 0.3388 0.5111
## PP.Nat_1_CBB 0.1712 0.1817 0.2925
## PP.Nat_4R_CBB 0.1712 0.0000 0.0000
## PP.Nat_2R_CBB 0.1817 0.0000 0.0000
## PP.Nat_3R_CBB 0.2925 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.7181 0.9029 0.9195
## PP.Nat_4R_PBPB 0.3959 0.1535 0.3356 0.5218
## PP.Nat_2R_PBPB 0.3627 0.0599 0.0281 0.0445
## PP.Nat_3R_PBPB 0.4294 0.1611 0.0465 0.0493
## PP.Nat_1_PBFB 0.0000 0.3778 0.4682 0.5058
## PP.Nat_4R_PBFB 0.5297 0.0029 0.0135 0.0297
## PP.Nat_2R_PBFB 0.7010 0.0008 0.0006 0.0010
## PP.Nat_3R_PBFB 0.6696 0.0147 0.0044 0.0075
## PP.Nat_1_VB 0.1041 0.2611 0.1418 0.1023
## PP.Nat_4R_VB 0.0059 0.8914 0.6126 0.5513
## PP.Nat_2R_VB 0.0003 0.8406 0.5631 0.5376
## PP.Nat_3R_VB 0.0014 0.7138 0.5793 0.5443
## PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB
## PP.Nat_1_GFFB 0.5045 0.0006 0.0002 0.0009
## PP.Nat_4R_GFFB 0.0002 0.9735 0.8025 0.8024
## PP.Nat_2R_GFFB 0.0002 0.7138 0.7423 0.5044
## PP.Nat_3R_GFFB 0.0000 0.4183 0.3375 0.2765
## PP.Nat_1_GFPRB 0.2618 0.1301 0.0781 0.0011
## PP.Nat_4R_GFPRB 0.0005 0.5838 0.8204 0.6804
## PP.Nat_2R_GFPRB 0.0000 0.8461 0.6922 0.3948
## PP.Nat_3R_GFPRB 0.0000 0.8048 0.8861 0.9416
## PP.Nat_1_CBB 0.0000 0.3959 0.3627 0.4294
## PP.Nat_4R_CBB 0.7181 0.1535 0.0599 0.1611
## PP.Nat_2R_CBB 0.9029 0.3356 0.0281 0.0465
## PP.Nat_3R_CBB 0.9195 0.5218 0.0445 0.0493
## PP.Nat_1_PBPB 0.2340 0.5054 0.9323
## PP.Nat_4R_PBPB 0.2340 0.0000 0.0004
## PP.Nat_2R_PBPB 0.5054 0.0000 0.0000
## PP.Nat_3R_PBPB 0.9323 0.0004 0.0000
## PP.Nat_1_PBFB 0.0000 0.7858 0.8112 0.8419
## PP.Nat_4R_PBFB 0.0705 0.0003 0.0010 0.0033
## PP.Nat_2R_PBFB 0.8426 0.0009 0.0000 0.0000
## PP.Nat_3R_PBFB 0.9692 0.0024 0.0000 0.0000
## PP.Nat_1_VB 0.0000 0.3339 0.8868 0.5869
## PP.Nat_4R_VB 0.8526 0.0001 0.0046 0.0248
## PP.Nat_2R_VB 0.1736 0.0164 0.0143 0.0554
## PP.Nat_3R_VB 0.0603 0.0319 0.0380 0.0045
## PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB
## PP.Nat_1_GFFB 0.3200 0.0104 0.0015 0.0010
## PP.Nat_4R_GFFB 0.0000 0.9044 0.7727 0.8836
## PP.Nat_2R_GFFB 0.0000 0.9695 0.3059 0.3478
## PP.Nat_3R_GFFB 0.0000 0.5111 0.1745 0.1321
## PP.Nat_1_GFPRB 0.1809 0.0092 0.0120 0.0033
## PP.Nat_4R_GFPRB 0.0000 0.8393 0.7449 0.9304
## PP.Nat_2R_GFPRB 0.0000 0.4665 0.8058 0.7524
## PP.Nat_3R_GFPRB 0.0000 0.7390 0.7634 0.6762
## PP.Nat_1_CBB 0.0000 0.5297 0.7010 0.6696
## PP.Nat_4R_CBB 0.3778 0.0029 0.0008 0.0147
## PP.Nat_2R_CBB 0.4682 0.0135 0.0006 0.0044
## PP.Nat_3R_CBB 0.5058 0.0297 0.0010 0.0075
## PP.Nat_1_PBPB 0.0000 0.0705 0.8426 0.9692
## PP.Nat_4R_PBPB 0.7858 0.0003 0.0009 0.0024
## PP.Nat_2R_PBPB 0.8112 0.0010 0.0000 0.0000
## PP.Nat_3R_PBPB 0.8419 0.0033 0.0000 0.0000
## PP.Nat_1_PBFB 0.0878 0.6673 0.8615
## PP.Nat_4R_PBFB 0.0878 0.0000 0.0000
## PP.Nat_2R_PBFB 0.6673 0.0000 0.0000
## PP.Nat_3R_PBFB 0.8615 0.0000 0.0000
## PP.Nat_1_VB 0.0004 0.1462 0.9201 0.9170
## PP.Nat_4R_VB 0.3540 0.0027 0.0035 0.0092
## PP.Nat_2R_VB 0.0897 0.0594 0.0113 0.0184
## PP.Nat_3R_VB 0.0438 0.0848 0.0136 0.0023
## PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Nat_1_GFFB 0.9003 0.0003 0.0003 0.0004
## PP.Nat_4R_GFFB 0.0131 0.1478 0.1127 0.2013
## PP.Nat_2R_GFFB 0.0140 0.2496 0.1771 0.1629
## PP.Nat_3R_GFFB 0.0564 0.0129 0.0051 0.0064
## PP.Nat_1_GFPRB 0.8623 0.5410 0.9038 0.6801
## PP.Nat_4R_GFPRB 0.0670 0.0179 0.0018 0.0121
## PP.Nat_2R_GFPRB 0.0359 0.0686 0.0074 0.0220
## PP.Nat_3R_GFPRB 0.0201 0.0557 0.0069 0.0106
## PP.Nat_1_CBB 0.1041 0.0059 0.0003 0.0014
## PP.Nat_4R_CBB 0.2611 0.8914 0.8406 0.7138
## PP.Nat_2R_CBB 0.1418 0.6126 0.5631 0.5793
## PP.Nat_3R_CBB 0.1023 0.5513 0.5376 0.5443
## PP.Nat_1_PBPB 0.0000 0.8526 0.1736 0.0603
## PP.Nat_4R_PBPB 0.3339 0.0001 0.0164 0.0319
## PP.Nat_2R_PBPB 0.8868 0.0046 0.0143 0.0380
## PP.Nat_3R_PBPB 0.5869 0.0248 0.0554 0.0045
## PP.Nat_1_PBFB 0.0004 0.3540 0.0897 0.0438
## PP.Nat_4R_PBFB 0.1462 0.0027 0.0594 0.0848
## PP.Nat_2R_PBFB 0.9201 0.0035 0.0113 0.0136
## PP.Nat_3R_PBFB 0.9170 0.0092 0.0184 0.0023
## PP.Nat_1_VB 0.1949 0.6137 0.9263
## PP.Nat_4R_VB 0.1949 0.0000 0.0000
## PP.Nat_2R_VB 0.6137 0.0000 0.0000
## PP.Nat_3R_VB 0.9263 0.0000 0.0000library(corrplot)
corrplot(mydata.cor5, method="color")
corrplot(mydata.cor5, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Naturalness (TOTAL SCALE) and Support Scales
PP$corNSScales <- data.frame(PP$Naturalness_Scale_GFFB_Tot, PP$Naturalness_Scale_GFPRB_Tot, PP$Naturalness_Scale_CBB_Tot, PP$Naturalness_Scale_PBPB_Tot, PP$Naturalness_Scale_PBFB_Tot, PP$Naturalness_Scale_VB_Tot, PP$Behav_Scale_GFFB, PP$Behav_Scale_GFPRB, PP$Behav_Scale_CBB, PP$Behav_Scale_PBPB, PP$Behav_Scale_PBFB, PP$Behav_Scale_VB)
mydata.cor4 = cor(PP$corNSScales, use = "pairwise.complete.obs")
head(round(mydata.cor4,2))## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 0.18 0.18 -0.15
## PP.Nat_4R_GFFB 0.18 1.00 0.61 0.50
## PP.Nat_2R_GFFB 0.18 0.61 1.00 0.44
## PP.Nat_3R_GFFB -0.15 0.50 0.44 1.00
## PP.Nat_1_GFPRB 0.42 0.15 0.07 0.01
## PP.Nat_4R_GFPRB 0.04 0.47 0.21 0.33
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB
## PP.Nat_1_GFFB 0.42 0.04 -0.03 -0.04
## PP.Nat_4R_GFFB 0.15 0.47 0.49 0.38
## PP.Nat_2R_GFFB 0.07 0.21 0.29 0.17
## PP.Nat_3R_GFFB 0.01 0.33 0.34 0.49
## PP.Nat_1_GFPRB 1.00 0.38 0.25 0.14
## PP.Nat_4R_GFPRB 0.38 1.00 0.68 0.52
## PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB
## PP.Nat_1_GFFB 0.35 -0.01 0.04 0.00
## PP.Nat_4R_GFFB -0.36 0.20 0.14 0.05
## PP.Nat_2R_GFFB -0.32 0.13 0.22 0.21
## PP.Nat_3R_GFFB -0.41 0.08 0.07 0.01
## PP.Nat_1_GFPRB -0.10 -0.05 -0.13 -0.13
## PP.Nat_4R_GFPRB -0.34 0.11 -0.06 -0.06
## PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB
## PP.Nat_1_GFFB 0.14 -0.21 -0.26 -0.17
## PP.Nat_4R_GFFB -0.23 0.08 0.00 -0.04
## PP.Nat_2R_GFFB -0.27 0.15 0.04 0.06
## PP.Nat_3R_GFFB -0.36 0.09 0.14 0.10
## PP.Nat_1_GFPRB -0.04 0.03 0.05 -0.33
## PP.Nat_4R_GFPRB -0.23 0.20 0.11 -0.12
## PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB
## PP.Nat_1_GFFB 0.17 0.24 0.28 0.29
## PP.Nat_4R_GFFB -0.35 -0.07 0.03 0.05
## PP.Nat_2R_GFFB -0.33 0.05 -0.11 -0.07
## PP.Nat_3R_GFFB -0.37 -0.11 -0.11 -0.15
## PP.Nat_1_GFPRB -0.06 0.23 0.20 0.28
## PP.Nat_4R_GFPRB -0.35 -0.01 -0.04 0.05
## PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Nat_1_GFFB 0.09 -0.21 -0.22 -0.24
## PP.Nat_4R_GFFB -0.13 0.25 0.13 0.05
## PP.Nat_2R_GFFB -0.09 0.15 0.12 0.12
## PP.Nat_3R_GFFB -0.04 0.27 0.22 0.20
## PP.Nat_1_GFPRB 0.09 0.02 0.07 0.06
## PP.Nat_4R_GFPRB -0.11 0.32 0.39 0.25
## PP.BehavInt1_GFFB PP.BehavInt2_GFFB PP.BehavInt3_GFFB
## PP.Nat_1_GFFB 0.59 0.52 0.58
## PP.Nat_4R_GFFB 0.13 0.18 0.10
## PP.Nat_2R_GFFB 0.16 0.16 0.12
## PP.Nat_3R_GFFB -0.19 -0.09 -0.21
## PP.Nat_1_GFPRB 0.27 0.31 0.24
## PP.Nat_4R_GFPRB -0.04 0.05 -0.03
## PP.BehavInt4_GFFB PP.BehavInt1_GFPRB PP.BehavInt2_GFPRB
## PP.Nat_1_GFFB 0.59 0.08 0.03
## PP.Nat_4R_GFFB 0.15 -0.19 -0.24
## PP.Nat_2R_GFFB 0.14 -0.29 -0.31
## PP.Nat_3R_GFFB -0.17 -0.20 -0.23
## PP.Nat_1_GFPRB 0.26 0.15 0.04
## PP.Nat_4R_GFPRB -0.05 -0.04 -0.13
## PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB PP.BehavInt1_CBB
## PP.Nat_1_GFFB 0.02 0.02 0.26
## PP.Nat_4R_GFFB -0.22 -0.29 -0.26
## PP.Nat_2R_GFFB -0.34 -0.33 -0.27
## PP.Nat_3R_GFFB -0.22 -0.22 -0.29
## PP.Nat_1_GFPRB 0.08 0.07 -0.02
## PP.Nat_4R_GFPRB -0.10 -0.05 -0.27
## PP.BehavInt2_CBB PP.BehavInt3_CBB PP.BehavInt4_CBB
## PP.Nat_1_GFFB 0.35 0.29 0.31
## PP.Nat_4R_GFFB -0.29 -0.29 -0.26
## PP.Nat_2R_GFFB -0.25 -0.28 -0.28
## PP.Nat_3R_GFFB -0.29 -0.32 -0.33
## PP.Nat_1_GFPRB -0.03 -0.06 -0.03
## PP.Nat_4R_GFPRB -0.34 -0.33 -0.30
## PP.BehavInt1_PBPB PP.BehavInt2_PBPB PP.BehavInt3_PBPB
## PP.Nat_1_GFFB 0.08 0.03 0.02
## PP.Nat_4R_GFFB -0.19 -0.24 -0.22
## PP.Nat_2R_GFFB -0.29 -0.31 -0.34
## PP.Nat_3R_GFFB -0.20 -0.23 -0.22
## PP.Nat_1_GFPRB 0.15 0.04 0.08
## PP.Nat_4R_GFPRB -0.04 -0.13 -0.10
## PP.BehavInt4_PBPB PP.BehavInt1_PBFB PP.BehavInt2_PBFB
## PP.Nat_1_GFFB 0.02 0.03 0.16
## PP.Nat_4R_GFFB -0.29 -0.38 -0.38
## PP.Nat_2R_GFFB -0.33 -0.37 -0.29
## PP.Nat_3R_GFFB -0.22 -0.34 -0.39
## PP.Nat_1_GFPRB 0.07 -0.10 -0.04
## PP.Nat_4R_GFPRB -0.05 -0.27 -0.28
## PP.BehavInt3_PBFB PP.BehavInt4_PBFB PP.BehavInt1_VB
## PP.Nat_1_GFFB 0.08 0.12 0.05
## PP.Nat_4R_GFFB -0.37 -0.35 -0.14
## PP.Nat_2R_GFFB -0.36 -0.31 -0.17
## PP.Nat_3R_GFFB -0.38 -0.41 -0.11
## PP.Nat_1_GFPRB -0.01 0.05 0.10
## PP.Nat_4R_GFPRB -0.22 -0.20 -0.08
## PP.BehavInt2_VB PP.BehavInt3_VB PP.BehavInt4_VB
## PP.Nat_1_GFFB 0.04 0.05 0.05
## PP.Nat_4R_GFFB -0.17 -0.17 -0.15
## PP.Nat_2R_GFFB -0.12 -0.18 -0.18
## PP.Nat_3R_GFFB -0.09 -0.10 -0.09
## PP.Nat_1_GFPRB 0.03 0.17 0.13
## PP.Nat_4R_GFPRB -0.21 -0.03 -0.12library("Hmisc")
mydata.rcorr4 = rcorr(as.matrix(mydata.cor4))
mydata.rcorr4## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 0.00 0.02 -0.39
## PP.Nat_4R_GFFB 0.00 1.00 0.92 0.85
## PP.Nat_2R_GFFB 0.02 0.92 1.00 0.81
## PP.Nat_3R_GFFB -0.39 0.85 0.81 1.00
## PP.Nat_1_GFPRB 0.46 0.34 0.22 0.12
## PP.Nat_4R_GFPRB -0.25 0.82 0.67 0.80
## PP.Nat_2R_GFPRB -0.20 0.83 0.68 0.78
## PP.Nat_3R_GFPRB -0.29 0.81 0.69 0.87
## PP.Nat_1_CBB 0.48 -0.74 -0.69 -0.86
## PP.Nat_4R_CBB -0.26 0.04 0.07 0.07
## PP.Nat_2R_CBB -0.11 0.12 0.24 0.10
## PP.Nat_3R_CBB -0.14 0.17 0.31 0.16
## PP.Nat_1_PBPB 0.00 -0.82 -0.83 -0.76
## PP.Nat_4R_PBPB -0.72 -0.02 -0.01 0.22
## PP.Nat_2R_PBPB -0.79 -0.03 0.01 0.27
## PP.Nat_3R_PBPB -0.59 0.19 0.32 0.42
## PP.Nat_1_PBFB 0.12 -0.88 -0.85 -0.84
## PP.Nat_4R_PBFB 0.55 0.26 0.27 0.03
## PP.Nat_2R_PBFB 0.61 -0.02 -0.11 -0.26
## PP.Nat_3R_PBFB 0.57 -0.07 -0.18 -0.32
## PP.Nat_1_VB -0.12 -0.63 -0.66 -0.50
## PP.Nat_4R_VB -0.73 0.26 0.19 0.50
## PP.Nat_2R_VB -0.71 0.39 0.33 0.62
## PP.Nat_3R_VB -0.67 0.39 0.39 0.64
## PP.BehavInt1_GFFB 0.92 -0.03 0.00 -0.41
## PP.BehavInt2_GFFB 0.89 0.05 0.06 -0.32
## PP.BehavInt3_GFFB 0.91 -0.08 -0.04 -0.45
## PP.BehavInt4_GFFB 0.91 0.00 0.03 -0.38
## PP.BehavInt1_GFPRB -0.12 -0.76 -0.84 -0.65
## PP.BehavInt2_GFPRB -0.12 -0.81 -0.86 -0.69
## PP.BehavInt3_GFPRB -0.10 -0.79 -0.86 -0.68
## PP.BehavInt4_GFPRB -0.11 -0.79 -0.86 -0.67
## PP.BehavInt1_CBB 0.37 -0.77 -0.75 -0.83
## PP.BehavInt2_CBB 0.43 -0.75 -0.72 -0.84
## PP.BehavInt3_CBB 0.39 -0.77 -0.74 -0.84
## PP.BehavInt4_CBB 0.40 -0.75 -0.74 -0.84
## PP.BehavInt1_PBPB -0.12 -0.76 -0.84 -0.65
## PP.BehavInt2_PBPB -0.12 -0.81 -0.86 -0.69
## PP.BehavInt3_PBPB -0.10 -0.79 -0.86 -0.68
## PP.BehavInt4_PBPB -0.11 -0.79 -0.86 -0.67
## PP.BehavInt1_PBFB -0.01 -0.87 -0.88 -0.79
## PP.BehavInt2_PBFB 0.06 -0.88 -0.86 -0.82
## PP.BehavInt3_PBFB 0.04 -0.87 -0.88 -0.80
## PP.BehavInt4_PBFB 0.05 -0.87 -0.87 -0.81
## PP.BehavInt1_VB -0.14 -0.67 -0.72 -0.54
## PP.BehavInt2_VB -0.17 -0.71 -0.72 -0.56
## PP.BehavInt3_VB -0.15 -0.66 -0.73 -0.52
## PP.BehavInt4_VB -0.15 -0.68 -0.73 -0.53
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Nat_1_GFFB 0.46 -0.25 -0.20
## PP.Nat_4R_GFFB 0.34 0.82 0.83
## PP.Nat_2R_GFFB 0.22 0.67 0.68
## PP.Nat_3R_GFFB 0.12 0.80 0.78
## PP.Nat_1_GFPRB 1.00 0.48 0.44
## PP.Nat_4R_GFPRB 0.48 1.00 0.95
## PP.Nat_2R_GFPRB 0.44 0.95 1.00
## PP.Nat_3R_GFPRB 0.31 0.90 0.90
## PP.Nat_1_CBB -0.26 -0.82 -0.78
## PP.Nat_4R_CBB -0.45 -0.05 -0.08
## PP.Nat_2R_CBB -0.46 -0.09 -0.10
## PP.Nat_3R_CBB -0.43 -0.03 -0.04
## PP.Nat_1_PBPB -0.28 -0.71 -0.75
## PP.Nat_4R_PBPB -0.29 0.16 0.03
## PP.Nat_2R_PBPB -0.35 0.15 0.05
## PP.Nat_3R_PBPB -0.48 0.16 0.07
## PP.Nat_1_PBFB -0.33 -0.82 -0.83
## PP.Nat_4R_PBFB 0.55 0.16 0.24
## PP.Nat_2R_PBFB 0.51 -0.08 0.02
## PP.Nat_3R_PBFB 0.56 -0.07 -0.01
## PP.Nat_1_VB -0.09 -0.46 -0.52
## PP.Nat_4R_VB -0.07 0.50 0.39
## PP.Nat_2R_VB 0.02 0.65 0.57
## PP.Nat_3R_VB -0.03 0.60 0.54
## PP.BehavInt1_GFFB 0.37 -0.28 -0.22
## PP.BehavInt2_GFFB 0.45 -0.17 -0.11
## PP.BehavInt3_GFFB 0.34 -0.31 -0.26
## PP.BehavInt4_GFFB 0.37 -0.25 -0.20
## PP.BehavInt1_GFPRB -0.17 -0.54 -0.56
## PP.BehavInt2_GFPRB -0.25 -0.62 -0.63
## PP.BehavInt3_GFPRB -0.19 -0.59 -0.61
## PP.BehavInt4_GFPRB -0.17 -0.57 -0.59
## PP.BehavInt1_CBB -0.24 -0.79 -0.73
## PP.BehavInt2_CBB -0.23 -0.81 -0.76
## PP.BehavInt3_CBB -0.24 -0.80 -0.74
## PP.BehavInt4_CBB -0.23 -0.79 -0.74
## PP.BehavInt1_PBPB -0.17 -0.54 -0.56
## PP.BehavInt2_PBPB -0.25 -0.62 -0.63
## PP.BehavInt3_PBPB -0.19 -0.59 -0.61
## PP.BehavInt4_PBPB -0.17 -0.57 -0.59
## PP.BehavInt1_PBFB -0.29 -0.73 -0.74
## PP.BehavInt2_PBFB -0.29 -0.77 -0.78
## PP.BehavInt3_PBFB -0.24 -0.72 -0.75
## PP.BehavInt4_PBFB -0.24 -0.73 -0.76
## PP.BehavInt1_VB -0.08 -0.47 -0.53
## PP.BehavInt2_VB -0.18 -0.55 -0.62
## PP.BehavInt3_VB -0.05 -0.44 -0.50
## PP.BehavInt4_VB -0.08 -0.48 -0.53
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Nat_1_GFFB -0.29 0.48 -0.26 -0.11
## PP.Nat_4R_GFFB 0.81 -0.74 0.04 0.12
## PP.Nat_2R_GFFB 0.69 -0.69 0.07 0.24
## PP.Nat_3R_GFFB 0.87 -0.86 0.07 0.10
## PP.Nat_1_GFPRB 0.31 -0.26 -0.45 -0.46
## PP.Nat_4R_GFPRB 0.90 -0.82 -0.05 -0.09
## PP.Nat_2R_GFPRB 0.90 -0.78 -0.08 -0.10
## PP.Nat_3R_GFPRB 1.00 -0.85 -0.09 -0.06
## PP.Nat_1_CBB -0.85 1.00 0.19 0.16
## PP.Nat_4R_CBB -0.09 0.19 1.00 0.87
## PP.Nat_2R_CBB -0.06 0.16 0.87 1.00
## PP.Nat_3R_CBB 0.05 0.04 0.79 0.92
## PP.Nat_1_PBPB -0.77 0.69 -0.01 -0.17
## PP.Nat_4R_PBPB 0.06 -0.28 0.27 0.10
## PP.Nat_2R_PBPB 0.12 -0.32 0.40 0.34
## PP.Nat_3R_PBPB 0.26 -0.39 0.28 0.39
## PP.Nat_1_PBFB -0.84 0.82 0.10 -0.03
## PP.Nat_4R_PBFB 0.19 -0.16 -0.53 -0.35
## PP.Nat_2R_PBFB -0.08 0.13 -0.63 -0.60
## PP.Nat_3R_PBFB -0.13 0.15 -0.52 -0.56
## PP.Nat_1_VB -0.52 0.37 -0.27 -0.44
## PP.Nat_4R_VB 0.45 -0.61 0.01 -0.16
## PP.Nat_2R_VB 0.61 -0.75 -0.01 -0.10
## PP.Nat_3R_VB 0.61 -0.73 -0.02 -0.05
## PP.BehavInt1_GFFB -0.31 0.49 -0.36 -0.18
## PP.BehavInt2_GFFB -0.23 0.39 -0.40 -0.23
## PP.BehavInt3_GFFB -0.35 0.53 -0.35 -0.17
## PP.BehavInt4_GFFB -0.27 0.47 -0.36 -0.17
## PP.BehavInt1_GFPRB -0.62 0.55 -0.04 -0.27
## PP.BehavInt2_GFPRB -0.67 0.60 -0.01 -0.22
## PP.BehavInt3_GFPRB -0.65 0.58 -0.04 -0.26
## PP.BehavInt4_GFPRB -0.63 0.56 -0.09 -0.32
## PP.BehavInt1_CBB -0.83 0.93 0.17 0.08
## PP.BehavInt2_CBB -0.85 0.96 0.17 0.11
## PP.BehavInt3_CBB -0.85 0.94 0.16 0.09
## PP.BehavInt4_CBB -0.84 0.94 0.18 0.09
## PP.BehavInt1_PBPB -0.62 0.55 -0.04 -0.27
## PP.BehavInt2_PBPB -0.67 0.60 -0.01 -0.22
## PP.BehavInt3_PBPB -0.65 0.58 -0.04 -0.26
## PP.BehavInt4_PBPB -0.63 0.56 -0.09 -0.32
## PP.BehavInt1_PBFB -0.77 0.70 0.01 -0.16
## PP.BehavInt2_PBFB -0.82 0.75 0.04 -0.11
## PP.BehavInt3_PBFB -0.78 0.70 -0.02 -0.18
## PP.BehavInt4_PBFB -0.80 0.72 0.00 -0.15
## PP.BehavInt1_VB -0.54 0.37 -0.25 -0.46
## PP.BehavInt2_VB -0.60 0.40 -0.21 -0.39
## PP.BehavInt3_VB -0.50 0.34 -0.28 -0.50
## PP.BehavInt4_VB -0.53 0.36 -0.26 -0.46
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Nat_1_GFFB -0.14 0.00 -0.72 -0.79
## PP.Nat_4R_GFFB 0.17 -0.82 -0.02 -0.03
## PP.Nat_2R_GFFB 0.31 -0.83 -0.01 0.01
## PP.Nat_3R_GFFB 0.16 -0.76 0.22 0.27
## PP.Nat_1_GFPRB -0.43 -0.28 -0.29 -0.35
## PP.Nat_4R_GFPRB -0.03 -0.71 0.16 0.15
## PP.Nat_2R_GFPRB -0.04 -0.75 0.03 0.05
## PP.Nat_3R_GFPRB 0.05 -0.77 0.06 0.12
## PP.Nat_1_CBB 0.04 0.69 -0.28 -0.32
## PP.Nat_4R_CBB 0.79 -0.01 0.27 0.40
## PP.Nat_2R_CBB 0.92 -0.17 0.10 0.34
## PP.Nat_3R_CBB 1.00 -0.25 0.07 0.34
## PP.Nat_1_PBPB -0.25 1.00 0.24 0.12
## PP.Nat_4R_PBPB 0.07 0.24 1.00 0.83
## PP.Nat_2R_PBPB 0.34 0.12 0.83 1.00
## PP.Nat_3R_PBPB 0.43 -0.23 0.62 0.72
## PP.Nat_1_PBFB -0.10 0.93 0.03 -0.02
## PP.Nat_4R_PBFB -0.29 -0.48 -0.66 -0.62
## PP.Nat_2R_PBFB -0.58 -0.04 -0.62 -0.77
## PP.Nat_3R_PBFB -0.54 0.07 -0.54 -0.68
## PP.Nat_1_VB -0.48 0.84 0.28 0.10
## PP.Nat_4R_VB -0.12 -0.02 0.75 0.65
## PP.Nat_2R_VB -0.05 -0.30 0.56 0.58
## PP.Nat_3R_VB -0.01 -0.43 0.49 0.51
## PP.BehavInt1_GFFB -0.21 0.02 -0.70 -0.82
## PP.BehavInt2_GFFB -0.26 -0.06 -0.68 -0.83
## PP.BehavInt3_GFFB -0.20 0.06 -0.68 -0.80
## PP.BehavInt4_GFFB -0.19 0.00 -0.68 -0.80
## PP.BehavInt1_GFPRB -0.36 0.92 0.27 0.15
## PP.BehavInt2_GFPRB -0.30 0.93 0.25 0.14
## PP.BehavInt3_GFPRB -0.33 0.93 0.23 0.12
## PP.BehavInt4_GFPRB -0.39 0.92 0.22 0.10
## PP.BehavInt1_CBB -0.06 0.74 -0.24 -0.28
## PP.BehavInt2_CBB -0.03 0.72 -0.26 -0.30
## PP.BehavInt3_CBB -0.04 0.73 -0.24 -0.28
## PP.BehavInt4_CBB -0.04 0.74 -0.23 -0.28
## PP.BehavInt1_PBPB -0.36 0.92 0.27 0.15
## PP.BehavInt2_PBPB -0.30 0.93 0.25 0.14
## PP.BehavInt3_PBPB -0.33 0.93 0.23 0.12
## PP.BehavInt4_PBPB -0.39 0.92 0.22 0.10
## PP.BehavInt1_PBFB -0.25 0.92 0.10 0.02
## PP.BehavInt2_PBFB -0.19 0.93 0.08 0.00
## PP.BehavInt3_PBFB -0.27 0.92 0.06 -0.02
## PP.BehavInt4_PBFB -0.24 0.93 0.07 0.00
## PP.BehavInt1_VB -0.49 0.86 0.28 0.10
## PP.BehavInt2_VB -0.43 0.88 0.35 0.19
## PP.BehavInt3_VB -0.53 0.85 0.28 0.11
## PP.BehavInt4_VB -0.50 0.86 0.29 0.13
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Nat_1_GFFB -0.59 0.12 0.55 0.61
## PP.Nat_4R_GFFB 0.19 -0.88 0.26 -0.02
## PP.Nat_2R_GFFB 0.32 -0.85 0.27 -0.11
## PP.Nat_3R_GFFB 0.42 -0.84 0.03 -0.26
## PP.Nat_1_GFPRB -0.48 -0.33 0.55 0.51
## PP.Nat_4R_GFPRB 0.16 -0.82 0.16 -0.08
## PP.Nat_2R_GFPRB 0.07 -0.83 0.24 0.02
## PP.Nat_3R_GFPRB 0.26 -0.84 0.19 -0.08
## PP.Nat_1_CBB -0.39 0.82 -0.16 0.13
## PP.Nat_4R_CBB 0.28 0.10 -0.53 -0.63
## PP.Nat_2R_CBB 0.39 -0.03 -0.35 -0.60
## PP.Nat_3R_CBB 0.43 -0.10 -0.29 -0.58
## PP.Nat_1_PBPB -0.23 0.93 -0.48 -0.04
## PP.Nat_4R_PBPB 0.62 0.03 -0.66 -0.62
## PP.Nat_2R_PBPB 0.72 -0.02 -0.62 -0.77
## PP.Nat_3R_PBPB 1.00 -0.29 -0.41 -0.71
## PP.Nat_1_PBFB -0.29 1.00 -0.44 -0.06
## PP.Nat_4R_PBFB -0.41 -0.44 1.00 0.78
## PP.Nat_2R_PBFB -0.71 -0.06 0.78 1.00
## PP.Nat_3R_PBFB -0.81 0.05 0.69 0.88
## PP.Nat_1_VB -0.23 0.73 -0.43 0.01
## PP.Nat_4R_VB 0.50 -0.22 -0.52 -0.53
## PP.Nat_2R_VB 0.50 -0.42 -0.32 -0.49
## PP.Nat_3R_VB 0.64 -0.50 -0.27 -0.49
## PP.BehavInt1_GFFB -0.56 0.14 0.53 0.59
## PP.BehavInt2_GFFB -0.57 0.06 0.57 0.62
## PP.BehavInt3_GFFB -0.54 0.18 0.52 0.58
## PP.BehavInt4_GFFB -0.52 0.11 0.53 0.57
## PP.BehavInt1_GFPRB -0.29 0.86 -0.49 -0.06
## PP.BehavInt2_GFPRB -0.27 0.88 -0.48 -0.05
## PP.BehavInt3_GFPRB -0.32 0.87 -0.46 -0.02
## PP.BehavInt4_GFPRB -0.33 0.86 -0.43 0.00
## PP.BehavInt1_CBB -0.49 0.88 -0.22 0.12
## PP.BehavInt2_CBB -0.47 0.86 -0.19 0.13
## PP.BehavInt3_CBB -0.48 0.87 -0.20 0.12
## PP.BehavInt4_CBB -0.47 0.86 -0.21 0.12
## PP.BehavInt1_PBPB -0.29 0.86 -0.49 -0.06
## PP.BehavInt2_PBPB -0.27 0.88 -0.48 -0.05
## PP.BehavInt3_PBPB -0.32 0.87 -0.46 -0.02
## PP.BehavInt4_PBPB -0.33 0.86 -0.43 0.00
## PP.BehavInt1_PBFB -0.32 0.94 -0.43 -0.03
## PP.BehavInt2_PBFB -0.31 0.95 -0.42 -0.03
## PP.BehavInt3_PBFB -0.36 0.95 -0.40 0.00
## PP.BehavInt4_PBFB -0.35 0.95 -0.41 -0.02
## PP.BehavInt1_VB -0.28 0.74 -0.42 0.03
## PP.BehavInt2_VB -0.14 0.77 -0.43 -0.04
## PP.BehavInt3_VB -0.27 0.73 -0.40 0.03
## PP.BehavInt4_VB -0.26 0.73 -0.40 0.04
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Nat_1_GFFB 0.57 -0.12 -0.73 -0.71
## PP.Nat_4R_GFFB -0.07 -0.63 0.26 0.39
## PP.Nat_2R_GFFB -0.18 -0.66 0.19 0.33
## PP.Nat_3R_GFFB -0.32 -0.50 0.50 0.62
## PP.Nat_1_GFPRB 0.56 -0.09 -0.07 0.02
## PP.Nat_4R_GFPRB -0.07 -0.46 0.50 0.65
## PP.Nat_2R_GFPRB -0.01 -0.52 0.39 0.57
## PP.Nat_3R_GFPRB -0.13 -0.52 0.45 0.61
## PP.Nat_1_CBB 0.15 0.37 -0.61 -0.75
## PP.Nat_4R_CBB -0.52 -0.27 0.01 -0.01
## PP.Nat_2R_CBB -0.56 -0.44 -0.16 -0.10
## PP.Nat_3R_CBB -0.54 -0.48 -0.12 -0.05
## PP.Nat_1_PBPB 0.07 0.84 -0.02 -0.30
## PP.Nat_4R_PBPB -0.54 0.28 0.75 0.56
## PP.Nat_2R_PBPB -0.68 0.10 0.65 0.58
## PP.Nat_3R_PBPB -0.81 -0.23 0.50 0.50
## PP.Nat_1_PBFB 0.05 0.73 -0.22 -0.42
## PP.Nat_4R_PBFB 0.69 -0.43 -0.52 -0.32
## PP.Nat_2R_PBFB 0.88 0.01 -0.53 -0.49
## PP.Nat_3R_PBFB 1.00 0.10 -0.44 -0.45
## PP.Nat_1_VB 0.10 1.00 0.27 0.04
## PP.Nat_4R_VB -0.44 0.27 1.00 0.89
## PP.Nat_2R_VB -0.45 0.04 0.89 1.00
## PP.Nat_3R_VB -0.56 -0.11 0.77 0.87
## PP.BehavInt1_GFFB 0.54 -0.07 -0.68 -0.67
## PP.BehavInt2_GFFB 0.56 -0.10 -0.64 -0.59
## PP.BehavInt3_GFFB 0.52 -0.05 -0.70 -0.69
## PP.BehavInt4_GFFB 0.51 -0.10 -0.67 -0.66
## PP.BehavInt1_GFPRB 0.09 0.84 0.10 -0.15
## PP.BehavInt2_GFPRB 0.07 0.83 0.05 -0.20
## PP.BehavInt3_GFPRB 0.13 0.85 0.04 -0.21
## PP.BehavInt4_GFPRB 0.16 0.86 0.07 -0.18
## PP.BehavInt1_CBB 0.17 0.45 -0.54 -0.67
## PP.BehavInt2_CBB 0.18 0.42 -0.58 -0.71
## PP.BehavInt3_CBB 0.17 0.43 -0.57 -0.69
## PP.BehavInt4_CBB 0.17 0.42 -0.55 -0.70
## PP.BehavInt1_PBPB 0.09 0.84 0.10 -0.15
## PP.BehavInt2_PBPB 0.07 0.83 0.05 -0.20
## PP.BehavInt3_PBPB 0.13 0.85 0.04 -0.21
## PP.BehavInt4_PBPB 0.16 0.86 0.07 -0.18
## PP.BehavInt1_PBFB 0.10 0.78 -0.11 -0.32
## PP.BehavInt2_PBFB 0.09 0.75 -0.16 -0.37
## PP.BehavInt3_PBFB 0.14 0.78 -0.13 -0.34
## PP.BehavInt4_PBFB 0.13 0.78 -0.13 -0.34
## PP.BehavInt1_VB 0.16 0.92 0.21 -0.04
## PP.BehavInt2_VB 0.09 0.89 0.20 -0.06
## PP.BehavInt3_VB 0.16 0.91 0.24 -0.01
## PP.BehavInt4_VB 0.15 0.91 0.21 -0.04
## PP.Nat_3R_VB PP.BehavInt1_GFFB PP.BehavInt2_GFFB
## PP.Nat_1_GFFB -0.67 0.92 0.89
## PP.Nat_4R_GFFB 0.39 -0.03 0.05
## PP.Nat_2R_GFFB 0.39 0.00 0.06
## PP.Nat_3R_GFFB 0.64 -0.41 -0.32
## PP.Nat_1_GFPRB -0.03 0.37 0.45
## PP.Nat_4R_GFPRB 0.60 -0.28 -0.17
## PP.Nat_2R_GFPRB 0.54 -0.22 -0.11
## PP.Nat_3R_GFPRB 0.61 -0.31 -0.23
## PP.Nat_1_CBB -0.73 0.49 0.39
## PP.Nat_4R_CBB -0.02 -0.36 -0.40
## PP.Nat_2R_CBB -0.05 -0.18 -0.23
## PP.Nat_3R_CBB -0.01 -0.21 -0.26
## PP.Nat_1_PBPB -0.43 0.02 -0.06
## PP.Nat_4R_PBPB 0.49 -0.70 -0.68
## PP.Nat_2R_PBPB 0.51 -0.82 -0.83
## PP.Nat_3R_PBPB 0.64 -0.56 -0.57
## PP.Nat_1_PBFB -0.50 0.14 0.06
## PP.Nat_4R_PBFB -0.27 0.53 0.57
## PP.Nat_2R_PBFB -0.49 0.59 0.62
## PP.Nat_3R_PBFB -0.56 0.54 0.56
## PP.Nat_1_VB -0.11 -0.07 -0.10
## PP.Nat_4R_VB 0.77 -0.68 -0.64
## PP.Nat_2R_VB 0.87 -0.67 -0.59
## PP.Nat_3R_VB 1.00 -0.63 -0.57
## PP.BehavInt1_GFFB -0.63 1.00 0.98
## PP.BehavInt2_GFFB -0.57 0.98 1.00
## PP.BehavInt3_GFFB -0.65 0.99 0.97
## PP.BehavInt4_GFFB -0.61 0.99 0.97
## PP.BehavInt1_GFPRB -0.28 -0.11 -0.15
## PP.BehavInt2_GFPRB -0.32 -0.10 -0.16
## PP.BehavInt3_GFPRB -0.35 -0.08 -0.13
## PP.BehavInt4_GFPRB -0.32 -0.08 -0.13
## PP.BehavInt1_CBB -0.71 0.39 0.31
## PP.BehavInt2_CBB -0.73 0.44 0.36
## PP.BehavInt3_CBB -0.72 0.40 0.33
## PP.BehavInt4_CBB -0.73 0.40 0.32
## PP.BehavInt1_PBPB -0.28 -0.11 -0.15
## PP.BehavInt2_PBPB -0.32 -0.10 -0.16
## PP.BehavInt3_PBPB -0.35 -0.08 -0.13
## PP.BehavInt4_PBPB -0.32 -0.08 -0.13
## PP.BehavInt1_PBFB -0.40 0.02 -0.05
## PP.BehavInt2_PBFB -0.45 0.08 0.00
## PP.BehavInt3_PBFB -0.43 0.07 0.00
## PP.BehavInt4_PBFB -0.44 0.07 0.01
## PP.BehavInt1_VB -0.18 -0.11 -0.14
## PP.BehavInt2_VB -0.18 -0.14 -0.18
## PP.BehavInt3_VB -0.15 -0.11 -0.13
## PP.BehavInt4_VB -0.18 -0.13 -0.16
## PP.BehavInt3_GFFB PP.BehavInt4_GFFB PP.BehavInt1_GFPRB
## PP.Nat_1_GFFB 0.91 0.91 -0.12
## PP.Nat_4R_GFFB -0.08 0.00 -0.76
## PP.Nat_2R_GFFB -0.04 0.03 -0.84
## PP.Nat_3R_GFFB -0.45 -0.38 -0.65
## PP.Nat_1_GFPRB 0.34 0.37 -0.17
## PP.Nat_4R_GFPRB -0.31 -0.25 -0.54
## PP.Nat_2R_GFPRB -0.26 -0.20 -0.56
## PP.Nat_3R_GFPRB -0.35 -0.27 -0.62
## PP.Nat_1_CBB 0.53 0.47 0.55
## PP.Nat_4R_CBB -0.35 -0.36 -0.04
## PP.Nat_2R_CBB -0.17 -0.17 -0.27
## PP.Nat_3R_CBB -0.20 -0.19 -0.36
## PP.Nat_1_PBPB 0.06 0.00 0.92
## PP.Nat_4R_PBPB -0.68 -0.68 0.27
## PP.Nat_2R_PBPB -0.80 -0.80 0.15
## PP.Nat_3R_PBPB -0.54 -0.52 -0.29
## PP.Nat_1_PBFB 0.18 0.11 0.86
## PP.Nat_4R_PBFB 0.52 0.53 -0.49
## PP.Nat_2R_PBFB 0.58 0.57 -0.06
## PP.Nat_3R_PBFB 0.52 0.51 0.09
## PP.Nat_1_VB -0.05 -0.10 0.84
## PP.Nat_4R_VB -0.70 -0.67 0.10
## PP.Nat_2R_VB -0.69 -0.66 -0.15
## PP.Nat_3R_VB -0.65 -0.61 -0.28
## PP.BehavInt1_GFFB 0.99 0.99 -0.11
## PP.BehavInt2_GFFB 0.97 0.97 -0.15
## PP.BehavInt3_GFFB 1.00 0.99 -0.08
## PP.BehavInt4_GFFB 0.99 1.00 -0.14
## PP.BehavInt1_GFPRB -0.08 -0.14 1.00
## PP.BehavInt2_GFPRB -0.07 -0.14 0.98
## PP.BehavInt3_GFPRB -0.04 -0.11 0.99
## PP.BehavInt4_GFPRB -0.04 -0.11 0.99
## PP.BehavInt1_CBB 0.42 0.36 0.69
## PP.BehavInt2_CBB 0.48 0.41 0.64
## PP.BehavInt3_CBB 0.44 0.37 0.67
## PP.BehavInt4_CBB 0.44 0.38 0.66
## PP.BehavInt1_PBPB -0.08 -0.14 1.00
## PP.BehavInt2_PBPB -0.07 -0.14 0.98
## PP.BehavInt3_PBPB -0.04 -0.11 0.99
## PP.BehavInt4_PBPB -0.04 -0.11 0.99
## PP.BehavInt1_PBFB 0.05 -0.01 0.95
## PP.BehavInt2_PBFB 0.11 0.04 0.92
## PP.BehavInt3_PBFB 0.10 0.03 0.94
## PP.BehavInt4_PBFB 0.10 0.04 0.93
## PP.BehavInt1_VB -0.09 -0.14 0.92
## PP.BehavInt2_VB -0.11 -0.16 0.91
## PP.BehavInt3_VB -0.09 -0.14 0.92
## PP.BehavInt4_VB -0.10 -0.16 0.92
## PP.BehavInt2_GFPRB PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB
## PP.Nat_1_GFFB -0.12 -0.10 -0.11
## PP.Nat_4R_GFFB -0.81 -0.79 -0.79
## PP.Nat_2R_GFFB -0.86 -0.86 -0.86
## PP.Nat_3R_GFFB -0.69 -0.68 -0.67
## PP.Nat_1_GFPRB -0.25 -0.19 -0.17
## PP.Nat_4R_GFPRB -0.62 -0.59 -0.57
## PP.Nat_2R_GFPRB -0.63 -0.61 -0.59
## PP.Nat_3R_GFPRB -0.67 -0.65 -0.63
## PP.Nat_1_CBB 0.60 0.58 0.56
## PP.Nat_4R_CBB -0.01 -0.04 -0.09
## PP.Nat_2R_CBB -0.22 -0.26 -0.32
## PP.Nat_3R_CBB -0.30 -0.33 -0.39
## PP.Nat_1_PBPB 0.93 0.93 0.92
## PP.Nat_4R_PBPB 0.25 0.23 0.22
## PP.Nat_2R_PBPB 0.14 0.12 0.10
## PP.Nat_3R_PBPB -0.27 -0.32 -0.33
## PP.Nat_1_PBFB 0.88 0.87 0.86
## PP.Nat_4R_PBFB -0.48 -0.46 -0.43
## PP.Nat_2R_PBFB -0.05 -0.02 0.00
## PP.Nat_3R_PBFB 0.07 0.13 0.16
## PP.Nat_1_VB 0.83 0.85 0.86
## PP.Nat_4R_VB 0.05 0.04 0.07
## PP.Nat_2R_VB -0.20 -0.21 -0.18
## PP.Nat_3R_VB -0.32 -0.35 -0.32
## PP.BehavInt1_GFFB -0.10 -0.08 -0.08
## PP.BehavInt2_GFFB -0.16 -0.13 -0.13
## PP.BehavInt3_GFFB -0.07 -0.04 -0.04
## PP.BehavInt4_GFFB -0.14 -0.11 -0.11
## PP.BehavInt1_GFPRB 0.98 0.99 0.99
## PP.BehavInt2_GFPRB 1.00 0.98 0.98
## PP.BehavInt3_GFPRB 0.98 1.00 0.99
## PP.BehavInt4_GFPRB 0.98 0.99 1.00
## PP.BehavInt1_CBB 0.72 0.71 0.69
## PP.BehavInt2_CBB 0.69 0.67 0.65
## PP.BehavInt3_CBB 0.70 0.70 0.67
## PP.BehavInt4_CBB 0.70 0.70 0.66
## PP.BehavInt1_PBPB 0.98 0.99 0.99
## PP.BehavInt2_PBPB 1.00 0.98 0.98
## PP.BehavInt3_PBPB 0.98 1.00 0.99
## PP.BehavInt4_PBPB 0.98 0.99 1.00
## PP.BehavInt1_PBFB 0.96 0.95 0.95
## PP.BehavInt2_PBFB 0.95 0.92 0.91
## PP.BehavInt3_PBFB 0.94 0.95 0.94
## PP.BehavInt4_PBFB 0.94 0.95 0.94
## PP.BehavInt1_VB 0.91 0.92 0.93
## PP.BehavInt2_VB 0.91 0.92 0.92
## PP.BehavInt3_VB 0.89 0.92 0.93
## PP.BehavInt4_VB 0.91 0.92 0.93
## PP.BehavInt1_CBB PP.BehavInt2_CBB PP.BehavInt3_CBB
## PP.Nat_1_GFFB 0.37 0.43 0.39
## PP.Nat_4R_GFFB -0.77 -0.75 -0.77
## PP.Nat_2R_GFFB -0.75 -0.72 -0.74
## PP.Nat_3R_GFFB -0.83 -0.84 -0.84
## PP.Nat_1_GFPRB -0.24 -0.23 -0.24
## PP.Nat_4R_GFPRB -0.79 -0.81 -0.80
## PP.Nat_2R_GFPRB -0.73 -0.76 -0.74
## PP.Nat_3R_GFPRB -0.83 -0.85 -0.85
## PP.Nat_1_CBB 0.93 0.96 0.94
## PP.Nat_4R_CBB 0.17 0.17 0.16
## PP.Nat_2R_CBB 0.08 0.11 0.09
## PP.Nat_3R_CBB -0.06 -0.03 -0.04
## PP.Nat_1_PBPB 0.74 0.72 0.73
## PP.Nat_4R_PBPB -0.24 -0.26 -0.24
## PP.Nat_2R_PBPB -0.28 -0.30 -0.28
## PP.Nat_3R_PBPB -0.49 -0.47 -0.48
## PP.Nat_1_PBFB 0.88 0.86 0.87
## PP.Nat_4R_PBFB -0.22 -0.19 -0.20
## PP.Nat_2R_PBFB 0.12 0.13 0.12
## PP.Nat_3R_PBFB 0.17 0.18 0.17
## PP.Nat_1_VB 0.45 0.42 0.43
## PP.Nat_4R_VB -0.54 -0.58 -0.57
## PP.Nat_2R_VB -0.67 -0.71 -0.69
## PP.Nat_3R_VB -0.71 -0.73 -0.72
## PP.BehavInt1_GFFB 0.39 0.44 0.40
## PP.BehavInt2_GFFB 0.31 0.36 0.33
## PP.BehavInt3_GFFB 0.42 0.48 0.44
## PP.BehavInt4_GFFB 0.36 0.41 0.37
## PP.BehavInt1_GFPRB 0.69 0.64 0.67
## PP.BehavInt2_GFPRB 0.72 0.69 0.70
## PP.BehavInt3_GFPRB 0.71 0.67 0.70
## PP.BehavInt4_GFPRB 0.69 0.65 0.67
## PP.BehavInt1_CBB 1.00 0.99 0.99
## PP.BehavInt2_CBB 0.99 1.00 0.99
## PP.BehavInt3_CBB 0.99 0.99 1.00
## PP.BehavInt4_CBB 0.99 0.99 0.99
## PP.BehavInt1_PBPB 0.69 0.64 0.67
## PP.BehavInt2_PBPB 0.72 0.69 0.70
## PP.BehavInt3_PBPB 0.71 0.67 0.70
## PP.BehavInt4_PBPB 0.69 0.65 0.67
## PP.BehavInt1_PBFB 0.81 0.78 0.80
## PP.BehavInt2_PBFB 0.83 0.81 0.82
## PP.BehavInt3_PBFB 0.81 0.78 0.80
## PP.BehavInt4_PBFB 0.82 0.79 0.81
## PP.BehavInt1_VB 0.49 0.46 0.48
## PP.BehavInt2_VB 0.50 0.48 0.48
## PP.BehavInt3_VB 0.48 0.43 0.46
## PP.BehavInt4_VB 0.49 0.45 0.47
## PP.BehavInt4_CBB PP.BehavInt1_PBPB PP.BehavInt2_PBPB
## PP.Nat_1_GFFB 0.40 -0.12 -0.12
## PP.Nat_4R_GFFB -0.75 -0.76 -0.81
## PP.Nat_2R_GFFB -0.74 -0.84 -0.86
## PP.Nat_3R_GFFB -0.84 -0.65 -0.69
## PP.Nat_1_GFPRB -0.23 -0.17 -0.25
## PP.Nat_4R_GFPRB -0.79 -0.54 -0.62
## PP.Nat_2R_GFPRB -0.74 -0.56 -0.63
## PP.Nat_3R_GFPRB -0.84 -0.62 -0.67
## PP.Nat_1_CBB 0.94 0.55 0.60
## PP.Nat_4R_CBB 0.18 -0.04 -0.01
## PP.Nat_2R_CBB 0.09 -0.27 -0.22
## PP.Nat_3R_CBB -0.04 -0.36 -0.30
## PP.Nat_1_PBPB 0.74 0.92 0.93
## PP.Nat_4R_PBPB -0.23 0.27 0.25
## PP.Nat_2R_PBPB -0.28 0.15 0.14
## PP.Nat_3R_PBPB -0.47 -0.29 -0.27
## PP.Nat_1_PBFB 0.86 0.86 0.88
## PP.Nat_4R_PBFB -0.21 -0.49 -0.48
## PP.Nat_2R_PBFB 0.12 -0.06 -0.05
## PP.Nat_3R_PBFB 0.17 0.09 0.07
## PP.Nat_1_VB 0.42 0.84 0.83
## PP.Nat_4R_VB -0.55 0.10 0.05
## PP.Nat_2R_VB -0.70 -0.15 -0.20
## PP.Nat_3R_VB -0.73 -0.28 -0.32
## PP.BehavInt1_GFFB 0.40 -0.11 -0.10
## PP.BehavInt2_GFFB 0.32 -0.15 -0.16
## PP.BehavInt3_GFFB 0.44 -0.08 -0.07
## PP.BehavInt4_GFFB 0.38 -0.14 -0.14
## PP.BehavInt1_GFPRB 0.66 1.00 0.98
## PP.BehavInt2_GFPRB 0.70 0.98 1.00
## PP.BehavInt3_GFPRB 0.70 0.99 0.98
## PP.BehavInt4_GFPRB 0.66 0.99 0.98
## PP.BehavInt1_CBB 0.99 0.69 0.72
## PP.BehavInt2_CBB 0.99 0.64 0.69
## PP.BehavInt3_CBB 0.99 0.67 0.70
## PP.BehavInt4_CBB 1.00 0.66 0.70
## PP.BehavInt1_PBPB 0.66 1.00 0.98
## PP.BehavInt2_PBPB 0.70 0.98 1.00
## PP.BehavInt3_PBPB 0.70 0.99 0.98
## PP.BehavInt4_PBPB 0.66 0.99 0.98
## PP.BehavInt1_PBFB 0.79 0.95 0.96
## PP.BehavInt2_PBFB 0.82 0.92 0.95
## PP.BehavInt3_PBFB 0.80 0.94 0.94
## PP.BehavInt4_PBFB 0.81 0.93 0.94
## PP.BehavInt1_VB 0.48 0.92 0.91
## PP.BehavInt2_VB 0.48 0.91 0.91
## PP.BehavInt3_VB 0.46 0.92 0.89
## PP.BehavInt4_VB 0.47 0.92 0.91
## PP.BehavInt3_PBPB PP.BehavInt4_PBPB PP.BehavInt1_PBFB
## PP.Nat_1_GFFB -0.10 -0.11 -0.01
## PP.Nat_4R_GFFB -0.79 -0.79 -0.87
## PP.Nat_2R_GFFB -0.86 -0.86 -0.88
## PP.Nat_3R_GFFB -0.68 -0.67 -0.79
## PP.Nat_1_GFPRB -0.19 -0.17 -0.29
## PP.Nat_4R_GFPRB -0.59 -0.57 -0.73
## PP.Nat_2R_GFPRB -0.61 -0.59 -0.74
## PP.Nat_3R_GFPRB -0.65 -0.63 -0.77
## PP.Nat_1_CBB 0.58 0.56 0.70
## PP.Nat_4R_CBB -0.04 -0.09 0.01
## PP.Nat_2R_CBB -0.26 -0.32 -0.16
## PP.Nat_3R_CBB -0.33 -0.39 -0.25
## PP.Nat_1_PBPB 0.93 0.92 0.92
## PP.Nat_4R_PBPB 0.23 0.22 0.10
## PP.Nat_2R_PBPB 0.12 0.10 0.02
## PP.Nat_3R_PBPB -0.32 -0.33 -0.32
## PP.Nat_1_PBFB 0.87 0.86 0.94
## PP.Nat_4R_PBFB -0.46 -0.43 -0.43
## PP.Nat_2R_PBFB -0.02 0.00 -0.03
## PP.Nat_3R_PBFB 0.13 0.16 0.10
## PP.Nat_1_VB 0.85 0.86 0.78
## PP.Nat_4R_VB 0.04 0.07 -0.11
## PP.Nat_2R_VB -0.21 -0.18 -0.32
## PP.Nat_3R_VB -0.35 -0.32 -0.40
## PP.BehavInt1_GFFB -0.08 -0.08 0.02
## PP.BehavInt2_GFFB -0.13 -0.13 -0.05
## PP.BehavInt3_GFFB -0.04 -0.04 0.05
## PP.BehavInt4_GFFB -0.11 -0.11 -0.01
## PP.BehavInt1_GFPRB 0.99 0.99 0.95
## PP.BehavInt2_GFPRB 0.98 0.98 0.96
## PP.BehavInt3_GFPRB 1.00 0.99 0.95
## PP.BehavInt4_GFPRB 0.99 1.00 0.95
## PP.BehavInt1_CBB 0.71 0.69 0.81
## PP.BehavInt2_CBB 0.67 0.65 0.78
## PP.BehavInt3_CBB 0.70 0.67 0.80
## PP.BehavInt4_CBB 0.70 0.66 0.79
## PP.BehavInt1_PBPB 0.99 0.99 0.95
## PP.BehavInt2_PBPB 0.98 0.98 0.96
## PP.BehavInt3_PBPB 1.00 0.99 0.95
## PP.BehavInt4_PBPB 0.99 1.00 0.95
## PP.BehavInt1_PBFB 0.95 0.95 1.00
## PP.BehavInt2_PBFB 0.92 0.91 0.99
## PP.BehavInt3_PBFB 0.95 0.94 0.99
## PP.BehavInt4_PBFB 0.95 0.94 0.99
## PP.BehavInt1_VB 0.92 0.93 0.86
## PP.BehavInt2_VB 0.92 0.92 0.87
## PP.BehavInt3_VB 0.92 0.93 0.85
## PP.BehavInt4_VB 0.92 0.93 0.85
## PP.BehavInt2_PBFB PP.BehavInt3_PBFB PP.BehavInt4_PBFB
## PP.Nat_1_GFFB 0.06 0.04 0.05
## PP.Nat_4R_GFFB -0.88 -0.87 -0.87
## PP.Nat_2R_GFFB -0.86 -0.88 -0.87
## PP.Nat_3R_GFFB -0.82 -0.80 -0.81
## PP.Nat_1_GFPRB -0.29 -0.24 -0.24
## PP.Nat_4R_GFPRB -0.77 -0.72 -0.73
## PP.Nat_2R_GFPRB -0.78 -0.75 -0.76
## PP.Nat_3R_GFPRB -0.82 -0.78 -0.80
## PP.Nat_1_CBB 0.75 0.70 0.72
## PP.Nat_4R_CBB 0.04 -0.02 0.00
## PP.Nat_2R_CBB -0.11 -0.18 -0.15
## PP.Nat_3R_CBB -0.19 -0.27 -0.24
## PP.Nat_1_PBPB 0.93 0.92 0.93
## PP.Nat_4R_PBPB 0.08 0.06 0.07
## PP.Nat_2R_PBPB 0.00 -0.02 0.00
## PP.Nat_3R_PBPB -0.31 -0.36 -0.35
## PP.Nat_1_PBFB 0.95 0.95 0.95
## PP.Nat_4R_PBFB -0.42 -0.40 -0.41
## PP.Nat_2R_PBFB -0.03 0.00 -0.02
## PP.Nat_3R_PBFB 0.09 0.14 0.13
## PP.Nat_1_VB 0.75 0.78 0.78
## PP.Nat_4R_VB -0.16 -0.13 -0.13
## PP.Nat_2R_VB -0.37 -0.34 -0.34
## PP.Nat_3R_VB -0.45 -0.43 -0.44
## PP.BehavInt1_GFFB 0.08 0.07 0.07
## PP.BehavInt2_GFFB 0.00 0.00 0.01
## PP.BehavInt3_GFFB 0.11 0.10 0.10
## PP.BehavInt4_GFFB 0.04 0.03 0.04
## PP.BehavInt1_GFPRB 0.92 0.94 0.93
## PP.BehavInt2_GFPRB 0.95 0.94 0.94
## PP.BehavInt3_GFPRB 0.92 0.95 0.95
## PP.BehavInt4_GFPRB 0.91 0.94 0.94
## PP.BehavInt1_CBB 0.83 0.81 0.82
## PP.BehavInt2_CBB 0.81 0.78 0.79
## PP.BehavInt3_CBB 0.82 0.80 0.81
## PP.BehavInt4_CBB 0.82 0.80 0.81
## PP.BehavInt1_PBPB 0.92 0.94 0.93
## PP.BehavInt2_PBPB 0.95 0.94 0.94
## PP.BehavInt3_PBPB 0.92 0.95 0.95
## PP.BehavInt4_PBPB 0.91 0.94 0.94
## PP.BehavInt1_PBFB 0.99 0.99 0.99
## PP.BehavInt2_PBFB 1.00 0.98 0.98
## PP.BehavInt3_PBFB 0.98 1.00 1.00
## PP.BehavInt4_PBFB 0.98 1.00 1.00
## PP.BehavInt1_VB 0.83 0.86 0.85
## PP.BehavInt2_VB 0.86 0.86 0.86
## PP.BehavInt3_VB 0.81 0.86 0.85
## PP.BehavInt4_VB 0.83 0.86 0.85
## PP.BehavInt1_VB PP.BehavInt2_VB PP.BehavInt3_VB
## PP.Nat_1_GFFB -0.14 -0.17 -0.15
## PP.Nat_4R_GFFB -0.67 -0.71 -0.66
## PP.Nat_2R_GFFB -0.72 -0.72 -0.73
## PP.Nat_3R_GFFB -0.54 -0.56 -0.52
## PP.Nat_1_GFPRB -0.08 -0.18 -0.05
## PP.Nat_4R_GFPRB -0.47 -0.55 -0.44
## PP.Nat_2R_GFPRB -0.53 -0.62 -0.50
## PP.Nat_3R_GFPRB -0.54 -0.60 -0.50
## PP.Nat_1_CBB 0.37 0.40 0.34
## PP.Nat_4R_CBB -0.25 -0.21 -0.28
## PP.Nat_2R_CBB -0.46 -0.39 -0.50
## PP.Nat_3R_CBB -0.49 -0.43 -0.53
## PP.Nat_1_PBPB 0.86 0.88 0.85
## PP.Nat_4R_PBPB 0.28 0.35 0.28
## PP.Nat_2R_PBPB 0.10 0.19 0.11
## PP.Nat_3R_PBPB -0.28 -0.14 -0.27
## PP.Nat_1_PBFB 0.74 0.77 0.73
## PP.Nat_4R_PBFB -0.42 -0.43 -0.40
## PP.Nat_2R_PBFB 0.03 -0.04 0.03
## PP.Nat_3R_PBFB 0.16 0.09 0.16
## PP.Nat_1_VB 0.92 0.89 0.91
## PP.Nat_4R_VB 0.21 0.20 0.24
## PP.Nat_2R_VB -0.04 -0.06 -0.01
## PP.Nat_3R_VB -0.18 -0.18 -0.15
## PP.BehavInt1_GFFB -0.11 -0.14 -0.11
## PP.BehavInt2_GFFB -0.14 -0.18 -0.13
## PP.BehavInt3_GFFB -0.09 -0.11 -0.09
## PP.BehavInt4_GFFB -0.14 -0.16 -0.14
## PP.BehavInt1_GFPRB 0.92 0.91 0.92
## PP.BehavInt2_GFPRB 0.91 0.91 0.89
## PP.BehavInt3_GFPRB 0.92 0.92 0.92
## PP.BehavInt4_GFPRB 0.93 0.92 0.93
## PP.BehavInt1_CBB 0.49 0.50 0.48
## PP.BehavInt2_CBB 0.46 0.48 0.43
## PP.BehavInt3_CBB 0.48 0.48 0.46
## PP.BehavInt4_CBB 0.48 0.48 0.46
## PP.BehavInt1_PBPB 0.92 0.91 0.92
## PP.BehavInt2_PBPB 0.91 0.91 0.89
## PP.BehavInt3_PBPB 0.92 0.92 0.92
## PP.BehavInt4_PBPB 0.93 0.92 0.93
## PP.BehavInt1_PBFB 0.86 0.87 0.85
## PP.BehavInt2_PBFB 0.83 0.86 0.81
## PP.BehavInt3_PBFB 0.86 0.86 0.86
## PP.BehavInt4_PBFB 0.85 0.86 0.85
## PP.BehavInt1_VB 1.00 0.97 0.99
## PP.BehavInt2_VB 0.97 1.00 0.95
## PP.BehavInt3_VB 0.99 0.95 1.00
## PP.BehavInt4_VB 0.99 0.97 0.99
## PP.BehavInt4_VB
## PP.Nat_1_GFFB -0.15
## PP.Nat_4R_GFFB -0.68
## PP.Nat_2R_GFFB -0.73
## PP.Nat_3R_GFFB -0.53
## PP.Nat_1_GFPRB -0.08
## PP.Nat_4R_GFPRB -0.48
## PP.Nat_2R_GFPRB -0.53
## PP.Nat_3R_GFPRB -0.53
## PP.Nat_1_CBB 0.36
## PP.Nat_4R_CBB -0.26
## PP.Nat_2R_CBB -0.46
## PP.Nat_3R_CBB -0.50
## PP.Nat_1_PBPB 0.86
## PP.Nat_4R_PBPB 0.29
## PP.Nat_2R_PBPB 0.13
## PP.Nat_3R_PBPB -0.26
## PP.Nat_1_PBFB 0.73
## PP.Nat_4R_PBFB -0.40
## PP.Nat_2R_PBFB 0.04
## PP.Nat_3R_PBFB 0.15
## PP.Nat_1_VB 0.91
## PP.Nat_4R_VB 0.21
## PP.Nat_2R_VB -0.04
## PP.Nat_3R_VB -0.18
## PP.BehavInt1_GFFB -0.13
## PP.BehavInt2_GFFB -0.16
## PP.BehavInt3_GFFB -0.10
## PP.BehavInt4_GFFB -0.16
## PP.BehavInt1_GFPRB 0.92
## PP.BehavInt2_GFPRB 0.91
## PP.BehavInt3_GFPRB 0.92
## PP.BehavInt4_GFPRB 0.93
## PP.BehavInt1_CBB 0.49
## PP.BehavInt2_CBB 0.45
## PP.BehavInt3_CBB 0.47
## PP.BehavInt4_CBB 0.47
## PP.BehavInt1_PBPB 0.92
## PP.BehavInt2_PBPB 0.91
## PP.BehavInt3_PBPB 0.92
## PP.BehavInt4_PBPB 0.93
## PP.BehavInt1_PBFB 0.85
## PP.BehavInt2_PBFB 0.83
## PP.BehavInt3_PBFB 0.86
## PP.BehavInt4_PBFB 0.85
## PP.BehavInt1_VB 0.99
## PP.BehavInt2_VB 0.97
## PP.BehavInt3_VB 0.99
## PP.BehavInt4_VB 1.00
##
## n= 48
##
##
## P
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 0.9750 0.9035 0.0055
## PP.Nat_4R_GFFB 0.9750 0.0000 0.0000
## PP.Nat_2R_GFFB 0.9035 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0055 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0009 0.0180 0.1291 0.4124
## PP.Nat_4R_GFPRB 0.0893 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.1703 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0475 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0006 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0741 0.8075 0.6245 0.6559
## PP.Nat_2R_CBB 0.4533 0.4298 0.0957 0.4871
## PP.Nat_3R_CBB 0.3388 0.2605 0.0315 0.2723
## PP.Nat_1_PBPB 0.9904 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0000 0.9023 0.9301 0.1403
## PP.Nat_2R_PBPB 0.0000 0.8597 0.9435 0.0596
## PP.Nat_3R_PBPB 0.0000 0.1943 0.0269 0.0032
## PP.Nat_1_PBFB 0.4227 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0803 0.0620 0.8538
## PP.Nat_2R_PBFB 0.0000 0.9014 0.4460 0.0799
## PP.Nat_3R_PBFB 0.0000 0.6431 0.2172 0.0243
## PP.Nat_1_VB 0.4114 0.0000 0.0000 0.0003
## PP.Nat_4R_VB 0.0000 0.0688 0.1987 0.0003
## PP.Nat_2R_VB 0.0000 0.0065 0.0201 0.0000
## PP.Nat_3R_VB 0.0000 0.0063 0.0060 0.0000
## PP.BehavInt1_GFFB 0.0000 0.8166 0.9957 0.0035
## PP.BehavInt2_GFFB 0.0000 0.7427 0.6654 0.0247
## PP.BehavInt3_GFFB 0.0000 0.6037 0.7974 0.0015
## PP.BehavInt4_GFFB 0.0000 0.9813 0.8575 0.0074
## PP.BehavInt1_GFPRB 0.4073 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.4043 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.4905 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.4694 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0089 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0024 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0059 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0051 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.4073 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.4043 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.4905 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.4694 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.9372 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.6968 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.7981 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.7596 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.3462 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.2443 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.3179 0.0000 0.0000 0.0001
## PP.BehavInt4_VB 0.2993 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Nat_1_GFFB 0.0009 0.0893 0.1703
## PP.Nat_4R_GFFB 0.0180 0.0000 0.0000
## PP.Nat_2R_GFFB 0.1291 0.0000 0.0000
## PP.Nat_3R_GFFB 0.4124 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0006 0.0019
## PP.Nat_4R_GFPRB 0.0006 0.0000
## PP.Nat_2R_GFPRB 0.0019 0.0000
## PP.Nat_3R_GFPRB 0.0306 0.0000 0.0000
## PP.Nat_1_CBB 0.0750 0.0000 0.0000
## PP.Nat_4R_CBB 0.0012 0.7360 0.5989
## PP.Nat_2R_CBB 0.0010 0.5215 0.5202
## PP.Nat_3R_CBB 0.0025 0.8450 0.7837
## PP.Nat_1_PBPB 0.0563 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0453 0.2776 0.8376
## PP.Nat_2R_PBPB 0.0150 0.2990 0.7528
## PP.Nat_3R_PBPB 0.0006 0.2698 0.6346
## PP.Nat_1_PBFB 0.0213 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.2754 0.0959
## PP.Nat_2R_PBFB 0.0002 0.5809 0.8932
## PP.Nat_3R_PBFB 0.0000 0.6391 0.9673
## PP.Nat_1_VB 0.5501 0.0009 0.0002
## PP.Nat_4R_VB 0.6513 0.0003 0.0057
## PP.Nat_2R_VB 0.8906 0.0000 0.0000
## PP.Nat_3R_VB 0.8300 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0088 0.0554 0.1364
## PP.BehavInt2_GFFB 0.0013 0.2389 0.4398
## PP.BehavInt3_GFFB 0.0167 0.0294 0.0769
## PP.BehavInt4_GFFB 0.0091 0.0803 0.1765
## PP.BehavInt1_GFPRB 0.2613 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0841 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.1876 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.2464 0.0000 0.0000
## PP.BehavInt1_CBB 0.1057 0.0000 0.0000
## PP.BehavInt2_CBB 0.1086 0.0000 0.0000
## PP.BehavInt3_CBB 0.0990 0.0000 0.0000
## PP.BehavInt4_CBB 0.1116 0.0000 0.0000
## PP.BehavInt1_PBPB 0.2613 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0841 0.0000 0.0000
## PP.BehavInt3_PBPB 0.1876 0.0000 0.0000
## PP.BehavInt4_PBPB 0.2464 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0463 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0433 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0975 0.0000 0.0000
## PP.BehavInt4_PBFB 0.1067 0.0000 0.0000
## PP.BehavInt1_VB 0.5729 0.0007 0.0001
## PP.BehavInt2_VB 0.2083 0.0000 0.0000
## PP.BehavInt3_VB 0.7437 0.0019 0.0003
## PP.BehavInt4_VB 0.5869 0.0006 0.0001
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Nat_1_GFFB 0.0475 0.0006 0.0741 0.4533
## PP.Nat_4R_GFFB 0.0000 0.0000 0.8075 0.4298
## PP.Nat_2R_GFFB 0.0000 0.0000 0.6245 0.0957
## PP.Nat_3R_GFFB 0.0000 0.0000 0.6559 0.4871
## PP.Nat_1_GFPRB 0.0306 0.0750 0.0012 0.0010
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.7360 0.5215
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.5989 0.5202
## PP.Nat_3R_GFPRB 0.0000 0.5609 0.6687
## PP.Nat_1_CBB 0.0000 0.2001 0.2714
## PP.Nat_4R_CBB 0.5609 0.2001 0.0000
## PP.Nat_2R_CBB 0.6687 0.2714 0.0000
## PP.Nat_3R_CBB 0.7608 0.7653 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.9691 0.2569
## PP.Nat_4R_PBPB 0.7031 0.0575 0.0640 0.5041
## PP.Nat_2R_PBPB 0.4313 0.0252 0.0043 0.0164
## PP.Nat_3R_PBPB 0.0761 0.0067 0.0523 0.0066
## PP.Nat_1_PBFB 0.0000 0.0000 0.5080 0.8660
## PP.Nat_4R_PBFB 0.2066 0.2726 0.0001 0.0154
## PP.Nat_2R_PBFB 0.5951 0.3879 0.0000 0.0000
## PP.Nat_3R_PBFB 0.3703 0.3154 0.0001 0.0000
## PP.Nat_1_VB 0.0001 0.0107 0.0674 0.0019
## PP.Nat_4R_VB 0.0013 0.0000 0.9493 0.2787
## PP.Nat_2R_VB 0.0000 0.0000 0.9619 0.5037
## PP.Nat_3R_VB 0.0000 0.0000 0.9144 0.7186
## PP.BehavInt1_GFFB 0.0300 0.0004 0.0127 0.2145
## PP.BehavInt2_GFFB 0.1175 0.0055 0.0045 0.1099
## PP.BehavInt3_GFFB 0.0159 0.0001 0.0151 0.2490
## PP.BehavInt4_GFFB 0.0639 0.0009 0.0128 0.2549
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.7698 0.0645
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.9614 0.1375
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.7846 0.0764
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.5295 0.0289
## PP.BehavInt1_CBB 0.0000 0.0000 0.2535 0.5990
## PP.BehavInt2_CBB 0.0000 0.0000 0.2586 0.4740
## PP.BehavInt3_CBB 0.0000 0.0000 0.2674 0.5351
## PP.BehavInt4_CBB 0.0000 0.0000 0.2258 0.5242
## PP.BehavInt1_PBPB 0.0000 0.0000 0.7698 0.0645
## PP.BehavInt2_PBPB 0.0000 0.0000 0.9614 0.1375
## PP.BehavInt3_PBPB 0.0000 0.0000 0.7846 0.0764
## PP.BehavInt4_PBPB 0.0000 0.0000 0.5295 0.0289
## PP.BehavInt1_PBFB 0.0000 0.0000 0.9649 0.2856
## PP.BehavInt2_PBFB 0.0000 0.0000 0.8031 0.4689
## PP.BehavInt3_PBFB 0.0000 0.0000 0.8894 0.2195
## PP.BehavInt4_PBFB 0.0000 0.0000 0.9876 0.2934
## PP.BehavInt1_VB 0.0000 0.0094 0.0889 0.0010
## PP.BehavInt2_VB 0.0000 0.0049 0.1611 0.0069
## PP.BehavInt3_VB 0.0003 0.0169 0.0542 0.0003
## PP.BehavInt4_VB 0.0001 0.0126 0.0704 0.0009
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Nat_1_GFFB 0.3388 0.9904 0.0000 0.0000
## PP.Nat_4R_GFFB 0.2605 0.0000 0.9023 0.8597
## PP.Nat_2R_GFFB 0.0315 0.0000 0.9301 0.9435
## PP.Nat_3R_GFFB 0.2723 0.0000 0.1403 0.0596
## PP.Nat_1_GFPRB 0.0025 0.0563 0.0453 0.0150
## PP.Nat_4R_GFPRB 0.8450 0.0000 0.2776 0.2990
## PP.Nat_2R_GFPRB 0.7837 0.0000 0.8376 0.7528
## PP.Nat_3R_GFPRB 0.7608 0.0000 0.7031 0.4313
## PP.Nat_1_CBB 0.7653 0.0000 0.0575 0.0252
## PP.Nat_4R_CBB 0.0000 0.9691 0.0640 0.0043
## PP.Nat_2R_CBB 0.0000 0.2569 0.5041 0.0164
## PP.Nat_3R_CBB 0.0927 0.6270 0.0167
## PP.Nat_1_PBPB 0.0927 0.0980 0.4360
## PP.Nat_4R_PBPB 0.6270 0.0980 0.0000
## PP.Nat_2R_PBPB 0.0167 0.4360 0.0000
## PP.Nat_3R_PBPB 0.0021 0.1179 0.0000 0.0000
## PP.Nat_1_PBFB 0.4838 0.0000 0.8267 0.9185
## PP.Nat_4R_PBFB 0.0491 0.0005 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.7899 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.6341 0.0000 0.0000
## PP.Nat_1_VB 0.0006 0.0000 0.0523 0.4780
## PP.Nat_4R_VB 0.4204 0.8877 0.0000 0.0000
## PP.Nat_2R_VB 0.7310 0.0361 0.0000 0.0000
## PP.Nat_3R_VB 0.9568 0.0026 0.0004 0.0002
## PP.BehavInt1_GFFB 0.1548 0.8805 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0694 0.7045 0.0000 0.0000
## PP.BehavInt3_GFFB 0.1776 0.6909 0.0000 0.0000
## PP.BehavInt4_GFFB 0.1873 0.9945 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0124 0.0000 0.0679 0.3167
## PP.BehavInt2_GFPRB 0.0387 0.0000 0.0911 0.3541
## PP.BehavInt3_GFPRB 0.0208 0.0000 0.1151 0.4094
## PP.BehavInt4_GFPRB 0.0068 0.0000 0.1264 0.4930
## PP.BehavInt1_CBB 0.6880 0.0000 0.1056 0.0577
## PP.BehavInt2_CBB 0.8455 0.0000 0.0798 0.0358
## PP.BehavInt3_CBB 0.7641 0.0000 0.0977 0.0562
## PP.BehavInt4_CBB 0.8005 0.0000 0.1120 0.0582
## PP.BehavInt1_PBPB 0.0124 0.0000 0.0679 0.3167
## PP.BehavInt2_PBPB 0.0387 0.0000 0.0911 0.3541
## PP.BehavInt3_PBPB 0.0208 0.0000 0.1151 0.4094
## PP.BehavInt4_PBPB 0.0068 0.0000 0.1264 0.4930
## PP.BehavInt1_PBFB 0.0878 0.0000 0.4969 0.8688
## PP.BehavInt2_PBFB 0.1924 0.0000 0.5769 0.9918
## PP.BehavInt3_PBFB 0.0679 0.0000 0.6994 0.9038
## PP.BehavInt4_PBFB 0.0990 0.0000 0.6436 0.9847
## PP.BehavInt1_VB 0.0004 0.0000 0.0510 0.4804
## PP.BehavInt2_VB 0.0025 0.0000 0.0139 0.1995
## PP.BehavInt3_VB 0.0000 0.0000 0.0554 0.4538
## PP.BehavInt4_VB 0.0003 0.0000 0.0495 0.3842
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Nat_1_GFFB 0.0000 0.4227 0.0000 0.0000
## PP.Nat_4R_GFFB 0.1943 0.0000 0.0803 0.9014
## PP.Nat_2R_GFFB 0.0269 0.0000 0.0620 0.4460
## PP.Nat_3R_GFFB 0.0032 0.0000 0.8538 0.0799
## PP.Nat_1_GFPRB 0.0006 0.0213 0.0000 0.0002
## PP.Nat_4R_GFPRB 0.2698 0.0000 0.2754 0.5809
## PP.Nat_2R_GFPRB 0.6346 0.0000 0.0959 0.8932
## PP.Nat_3R_GFPRB 0.0761 0.0000 0.2066 0.5951
## PP.Nat_1_CBB 0.0067 0.0000 0.2726 0.3879
## PP.Nat_4R_CBB 0.0523 0.5080 0.0001 0.0000
## PP.Nat_2R_CBB 0.0066 0.8660 0.0154 0.0000
## PP.Nat_3R_CBB 0.0021 0.4838 0.0491 0.0000
## PP.Nat_1_PBPB 0.1179 0.0000 0.0005 0.7899
## PP.Nat_4R_PBPB 0.0000 0.8267 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.9185 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0468 0.0037 0.0000
## PP.Nat_1_PBFB 0.0468 0.0016 0.6806
## PP.Nat_4R_PBFB 0.0037 0.0016 0.0000
## PP.Nat_2R_PBFB 0.0000 0.6806 0.0000
## PP.Nat_3R_PBFB 0.0000 0.7361 0.0000 0.0000
## PP.Nat_1_VB 0.1208 0.0000 0.0025 0.9593
## PP.Nat_4R_VB 0.0003 0.1289 0.0002 0.0000
## PP.Nat_2R_VB 0.0003 0.0026 0.0252 0.0004
## PP.Nat_3R_VB 0.0000 0.0003 0.0650 0.0005
## PP.BehavInt1_GFFB 0.0000 0.3289 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.6913 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.2283 0.0002 0.0000
## PP.BehavInt4_GFFB 0.0002 0.4443 0.0001 0.0000
## PP.BehavInt1_GFPRB 0.0423 0.0000 0.0004 0.7019
## PP.BehavInt2_GFPRB 0.0633 0.0000 0.0005 0.7404
## PP.BehavInt3_GFPRB 0.0281 0.0000 0.0010 0.8750
## PP.BehavInt4_GFPRB 0.0240 0.0000 0.0022 0.9902
## PP.BehavInt1_CBB 0.0004 0.0000 0.1419 0.4328
## PP.BehavInt2_CBB 0.0008 0.0000 0.1978 0.3701
## PP.BehavInt3_CBB 0.0006 0.0000 0.1733 0.4005
## PP.BehavInt4_CBB 0.0007 0.0000 0.1473 0.4306
## PP.BehavInt1_PBPB 0.0423 0.0000 0.0004 0.7019
## PP.BehavInt2_PBPB 0.0633 0.0000 0.0005 0.7404
## PP.BehavInt3_PBPB 0.0281 0.0000 0.0010 0.8750
## PP.BehavInt4_PBPB 0.0240 0.0000 0.0022 0.9902
## PP.BehavInt1_PBFB 0.0245 0.0000 0.0022 0.8417
## PP.BehavInt2_PBFB 0.0334 0.0000 0.0031 0.8623
## PP.BehavInt3_PBFB 0.0117 0.0000 0.0045 0.9996
## PP.BehavInt4_PBFB 0.0144 0.0000 0.0037 0.9073
## PP.BehavInt1_VB 0.0576 0.0000 0.0032 0.8177
## PP.BehavInt2_VB 0.3288 0.0000 0.0023 0.8034
## PP.BehavInt3_VB 0.0682 0.0000 0.0047 0.8596
## PP.BehavInt4_VB 0.0770 0.0000 0.0051 0.8119
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Nat_1_GFFB 0.0000 0.4114 0.0000 0.0000
## PP.Nat_4R_GFFB 0.6431 0.0000 0.0688 0.0065
## PP.Nat_2R_GFFB 0.2172 0.0000 0.1987 0.0201
## PP.Nat_3R_GFFB 0.0243 0.0003 0.0003 0.0000
## PP.Nat_1_GFPRB 0.0000 0.5501 0.6513 0.8906
## PP.Nat_4R_GFPRB 0.6391 0.0009 0.0003 0.0000
## PP.Nat_2R_GFPRB 0.9673 0.0002 0.0057 0.0000
## PP.Nat_3R_GFPRB 0.3703 0.0001 0.0013 0.0000
## PP.Nat_1_CBB 0.3154 0.0107 0.0000 0.0000
## PP.Nat_4R_CBB 0.0001 0.0674 0.9493 0.9619
## PP.Nat_2R_CBB 0.0000 0.0019 0.2787 0.5037
## PP.Nat_3R_CBB 0.0000 0.0006 0.4204 0.7310
## PP.Nat_1_PBPB 0.6341 0.0000 0.8877 0.0361
## PP.Nat_4R_PBPB 0.0000 0.0523 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.4780 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.1208 0.0003 0.0003
## PP.Nat_1_PBFB 0.7361 0.0000 0.1289 0.0026
## PP.Nat_4R_PBFB 0.0000 0.0025 0.0002 0.0252
## PP.Nat_2R_PBFB 0.0000 0.9593 0.0000 0.0004
## PP.Nat_3R_PBFB 0.4963 0.0015 0.0012
## PP.Nat_1_VB 0.4963 0.0589 0.8010
## PP.Nat_4R_VB 0.0015 0.0589 0.0000
## PP.Nat_2R_VB 0.0012 0.8010 0.0000
## PP.Nat_3R_VB 0.0000 0.4384 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0000 0.6509 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.4965 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0002 0.7539 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0002 0.4941 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.5303 0.0000 0.5091 0.3204
## PP.BehavInt2_GFPRB 0.6319 0.0000 0.7559 0.1826
## PP.BehavInt3_GFPRB 0.3912 0.0000 0.7663 0.1487
## PP.BehavInt4_GFPRB 0.2823 0.0000 0.6510 0.2116
## PP.BehavInt1_CBB 0.2428 0.0013 0.0000 0.0000
## PP.BehavInt2_CBB 0.2341 0.0026 0.0000 0.0000
## PP.BehavInt3_CBB 0.2385 0.0022 0.0000 0.0000
## PP.BehavInt4_CBB 0.2531 0.0028 0.0000 0.0000
## PP.BehavInt1_PBPB 0.5303 0.0000 0.5091 0.3204
## PP.BehavInt2_PBPB 0.6319 0.0000 0.7559 0.1826
## PP.BehavInt3_PBPB 0.3912 0.0000 0.7663 0.1487
## PP.BehavInt4_PBPB 0.2823 0.0000 0.6510 0.2116
## PP.BehavInt1_PBFB 0.4922 0.0000 0.4542 0.0268
## PP.BehavInt2_PBFB 0.5253 0.0000 0.2743 0.0088
## PP.BehavInt3_PBFB 0.3432 0.0000 0.3919 0.0199
## PP.BehavInt4_PBFB 0.3898 0.0000 0.3737 0.0185
## PP.BehavInt1_VB 0.2727 0.0000 0.1454 0.8079
## PP.BehavInt2_VB 0.5522 0.0000 0.1699 0.6698
## PP.BehavInt3_VB 0.2843 0.0000 0.1057 0.9557
## PP.BehavInt4_VB 0.2964 0.0000 0.1541 0.7858
## PP.Nat_3R_VB PP.BehavInt1_GFFB PP.BehavInt2_GFFB
## PP.Nat_1_GFFB 0.0000 0.0000 0.0000
## PP.Nat_4R_GFFB 0.0063 0.8166 0.7427
## PP.Nat_2R_GFFB 0.0060 0.9957 0.6654
## PP.Nat_3R_GFFB 0.0000 0.0035 0.0247
## PP.Nat_1_GFPRB 0.8300 0.0088 0.0013
## PP.Nat_4R_GFPRB 0.0000 0.0554 0.2389
## PP.Nat_2R_GFPRB 0.0000 0.1364 0.4398
## PP.Nat_3R_GFPRB 0.0000 0.0300 0.1175
## PP.Nat_1_CBB 0.0000 0.0004 0.0055
## PP.Nat_4R_CBB 0.9144 0.0127 0.0045
## PP.Nat_2R_CBB 0.7186 0.2145 0.1099
## PP.Nat_3R_CBB 0.9568 0.1548 0.0694
## PP.Nat_1_PBPB 0.0026 0.8805 0.7045
## PP.Nat_4R_PBPB 0.0004 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0002 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_1_PBFB 0.0003 0.3289 0.6913
## PP.Nat_4R_PBFB 0.0650 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0005 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_1_VB 0.4384 0.6509 0.4965
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0573 0.4669 0.3039
## PP.BehavInt2_GFPRB 0.0286 0.4785 0.2628
## PP.BehavInt3_GFPRB 0.0140 0.6009 0.3717
## PP.BehavInt4_GFPRB 0.0271 0.6068 0.3910
## PP.BehavInt1_CBB 0.0000 0.0065 0.0316
## PP.BehavInt2_CBB 0.0000 0.0018 0.0126
## PP.BehavInt3_CBB 0.0000 0.0044 0.0228
## PP.BehavInt4_CBB 0.0000 0.0044 0.0247
## PP.BehavInt1_PBPB 0.0573 0.4669 0.3039
## PP.BehavInt2_PBPB 0.0286 0.4785 0.2628
## PP.BehavInt3_PBPB 0.0140 0.6009 0.3717
## PP.BehavInt4_PBPB 0.0271 0.6068 0.3910
## PP.BehavInt1_PBFB 0.0049 0.8822 0.7564
## PP.BehavInt2_PBFB 0.0013 0.6009 0.9975
## PP.BehavInt3_PBFB 0.0023 0.6344 0.9810
## PP.BehavInt4_PBFB 0.0020 0.6156 0.9712
## PP.BehavInt1_VB 0.2183 0.4529 0.3467
## PP.BehavInt2_VB 0.2140 0.3557 0.2212
## PP.BehavInt3_VB 0.3027 0.4525 0.3669
## PP.BehavInt4_VB 0.2199 0.3924 0.2868
## PP.BehavInt3_GFFB PP.BehavInt4_GFFB PP.BehavInt1_GFPRB
## PP.Nat_1_GFFB 0.0000 0.0000 0.4073
## PP.Nat_4R_GFFB 0.6037 0.9813 0.0000
## PP.Nat_2R_GFFB 0.7974 0.8575 0.0000
## PP.Nat_3R_GFFB 0.0015 0.0074 0.0000
## PP.Nat_1_GFPRB 0.0167 0.0091 0.2613
## PP.Nat_4R_GFPRB 0.0294 0.0803 0.0000
## PP.Nat_2R_GFPRB 0.0769 0.1765 0.0000
## PP.Nat_3R_GFPRB 0.0159 0.0639 0.0000
## PP.Nat_1_CBB 0.0001 0.0009 0.0000
## PP.Nat_4R_CBB 0.0151 0.0128 0.7698
## PP.Nat_2R_CBB 0.2490 0.2549 0.0645
## PP.Nat_3R_CBB 0.1776 0.1873 0.0124
## PP.Nat_1_PBPB 0.6909 0.9945 0.0000
## PP.Nat_4R_PBPB 0.0000 0.0000 0.0679
## PP.Nat_2R_PBPB 0.0000 0.0000 0.3167
## PP.Nat_3R_PBPB 0.0000 0.0002 0.0423
## PP.Nat_1_PBFB 0.2283 0.4443 0.0000
## PP.Nat_4R_PBFB 0.0002 0.0001 0.0004
## PP.Nat_2R_PBFB 0.0000 0.0000 0.7019
## PP.Nat_3R_PBFB 0.0002 0.0002 0.5303
## PP.Nat_1_VB 0.7539 0.4941 0.0000
## PP.Nat_4R_VB 0.0000 0.0000 0.5091
## PP.Nat_2R_VB 0.0000 0.0000 0.3204
## PP.Nat_3R_VB 0.0000 0.0000 0.0573
## PP.BehavInt1_GFFB 0.0000 0.0000 0.4669
## PP.BehavInt2_GFFB 0.0000 0.0000 0.3039
## PP.BehavInt3_GFFB 0.0000 0.6055
## PP.BehavInt4_GFFB 0.0000 0.3542
## PP.BehavInt1_GFPRB 0.6055 0.3542
## PP.BehavInt2_GFPRB 0.6454 0.3526 0.0000
## PP.BehavInt3_GFPRB 0.7700 0.4653 0.0000
## PP.BehavInt4_GFPRB 0.7644 0.4567 0.0000
## PP.BehavInt1_CBB 0.0028 0.0130 0.0000
## PP.BehavInt2_CBB 0.0006 0.0038 0.0000
## PP.BehavInt3_CBB 0.0018 0.0087 0.0000
## PP.BehavInt4_CBB 0.0018 0.0085 0.0000
## PP.BehavInt1_PBPB 0.6055 0.3542 0.0000
## PP.BehavInt2_PBPB 0.6454 0.3526 0.0000
## PP.BehavInt3_PBPB 0.7700 0.4653 0.0000
## PP.BehavInt4_PBPB 0.7644 0.4567 0.0000
## PP.BehavInt1_PBFB 0.7225 0.9417 0.0000
## PP.BehavInt2_PBFB 0.4561 0.7669 0.0000
## PP.BehavInt3_PBFB 0.5033 0.8157 0.0000
## PP.BehavInt4_PBFB 0.4799 0.7958 0.0000
## PP.BehavInt1_VB 0.5474 0.3352 0.0000
## PP.BehavInt2_VB 0.4693 0.2643 0.0000
## PP.BehavInt3_VB 0.5389 0.3349 0.0000
## PP.BehavInt4_VB 0.4839 0.2821 0.0000
## PP.BehavInt2_GFPRB PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB
## PP.Nat_1_GFFB 0.4043 0.4905 0.4694
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0841 0.1876 0.2464
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.9614 0.7846 0.5295
## PP.Nat_2R_CBB 0.1375 0.0764 0.0289
## PP.Nat_3R_CBB 0.0387 0.0208 0.0068
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0911 0.1151 0.1264
## PP.Nat_2R_PBPB 0.3541 0.4094 0.4930
## PP.Nat_3R_PBPB 0.0633 0.0281 0.0240
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0005 0.0010 0.0022
## PP.Nat_2R_PBFB 0.7404 0.8750 0.9902
## PP.Nat_3R_PBFB 0.6319 0.3912 0.2823
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.7559 0.7663 0.6510
## PP.Nat_2R_VB 0.1826 0.1487 0.2116
## PP.Nat_3R_VB 0.0286 0.0140 0.0271
## PP.BehavInt1_GFFB 0.4785 0.6009 0.6068
## PP.BehavInt2_GFFB 0.2628 0.3717 0.3910
## PP.BehavInt3_GFFB 0.6454 0.7700 0.7644
## PP.BehavInt4_GFFB 0.3526 0.4653 0.4567
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB PP.BehavInt2_CBB PP.BehavInt3_CBB
## PP.Nat_1_GFFB 0.0089 0.0024 0.0059
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.1057 0.1086 0.0990
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.2535 0.2586 0.2674
## PP.Nat_2R_CBB 0.5990 0.4740 0.5351
## PP.Nat_3R_CBB 0.6880 0.8455 0.7641
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1056 0.0798 0.0977
## PP.Nat_2R_PBPB 0.0577 0.0358 0.0562
## PP.Nat_3R_PBPB 0.0004 0.0008 0.0006
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.1419 0.1978 0.1733
## PP.Nat_2R_PBFB 0.4328 0.3701 0.4005
## PP.Nat_3R_PBFB 0.2428 0.2341 0.2385
## PP.Nat_1_VB 0.0013 0.0026 0.0022
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0065 0.0018 0.0044
## PP.BehavInt2_GFFB 0.0316 0.0126 0.0228
## PP.BehavInt3_GFFB 0.0028 0.0006 0.0018
## PP.BehavInt4_GFFB 0.0130 0.0038 0.0087
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0004 0.0011 0.0006
## PP.BehavInt2_VB 0.0003 0.0006 0.0005
## PP.BehavInt3_VB 0.0006 0.0021 0.0010
## PP.BehavInt4_VB 0.0004 0.0014 0.0007
## PP.BehavInt4_CBB PP.BehavInt1_PBPB PP.BehavInt2_PBPB
## PP.Nat_1_GFFB 0.0051 0.4073 0.4043
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.1116 0.2613 0.0841
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.2258 0.7698 0.9614
## PP.Nat_2R_CBB 0.5242 0.0645 0.1375
## PP.Nat_3R_CBB 0.8005 0.0124 0.0387
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1120 0.0679 0.0911
## PP.Nat_2R_PBPB 0.0582 0.3167 0.3541
## PP.Nat_3R_PBPB 0.0007 0.0423 0.0633
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.1473 0.0004 0.0005
## PP.Nat_2R_PBFB 0.4306 0.7019 0.7404
## PP.Nat_3R_PBFB 0.2531 0.5303 0.6319
## PP.Nat_1_VB 0.0028 0.0000 0.0000
## PP.Nat_4R_VB 0.0000 0.5091 0.7559
## PP.Nat_2R_VB 0.0000 0.3204 0.1826
## PP.Nat_3R_VB 0.0000 0.0573 0.0286
## PP.BehavInt1_GFFB 0.0044 0.4669 0.4785
## PP.BehavInt2_GFFB 0.0247 0.3039 0.2628
## PP.BehavInt3_GFFB 0.0018 0.6055 0.6454
## PP.BehavInt4_GFFB 0.0085 0.3542 0.3526
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0006 0.0000 0.0000
## PP.BehavInt2_VB 0.0006 0.0000 0.0000
## PP.BehavInt3_VB 0.0010 0.0000 0.0000
## PP.BehavInt4_VB 0.0007 0.0000 0.0000
## PP.BehavInt3_PBPB PP.BehavInt4_PBPB PP.BehavInt1_PBFB
## PP.Nat_1_GFFB 0.4905 0.4694 0.9372
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.1876 0.2464 0.0463
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.7846 0.5295 0.9649
## PP.Nat_2R_CBB 0.0764 0.0289 0.2856
## PP.Nat_3R_CBB 0.0208 0.0068 0.0878
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1151 0.1264 0.4969
## PP.Nat_2R_PBPB 0.4094 0.4930 0.8688
## PP.Nat_3R_PBPB 0.0281 0.0240 0.0245
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0010 0.0022 0.0022
## PP.Nat_2R_PBFB 0.8750 0.9902 0.8417
## PP.Nat_3R_PBFB 0.3912 0.2823 0.4922
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.7663 0.6510 0.4542
## PP.Nat_2R_VB 0.1487 0.2116 0.0268
## PP.Nat_3R_VB 0.0140 0.0271 0.0049
## PP.BehavInt1_GFFB 0.6009 0.6068 0.8822
## PP.BehavInt2_GFFB 0.3717 0.3910 0.7564
## PP.BehavInt3_GFFB 0.7700 0.7644 0.7225
## PP.BehavInt4_GFFB 0.4653 0.4567 0.9417
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB PP.BehavInt3_PBFB PP.BehavInt4_PBFB
## PP.Nat_1_GFFB 0.6968 0.7981 0.7596
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0433 0.0975 0.1067
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.8031 0.8894 0.9876
## PP.Nat_2R_CBB 0.4689 0.2195 0.2934
## PP.Nat_3R_CBB 0.1924 0.0679 0.0990
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.5769 0.6994 0.6436
## PP.Nat_2R_PBPB 0.9918 0.9038 0.9847
## PP.Nat_3R_PBPB 0.0334 0.0117 0.0144
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0031 0.0045 0.0037
## PP.Nat_2R_PBFB 0.8623 0.9996 0.9073
## PP.Nat_3R_PBFB 0.5253 0.3432 0.3898
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.2743 0.3919 0.3737
## PP.Nat_2R_VB 0.0088 0.0199 0.0185
## PP.Nat_3R_VB 0.0013 0.0023 0.0020
## PP.BehavInt1_GFFB 0.6009 0.6344 0.6156
## PP.BehavInt2_GFFB 0.9975 0.9810 0.9712
## PP.BehavInt3_GFFB 0.4561 0.5033 0.4799
## PP.BehavInt4_GFFB 0.7669 0.8157 0.7958
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB PP.BehavInt2_VB PP.BehavInt3_VB
## PP.Nat_1_GFFB 0.3462 0.2443 0.3179
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0001
## PP.Nat_1_GFPRB 0.5729 0.2083 0.7437
## PP.Nat_4R_GFPRB 0.0007 0.0000 0.0019
## PP.Nat_2R_GFPRB 0.0001 0.0000 0.0003
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0003
## PP.Nat_1_CBB 0.0094 0.0049 0.0169
## PP.Nat_4R_CBB 0.0889 0.1611 0.0542
## PP.Nat_2R_CBB 0.0010 0.0069 0.0003
## PP.Nat_3R_CBB 0.0004 0.0025 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0510 0.0139 0.0554
## PP.Nat_2R_PBPB 0.4804 0.1995 0.4538
## PP.Nat_3R_PBPB 0.0576 0.3288 0.0682
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0032 0.0023 0.0047
## PP.Nat_2R_PBFB 0.8177 0.8034 0.8596
## PP.Nat_3R_PBFB 0.2727 0.5522 0.2843
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.1454 0.1699 0.1057
## PP.Nat_2R_VB 0.8079 0.6698 0.9557
## PP.Nat_3R_VB 0.2183 0.2140 0.3027
## PP.BehavInt1_GFFB 0.4529 0.3557 0.4525
## PP.BehavInt2_GFFB 0.3467 0.2212 0.3669
## PP.BehavInt3_GFFB 0.5474 0.4693 0.5389
## PP.BehavInt4_GFFB 0.3352 0.2643 0.3349
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0004 0.0003 0.0006
## PP.BehavInt2_CBB 0.0011 0.0006 0.0021
## PP.BehavInt3_CBB 0.0006 0.0005 0.0010
## PP.BehavInt4_CBB 0.0006 0.0006 0.0010
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB
## PP.Nat_1_GFFB 0.2993
## PP.Nat_4R_GFFB 0.0000
## PP.Nat_2R_GFFB 0.0000
## PP.Nat_3R_GFFB 0.0000
## PP.Nat_1_GFPRB 0.5869
## PP.Nat_4R_GFPRB 0.0006
## PP.Nat_2R_GFPRB 0.0001
## PP.Nat_3R_GFPRB 0.0001
## PP.Nat_1_CBB 0.0126
## PP.Nat_4R_CBB 0.0704
## PP.Nat_2R_CBB 0.0009
## PP.Nat_3R_CBB 0.0003
## PP.Nat_1_PBPB 0.0000
## PP.Nat_4R_PBPB 0.0495
## PP.Nat_2R_PBPB 0.3842
## PP.Nat_3R_PBPB 0.0770
## PP.Nat_1_PBFB 0.0000
## PP.Nat_4R_PBFB 0.0051
## PP.Nat_2R_PBFB 0.8119
## PP.Nat_3R_PBFB 0.2964
## PP.Nat_1_VB 0.0000
## PP.Nat_4R_VB 0.1541
## PP.Nat_2R_VB 0.7858
## PP.Nat_3R_VB 0.2199
## PP.BehavInt1_GFFB 0.3924
## PP.BehavInt2_GFFB 0.2868
## PP.BehavInt3_GFFB 0.4839
## PP.BehavInt4_GFFB 0.2821
## PP.BehavInt1_GFPRB 0.0000
## PP.BehavInt2_GFPRB 0.0000
## PP.BehavInt3_GFPRB 0.0000
## PP.BehavInt4_GFPRB 0.0000
## PP.BehavInt1_CBB 0.0004
## PP.BehavInt2_CBB 0.0014
## PP.BehavInt3_CBB 0.0007
## PP.BehavInt4_CBB 0.0007
## PP.BehavInt1_PBPB 0.0000
## PP.BehavInt2_PBPB 0.0000
## PP.BehavInt3_PBPB 0.0000
## PP.BehavInt4_PBPB 0.0000
## PP.BehavInt1_PBFB 0.0000
## PP.BehavInt2_PBFB 0.0000
## PP.BehavInt3_PBFB 0.0000
## PP.BehavInt4_PBFB 0.0000
## PP.BehavInt1_VB 0.0000
## PP.BehavInt2_VB 0.0000
## PP.BehavInt3_VB 0.0000
## PP.BehavInt4_VBlibrary(corrplot)
corrplot(mydata.cor4, method="color")
corrplot(mydata.cor4, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Naturalness (TOTAL SCALE), Support, and Individual Difference Measures
PP$corAll <- data.frame(PP$Naturalness_Scale_GFFB_Tot, PP$Naturalness_Scale_GFPRB_Tot, PP$Naturalness_Scale_CBB_Tot, PP$Naturalness_Scale_PBPB_Tot, PP$Naturalness_Scale_PBFB_Tot, PP$Naturalness_Scale_VB_Tot, PP$Behav_Scale_GFFB, PP$Behav_Scale_GFPRB, PP$Behav_Scale_CBB, PP$Behav_Scale_PBPB, PP$Behav_Scale_PBFB, PP$Behav_Scale_VB, PP$CCB_Scale,PP$CNS_Scale,PP$ATNS_Scale, PP$CollScale, PP$IndScale)
mydata.cor6 = cor(PP$corAll, use = "pairwise.complete.obs")
head(round(mydata.cor6,2))## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 0.18 0.18 -0.15
## PP.Nat_4R_GFFB 0.18 1.00 0.61 0.50
## PP.Nat_2R_GFFB 0.18 0.61 1.00 0.44
## PP.Nat_3R_GFFB -0.15 0.50 0.44 1.00
## PP.Nat_1_GFPRB 0.42 0.15 0.07 0.01
## PP.Nat_4R_GFPRB 0.04 0.47 0.21 0.33
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB
## PP.Nat_1_GFFB 0.42 0.04 -0.03 -0.04
## PP.Nat_4R_GFFB 0.15 0.47 0.49 0.38
## PP.Nat_2R_GFFB 0.07 0.21 0.29 0.17
## PP.Nat_3R_GFFB 0.01 0.33 0.34 0.49
## PP.Nat_1_GFPRB 1.00 0.38 0.25 0.14
## PP.Nat_4R_GFPRB 0.38 1.00 0.68 0.52
## PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB
## PP.Nat_1_GFFB 0.35 -0.01 0.04 0.00
## PP.Nat_4R_GFFB -0.36 0.20 0.14 0.05
## PP.Nat_2R_GFFB -0.32 0.13 0.22 0.21
## PP.Nat_3R_GFFB -0.41 0.08 0.07 0.01
## PP.Nat_1_GFPRB -0.10 -0.05 -0.13 -0.13
## PP.Nat_4R_GFPRB -0.34 0.11 -0.06 -0.06
## PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB
## PP.Nat_1_GFFB 0.14 -0.21 -0.26 -0.17
## PP.Nat_4R_GFFB -0.23 0.08 0.00 -0.04
## PP.Nat_2R_GFFB -0.27 0.15 0.04 0.06
## PP.Nat_3R_GFFB -0.36 0.09 0.14 0.10
## PP.Nat_1_GFPRB -0.04 0.03 0.05 -0.33
## PP.Nat_4R_GFPRB -0.23 0.20 0.11 -0.12
## PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB
## PP.Nat_1_GFFB 0.17 0.24 0.28 0.29
## PP.Nat_4R_GFFB -0.35 -0.07 0.03 0.05
## PP.Nat_2R_GFFB -0.33 0.05 -0.11 -0.07
## PP.Nat_3R_GFFB -0.37 -0.11 -0.11 -0.15
## PP.Nat_1_GFPRB -0.06 0.23 0.20 0.28
## PP.Nat_4R_GFPRB -0.35 -0.01 -0.04 0.05
## PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Nat_1_GFFB 0.09 -0.21 -0.22 -0.24
## PP.Nat_4R_GFFB -0.13 0.25 0.13 0.05
## PP.Nat_2R_GFFB -0.09 0.15 0.12 0.12
## PP.Nat_3R_GFFB -0.04 0.27 0.22 0.20
## PP.Nat_1_GFPRB 0.09 0.02 0.07 0.06
## PP.Nat_4R_GFPRB -0.11 0.32 0.39 0.25
## PP.BehavInt1_GFFB PP.BehavInt2_GFFB PP.BehavInt3_GFFB
## PP.Nat_1_GFFB 0.59 0.52 0.58
## PP.Nat_4R_GFFB 0.13 0.18 0.10
## PP.Nat_2R_GFFB 0.16 0.16 0.12
## PP.Nat_3R_GFFB -0.19 -0.09 -0.21
## PP.Nat_1_GFPRB 0.27 0.31 0.24
## PP.Nat_4R_GFPRB -0.04 0.05 -0.03
## PP.BehavInt4_GFFB PP.BehavInt1_GFPRB PP.BehavInt2_GFPRB
## PP.Nat_1_GFFB 0.59 0.08 0.03
## PP.Nat_4R_GFFB 0.15 -0.19 -0.24
## PP.Nat_2R_GFFB 0.14 -0.29 -0.31
## PP.Nat_3R_GFFB -0.17 -0.20 -0.23
## PP.Nat_1_GFPRB 0.26 0.15 0.04
## PP.Nat_4R_GFPRB -0.05 -0.04 -0.13
## PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB PP.BehavInt1_CBB
## PP.Nat_1_GFFB 0.02 0.02 0.26
## PP.Nat_4R_GFFB -0.22 -0.29 -0.26
## PP.Nat_2R_GFFB -0.34 -0.33 -0.27
## PP.Nat_3R_GFFB -0.22 -0.22 -0.29
## PP.Nat_1_GFPRB 0.08 0.07 -0.02
## PP.Nat_4R_GFPRB -0.10 -0.05 -0.27
## PP.BehavInt2_CBB PP.BehavInt3_CBB PP.BehavInt4_CBB
## PP.Nat_1_GFFB 0.35 0.29 0.31
## PP.Nat_4R_GFFB -0.29 -0.29 -0.26
## PP.Nat_2R_GFFB -0.25 -0.28 -0.28
## PP.Nat_3R_GFFB -0.29 -0.32 -0.33
## PP.Nat_1_GFPRB -0.03 -0.06 -0.03
## PP.Nat_4R_GFPRB -0.34 -0.33 -0.30
## PP.BehavInt1_PBPB PP.BehavInt2_PBPB PP.BehavInt3_PBPB
## PP.Nat_1_GFFB 0.08 0.03 0.02
## PP.Nat_4R_GFFB -0.19 -0.24 -0.22
## PP.Nat_2R_GFFB -0.29 -0.31 -0.34
## PP.Nat_3R_GFFB -0.20 -0.23 -0.22
## PP.Nat_1_GFPRB 0.15 0.04 0.08
## PP.Nat_4R_GFPRB -0.04 -0.13 -0.10
## PP.BehavInt4_PBPB PP.BehavInt1_PBFB PP.BehavInt2_PBFB
## PP.Nat_1_GFFB 0.02 0.03 0.16
## PP.Nat_4R_GFFB -0.29 -0.38 -0.38
## PP.Nat_2R_GFFB -0.33 -0.37 -0.29
## PP.Nat_3R_GFFB -0.22 -0.34 -0.39
## PP.Nat_1_GFPRB 0.07 -0.10 -0.04
## PP.Nat_4R_GFPRB -0.05 -0.27 -0.28
## PP.BehavInt3_PBFB PP.BehavInt4_PBFB PP.BehavInt1_VB
## PP.Nat_1_GFFB 0.08 0.12 0.05
## PP.Nat_4R_GFFB -0.37 -0.35 -0.14
## PP.Nat_2R_GFFB -0.36 -0.31 -0.17
## PP.Nat_3R_GFFB -0.38 -0.41 -0.11
## PP.Nat_1_GFPRB -0.01 0.05 0.10
## PP.Nat_4R_GFPRB -0.22 -0.20 -0.08
## PP.BehavInt2_VB PP.BehavInt3_VB PP.BehavInt4_VB PP.CCB_48
## PP.Nat_1_GFFB 0.04 0.05 0.05 -0.05
## PP.Nat_4R_GFFB -0.17 -0.17 -0.15 -0.06
## PP.Nat_2R_GFFB -0.12 -0.18 -0.18 -0.17
## PP.Nat_3R_GFFB -0.09 -0.10 -0.09 -0.02
## PP.Nat_1_GFPRB 0.03 0.17 0.13 0.07
## PP.Nat_4R_GFPRB -0.21 -0.03 -0.12 0.02
## PP.CCB_49 PP.CCB_50 PP.CCB_51 PP.CNS_1 PP.CNS_2 PP.CNS_3
## PP.Nat_1_GFFB -0.03 0.01 0.07 0.12 0.16 0.11
## PP.Nat_4R_GFFB -0.10 -0.05 -0.05 -0.10 -0.04 -0.03
## PP.Nat_2R_GFFB -0.14 -0.13 -0.16 -0.11 -0.12 -0.14
## PP.Nat_3R_GFFB 0.01 -0.01 -0.03 -0.13 -0.09 -0.03
## PP.Nat_1_GFPRB 0.08 0.04 0.06 0.12 0.22 0.11
## PP.Nat_4R_GFPRB 0.00 0.01 -0.06 -0.09 0.00 -0.06
## PP.ATNS_1 PP.ATNS_2R PP.ATNS_3 PP.ATNS_4 PP.ATNS_5 PP.Ind_3
## PP.Nat_1_GFFB 0.20 -0.23 0.10 0.09 0.11 0.22
## PP.Nat_4R_GFFB -0.12 0.23 -0.13 -0.04 -0.10 -0.15
## PP.Nat_2R_GFFB -0.14 0.09 -0.10 -0.08 -0.10 -0.10
## PP.Nat_3R_GFFB -0.17 0.23 -0.11 -0.08 -0.10 -0.24
## PP.Nat_1_GFPRB 0.10 0.12 0.04 0.11 0.08 0.12
## PP.Nat_4R_GFPRB -0.09 0.34 -0.20 -0.10 -0.12 -0.21
## PP.Ind_4 PP.Ind_7 PP.Ind_8 PP.Ind_1 PP.Ind_2 PP.Ind_5 PP.Ind_6
## PP.Nat_1_GFFB 0.22 0.19 0.22 0.12 0.12 0.12 0.13
## PP.Nat_4R_GFFB 0.05 -0.12 0.04 0.06 0.02 -0.02 -0.02
## PP.Nat_2R_GFFB 0.00 -0.09 0.01 0.00 -0.06 -0.13 -0.04
## PP.Nat_3R_GFFB -0.05 -0.20 -0.09 -0.05 -0.02 -0.12 -0.05
## PP.Nat_1_GFPRB 0.26 0.10 0.31 0.26 0.24 0.21 0.26
## PP.Nat_4R_GFPRB 0.01 -0.17 -0.02 0.06 0.00 0.01 0.00library("Hmisc")
mydata.rcorr6 = rcorr(as.matrix(mydata.cor6))
mydata.rcorr6## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 1.00 -0.02 0.02 -0.42
## PP.Nat_4R_GFFB -0.02 1.00 0.92 0.85
## PP.Nat_2R_GFFB 0.02 0.92 1.00 0.79
## PP.Nat_3R_GFFB -0.42 0.85 0.79 1.00
## PP.Nat_1_GFPRB 0.45 0.34 0.22 0.11
## PP.Nat_4R_GFPRB -0.27 0.82 0.66 0.80
## PP.Nat_2R_GFPRB -0.22 0.83 0.68 0.79
## PP.Nat_3R_GFPRB -0.31 0.81 0.68 0.87
## PP.Nat_1_CBB 0.50 -0.73 -0.66 -0.84
## PP.Nat_4R_CBB -0.22 0.05 0.10 0.08
## PP.Nat_2R_CBB -0.08 0.12 0.26 0.10
## PP.Nat_3R_CBB -0.11 0.16 0.32 0.15
## PP.Nat_1_PBPB 0.04 -0.79 -0.79 -0.74
## PP.Nat_4R_PBPB -0.66 0.01 0.02 0.23
## PP.Nat_2R_PBPB -0.73 0.00 0.04 0.28
## PP.Nat_3R_PBPB -0.54 0.20 0.34 0.40
## PP.Nat_1_PBFB 0.14 -0.86 -0.82 -0.81
## PP.Nat_4R_PBFB 0.53 0.22 0.24 0.00
## PP.Nat_2R_PBFB 0.56 -0.05 -0.14 -0.26
## PP.Nat_3R_PBFB 0.50 -0.08 -0.20 -0.30
## PP.Nat_1_VB -0.10 -0.63 -0.66 -0.49
## PP.Nat_4R_VB -0.71 0.28 0.20 0.51
## PP.Nat_2R_VB -0.70 0.39 0.33 0.63
## PP.Nat_3R_VB -0.65 0.40 0.40 0.64
## PP.BehavInt1_GFFB 0.92 -0.05 0.00 -0.43
## PP.BehavInt2_GFFB 0.89 0.03 0.06 -0.35
## PP.BehavInt3_GFFB 0.91 -0.09 -0.04 -0.46
## PP.BehavInt4_GFFB 0.91 -0.02 0.02 -0.40
## PP.BehavInt1_GFPRB -0.09 -0.71 -0.77 -0.60
## PP.BehavInt2_GFPRB -0.09 -0.76 -0.80 -0.64
## PP.BehavInt3_GFPRB -0.08 -0.74 -0.80 -0.63
## PP.BehavInt4_GFPRB -0.08 -0.75 -0.80 -0.63
## PP.BehavInt1_CBB 0.39 -0.73 -0.71 -0.80
## PP.BehavInt2_CBB 0.44 -0.72 -0.68 -0.81
## PP.BehavInt3_CBB 0.40 -0.74 -0.70 -0.81
## PP.BehavInt4_CBB 0.41 -0.72 -0.70 -0.80
## PP.BehavInt1_PBPB -0.09 -0.71 -0.77 -0.60
## PP.BehavInt2_PBPB -0.09 -0.76 -0.80 -0.64
## PP.BehavInt3_PBPB -0.08 -0.74 -0.80 -0.63
## PP.BehavInt4_PBPB -0.08 -0.75 -0.80 -0.63
## PP.BehavInt1_PBFB 0.01 -0.83 -0.83 -0.74
## PP.BehavInt2_PBFB 0.09 -0.83 -0.80 -0.77
## PP.BehavInt3_PBFB 0.06 -0.83 -0.83 -0.76
## PP.BehavInt4_PBFB 0.07 -0.82 -0.81 -0.76
## PP.BehavInt1_VB -0.12 -0.66 -0.71 -0.52
## PP.BehavInt2_VB -0.13 -0.70 -0.71 -0.55
## PP.BehavInt3_VB -0.13 -0.65 -0.72 -0.51
## PP.BehavInt4_VB -0.13 -0.66 -0.72 -0.52
## PP.CCB_48 -0.17 -0.40 -0.53 -0.26
## PP.CCB_49 -0.13 -0.41 -0.53 -0.27
## PP.CCB_50 -0.12 -0.44 -0.56 -0.31
## PP.CCB_51 0.00 -0.52 -0.64 -0.43
## PP.CNS_1 0.24 -0.51 -0.55 -0.53
## PP.CNS_2 0.23 -0.36 -0.46 -0.40
## PP.CNS_3 0.17 -0.39 -0.48 -0.38
## PP.ATNS_1 0.42 -0.25 -0.26 -0.37
## PP.ATNS_2R -0.54 0.66 0.55 0.77
## PP.ATNS_3 0.24 -0.35 -0.35 -0.38
## PP.ATNS_4 0.15 -0.34 -0.40 -0.36
## PP.ATNS_5 0.23 -0.22 -0.25 -0.26
## PP.Ind_3 0.45 -0.44 -0.40 -0.58
## PP.Ind_4 0.39 -0.10 -0.16 -0.25
## PP.Ind_7 0.44 -0.44 -0.41 -0.59
## PP.Ind_8 0.43 -0.15 -0.21 -0.32
## PP.Ind_1 0.20 -0.05 -0.16 -0.14
## PP.Ind_2 0.21 -0.08 -0.17 -0.15
## PP.Ind_5 0.24 -0.22 -0.34 -0.30
## PP.Ind_6 0.27 -0.09 -0.16 -0.19
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Nat_1_GFFB 0.45 -0.27 -0.22
## PP.Nat_4R_GFFB 0.34 0.82 0.83
## PP.Nat_2R_GFFB 0.22 0.66 0.68
## PP.Nat_3R_GFFB 0.11 0.80 0.79
## PP.Nat_1_GFPRB 1.00 0.45 0.42
## PP.Nat_4R_GFPRB 0.45 1.00 0.94
## PP.Nat_2R_GFPRB 0.42 0.94 1.00
## PP.Nat_3R_GFPRB 0.31 0.89 0.90
## PP.Nat_1_CBB -0.26 -0.80 -0.77
## PP.Nat_4R_CBB -0.45 0.01 -0.05
## PP.Nat_2R_CBB -0.46 -0.04 -0.07
## PP.Nat_3R_CBB -0.43 -0.01 -0.03
## PP.Nat_1_PBPB -0.29 -0.67 -0.72
## PP.Nat_4R_PBPB -0.31 0.20 0.05
## PP.Nat_2R_PBPB -0.37 0.19 0.07
## PP.Nat_3R_PBPB -0.46 0.17 0.09
## PP.Nat_1_PBFB -0.34 -0.78 -0.81
## PP.Nat_4R_PBFB 0.53 0.11 0.21
## PP.Nat_2R_PBFB 0.50 -0.13 -0.01
## PP.Nat_3R_PBFB 0.54 -0.08 -0.02
## PP.Nat_1_VB -0.09 -0.45 -0.52
## PP.Nat_4R_VB -0.08 0.52 0.40
## PP.Nat_2R_VB -0.01 0.66 0.57
## PP.Nat_3R_VB -0.04 0.62 0.55
## PP.BehavInt1_GFFB 0.37 -0.30 -0.23
## PP.BehavInt2_GFFB 0.44 -0.20 -0.13
## PP.BehavInt3_GFFB 0.33 -0.33 -0.28
## PP.BehavInt4_GFFB 0.37 -0.28 -0.22
## PP.BehavInt1_GFPRB -0.20 -0.48 -0.53
## PP.BehavInt2_GFPRB -0.28 -0.56 -0.60
## PP.BehavInt3_GFPRB -0.23 -0.53 -0.58
## PP.BehavInt4_GFPRB -0.20 -0.51 -0.56
## PP.BehavInt1_CBB -0.24 -0.74 -0.71
## PP.BehavInt2_CBB -0.24 -0.77 -0.74
## PP.BehavInt3_CBB -0.25 -0.76 -0.73
## PP.BehavInt4_CBB -0.24 -0.74 -0.72
## PP.BehavInt1_PBPB -0.20 -0.48 -0.53
## PP.BehavInt2_PBPB -0.28 -0.56 -0.60
## PP.BehavInt3_PBPB -0.23 -0.53 -0.58
## PP.BehavInt4_PBPB -0.20 -0.51 -0.56
## PP.BehavInt1_PBFB -0.31 -0.67 -0.71
## PP.BehavInt2_PBFB -0.32 -0.71 -0.75
## PP.BehavInt3_PBFB -0.27 -0.67 -0.72
## PP.BehavInt4_PBFB -0.26 -0.66 -0.72
## PP.BehavInt1_VB -0.09 -0.46 -0.53
## PP.BehavInt2_VB -0.19 -0.54 -0.62
## PP.BehavInt3_VB -0.06 -0.42 -0.50
## PP.BehavInt4_VB -0.09 -0.46 -0.53
## PP.CCB_48 0.03 -0.22 -0.26
## PP.CCB_49 0.05 -0.24 -0.26
## PP.CCB_50 0.00 -0.29 -0.32
## PP.CCB_51 -0.01 -0.39 -0.39
## PP.CNS_1 0.08 -0.52 -0.48
## PP.CNS_2 0.28 -0.32 -0.30
## PP.CNS_3 0.14 -0.39 -0.38
## PP.ATNS_1 0.26 -0.33 -0.27
## PP.ATNS_2R 0.19 0.77 0.75
## PP.ATNS_3 0.13 -0.42 -0.34
## PP.ATNS_4 0.17 -0.36 -0.30
## PP.ATNS_5 0.23 -0.27 -0.18
## PP.Ind_3 0.15 -0.56 -0.49
## PP.Ind_4 0.46 -0.18 -0.14
## PP.Ind_7 0.17 -0.55 -0.51
## PP.Ind_8 0.49 -0.21 -0.16
## PP.Ind_1 0.46 -0.06 -0.03
## PP.Ind_2 0.43 -0.10 -0.07
## PP.Ind_5 0.39 -0.20 -0.17
## PP.Ind_6 0.45 -0.13 -0.09
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Nat_1_GFFB -0.31 0.50 -0.22 -0.08
## PP.Nat_4R_GFFB 0.81 -0.73 0.05 0.12
## PP.Nat_2R_GFFB 0.68 -0.66 0.10 0.26
## PP.Nat_3R_GFFB 0.87 -0.84 0.08 0.10
## PP.Nat_1_GFPRB 0.31 -0.26 -0.45 -0.46
## PP.Nat_4R_GFPRB 0.89 -0.80 0.01 -0.04
## PP.Nat_2R_GFPRB 0.90 -0.77 -0.05 -0.07
## PP.Nat_3R_GFPRB 1.00 -0.85 -0.08 -0.07
## PP.Nat_1_CBB -0.85 1.00 0.21 0.19
## PP.Nat_4R_CBB -0.08 0.21 1.00 0.89
## PP.Nat_2R_CBB -0.07 0.19 0.89 1.00
## PP.Nat_3R_CBB 0.03 0.07 0.80 0.92
## PP.Nat_1_PBPB -0.76 0.70 0.08 -0.07
## PP.Nat_4R_PBPB 0.06 -0.23 0.38 0.22
## PP.Nat_2R_PBPB 0.11 -0.28 0.48 0.43
## PP.Nat_3R_PBPB 0.26 -0.36 0.33 0.42
## PP.Nat_1_PBFB -0.84 0.82 0.15 0.03
## PP.Nat_4R_PBFB 0.17 -0.16 -0.55 -0.39
## PP.Nat_2R_PBFB -0.08 0.10 -0.68 -0.64
## PP.Nat_3R_PBFB -0.12 0.11 -0.56 -0.60
## PP.Nat_1_VB -0.52 0.37 -0.25 -0.41
## PP.Nat_4R_VB 0.44 -0.58 0.09 -0.07
## PP.Nat_2R_VB 0.60 -0.72 0.06 -0.04
## PP.Nat_3R_VB 0.61 -0.70 0.07 0.02
## PP.BehavInt1_GFFB -0.33 0.50 -0.31 -0.16
## PP.BehavInt2_GFFB -0.25 0.41 -0.35 -0.20
## PP.BehavInt3_GFFB -0.36 0.54 -0.30 -0.14
## PP.BehavInt4_GFFB -0.29 0.47 -0.34 -0.17
## PP.BehavInt1_GFPRB -0.60 0.55 0.08 -0.13
## PP.BehavInt2_GFPRB -0.65 0.61 0.12 -0.08
## PP.BehavInt3_GFPRB -0.64 0.58 0.08 -0.12
## PP.BehavInt4_GFPRB -0.62 0.56 0.03 -0.18
## PP.BehavInt1_CBB -0.82 0.93 0.23 0.15
## PP.BehavInt2_CBB -0.84 0.95 0.23 0.17
## PP.BehavInt3_CBB -0.84 0.94 0.22 0.15
## PP.BehavInt4_CBB -0.82 0.94 0.24 0.16
## PP.BehavInt1_PBPB -0.60 0.55 0.08 -0.13
## PP.BehavInt2_PBPB -0.65 0.61 0.12 -0.08
## PP.BehavInt3_PBPB -0.64 0.58 0.08 -0.12
## PP.BehavInt4_PBPB -0.62 0.56 0.03 -0.18
## PP.BehavInt1_PBFB -0.75 0.70 0.10 -0.06
## PP.BehavInt2_PBFB -0.79 0.75 0.14 0.01
## PP.BehavInt3_PBFB -0.76 0.71 0.08 -0.07
## PP.BehavInt4_PBFB -0.77 0.72 0.12 -0.03
## PP.BehavInt1_VB -0.54 0.38 -0.19 -0.39
## PP.BehavInt2_VB -0.61 0.43 -0.15 -0.31
## PP.BehavInt3_VB -0.51 0.36 -0.22 -0.43
## PP.BehavInt4_VB -0.53 0.37 -0.20 -0.39
## PP.CCB_48 -0.25 0.10 -0.39 -0.53
## PP.CCB_49 -0.27 0.13 -0.39 -0.54
## PP.CCB_50 -0.31 0.18 -0.35 -0.50
## PP.CCB_51 -0.42 0.34 -0.33 -0.48
## PP.CNS_1 -0.50 0.48 -0.36 -0.43
## PP.CNS_2 -0.32 0.26 -0.53 -0.61
## PP.CNS_3 -0.36 0.30 -0.47 -0.54
## PP.ATNS_1 -0.22 0.20 -0.62 -0.55
## PP.ATNS_2R 0.80 -0.85 -0.04 -0.06
## PP.ATNS_3 -0.28 0.19 -0.60 -0.58
## PP.ATNS_4 -0.28 0.16 -0.59 -0.61
## PP.ATNS_5 -0.12 0.05 -0.68 -0.64
## PP.Ind_3 -0.51 0.48 -0.35 -0.31
## PP.Ind_4 -0.14 0.11 -0.59 -0.59
## PP.Ind_7 -0.51 0.45 -0.44 -0.40
## PP.Ind_8 -0.19 0.18 -0.62 -0.61
## PP.Ind_1 -0.05 -0.02 -0.61 -0.65
## PP.Ind_2 -0.08 -0.01 -0.59 -0.60
## PP.Ind_5 -0.22 0.13 -0.60 -0.67
## PP.Ind_6 -0.08 0.05 -0.55 -0.57
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Nat_1_GFFB -0.11 0.04 -0.66 -0.73
## PP.Nat_4R_GFFB 0.16 -0.79 0.01 0.00
## PP.Nat_2R_GFFB 0.32 -0.79 0.02 0.04
## PP.Nat_3R_GFFB 0.15 -0.74 0.23 0.28
## PP.Nat_1_GFPRB -0.43 -0.29 -0.31 -0.37
## PP.Nat_4R_GFPRB -0.01 -0.67 0.20 0.19
## PP.Nat_2R_GFPRB -0.03 -0.72 0.05 0.07
## PP.Nat_3R_GFPRB 0.03 -0.76 0.06 0.11
## PP.Nat_1_CBB 0.07 0.70 -0.23 -0.28
## PP.Nat_4R_CBB 0.80 0.08 0.38 0.48
## PP.Nat_2R_CBB 0.92 -0.07 0.22 0.43
## PP.Nat_3R_CBB 1.00 -0.17 0.16 0.40
## PP.Nat_1_PBPB -0.17 1.00 0.28 0.16
## PP.Nat_4R_PBPB 0.16 0.28 1.00 0.84
## PP.Nat_2R_PBPB 0.40 0.16 0.84 1.00
## PP.Nat_3R_PBPB 0.47 -0.20 0.61 0.71
## PP.Nat_1_PBFB -0.06 0.93 0.07 0.02
## PP.Nat_4R_PBFB -0.32 -0.49 -0.67 -0.64
## PP.Nat_2R_PBFB -0.62 -0.09 -0.65 -0.80
## PP.Nat_3R_PBFB -0.59 0.02 -0.55 -0.69
## PP.Nat_1_VB -0.47 0.81 0.25 0.08
## PP.Nat_4R_VB -0.06 0.00 0.75 0.66
## PP.Nat_2R_VB -0.02 -0.27 0.57 0.60
## PP.Nat_3R_VB 0.04 -0.38 0.51 0.54
## PP.BehavInt1_GFFB -0.18 0.05 -0.65 -0.77
## PP.BehavInt2_GFFB -0.23 -0.03 -0.64 -0.78
## PP.BehavInt3_GFFB -0.16 0.09 -0.63 -0.75
## PP.BehavInt4_GFFB -0.19 0.01 -0.65 -0.77
## PP.BehavInt1_GFPRB -0.25 0.92 0.33 0.21
## PP.BehavInt2_GFPRB -0.19 0.93 0.31 0.20
## PP.BehavInt3_GFPRB -0.23 0.93 0.29 0.19
## PP.BehavInt4_GFPRB -0.28 0.92 0.28 0.17
## PP.BehavInt1_CBB 0.00 0.76 -0.15 -0.20
## PP.BehavInt2_CBB 0.02 0.74 -0.17 -0.23
## PP.BehavInt3_CBB 0.01 0.75 -0.16 -0.20
## PP.BehavInt4_CBB 0.01 0.76 -0.15 -0.20
## PP.BehavInt1_PBPB -0.25 0.92 0.33 0.21
## PP.BehavInt2_PBPB -0.19 0.93 0.31 0.20
## PP.BehavInt3_PBPB -0.23 0.93 0.29 0.19
## PP.BehavInt4_PBPB -0.28 0.92 0.28 0.17
## PP.BehavInt1_PBFB -0.18 0.92 0.16 0.08
## PP.BehavInt2_PBFB -0.10 0.93 0.15 0.07
## PP.BehavInt3_PBFB -0.19 0.92 0.12 0.04
## PP.BehavInt4_PBFB -0.15 0.93 0.15 0.08
## PP.BehavInt1_VB -0.46 0.85 0.28 0.11
## PP.BehavInt2_VB -0.38 0.87 0.34 0.19
## PP.BehavInt3_VB -0.49 0.84 0.27 0.11
## PP.BehavInt4_VB -0.45 0.85 0.28 0.13
## PP.CCB_48 -0.57 0.38 0.00 -0.12
## PP.CCB_49 -0.58 0.38 -0.05 -0.17
## PP.CCB_50 -0.54 0.43 -0.03 -0.15
## PP.CCB_51 -0.54 0.54 -0.08 -0.20
## PP.CNS_1 -0.45 0.42 -0.30 -0.41
## PP.CNS_2 -0.59 0.29 -0.33 -0.45
## PP.CNS_3 -0.53 0.30 -0.30 -0.41
## PP.ATNS_1 -0.46 0.02 -0.50 -0.59
## PP.ATNS_2R 0.04 -0.64 0.28 0.33
## PP.ATNS_3 -0.46 0.16 -0.43 -0.50
## PP.ATNS_4 -0.54 0.25 -0.31 -0.41
## PP.ATNS_5 -0.53 0.00 -0.46 -0.52
## PP.Ind_3 -0.26 0.34 -0.33 -0.42
## PP.Ind_4 -0.51 0.04 -0.42 -0.57
## PP.Ind_7 -0.35 0.36 -0.39 -0.49
## PP.Ind_8 -0.55 0.15 -0.40 -0.54
## PP.Ind_1 -0.63 -0.03 -0.38 -0.50
## PP.Ind_2 -0.56 -0.04 -0.44 -0.49
## PP.Ind_5 -0.65 0.16 -0.36 -0.49
## PP.Ind_6 -0.48 -0.03 -0.41 -0.52
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Nat_1_GFFB -0.54 0.14 0.53 0.56
## PP.Nat_4R_GFFB 0.20 -0.86 0.22 -0.05
## PP.Nat_2R_GFFB 0.34 -0.82 0.24 -0.14
## PP.Nat_3R_GFFB 0.40 -0.81 0.00 -0.26
## PP.Nat_1_GFPRB -0.46 -0.34 0.53 0.50
## PP.Nat_4R_GFPRB 0.17 -0.78 0.11 -0.13
## PP.Nat_2R_GFPRB 0.09 -0.81 0.21 -0.01
## PP.Nat_3R_GFPRB 0.26 -0.84 0.17 -0.08
## PP.Nat_1_CBB -0.36 0.82 -0.16 0.10
## PP.Nat_4R_CBB 0.33 0.15 -0.55 -0.68
## PP.Nat_2R_CBB 0.42 0.03 -0.39 -0.64
## PP.Nat_3R_CBB 0.47 -0.06 -0.32 -0.62
## PP.Nat_1_PBPB -0.20 0.93 -0.49 -0.09
## PP.Nat_4R_PBPB 0.61 0.07 -0.67 -0.65
## PP.Nat_2R_PBPB 0.71 0.02 -0.64 -0.80
## PP.Nat_3R_PBPB 1.00 -0.27 -0.41 -0.71
## PP.Nat_1_PBFB -0.27 1.00 -0.44 -0.09
## PP.Nat_4R_PBFB -0.41 -0.44 1.00 0.78
## PP.Nat_2R_PBFB -0.71 -0.09 0.78 1.00
## PP.Nat_3R_PBFB -0.82 0.03 0.68 0.87
## PP.Nat_1_VB -0.26 0.72 -0.40 0.03
## PP.Nat_4R_VB 0.50 -0.20 -0.54 -0.55
## PP.Nat_2R_VB 0.48 -0.39 -0.34 -0.51
## PP.Nat_3R_VB 0.63 -0.47 -0.29 -0.52
## PP.BehavInt1_GFFB -0.52 0.15 0.51 0.55
## PP.BehavInt2_GFFB -0.53 0.07 0.55 0.57
## PP.BehavInt3_GFFB -0.49 0.19 0.49 0.53
## PP.BehavInt4_GFFB -0.50 0.12 0.52 0.55
## PP.BehavInt1_GFPRB -0.25 0.85 -0.51 -0.13
## PP.BehavInt2_GFPRB -0.22 0.88 -0.49 -0.12
## PP.BehavInt3_GFPRB -0.27 0.87 -0.47 -0.09
## PP.BehavInt4_GFPRB -0.29 0.86 -0.45 -0.06
## PP.BehavInt1_CBB -0.44 0.88 -0.24 0.06
## PP.BehavInt2_CBB -0.42 0.86 -0.21 0.08
## PP.BehavInt3_CBB -0.43 0.87 -0.22 0.07
## PP.BehavInt4_CBB -0.43 0.87 -0.23 0.06
## PP.BehavInt1_PBPB -0.25 0.85 -0.51 -0.13
## PP.BehavInt2_PBPB -0.22 0.88 -0.49 -0.12
## PP.BehavInt3_PBPB -0.27 0.87 -0.47 -0.09
## PP.BehavInt4_PBPB -0.29 0.86 -0.45 -0.06
## PP.BehavInt1_PBFB -0.30 0.94 -0.44 -0.07
## PP.BehavInt2_PBFB -0.27 0.95 -0.43 -0.08
## PP.BehavInt3_PBFB -0.33 0.95 -0.41 -0.05
## PP.BehavInt4_PBFB -0.31 0.95 -0.42 -0.08
## PP.BehavInt1_VB -0.29 0.74 -0.41 0.03
## PP.BehavInt2_VB -0.15 0.77 -0.42 -0.04
## PP.BehavInt3_VB -0.28 0.74 -0.39 0.02
## PP.BehavInt4_VB -0.27 0.74 -0.40 0.03
## PP.CCB_48 -0.46 0.36 -0.08 0.30
## PP.CCB_49 -0.50 0.38 -0.05 0.32
## PP.CCB_50 -0.50 0.42 -0.06 0.33
## PP.CCB_51 -0.56 0.55 -0.09 0.32
## PP.CNS_1 -0.51 0.49 0.02 0.40
## PP.CNS_2 -0.57 0.33 0.14 0.48
## PP.CNS_3 -0.52 0.37 0.07 0.43
## PP.ATNS_1 -0.39 0.07 0.47 0.63
## PP.ATNS_2R 0.39 -0.74 -0.01 -0.22
## PP.ATNS_3 -0.36 0.19 0.30 0.55
## PP.ATNS_4 -0.44 0.26 0.22 0.50
## PP.ATNS_5 -0.40 0.04 0.43 0.62
## PP.Ind_3 -0.34 0.34 0.17 0.43
## PP.Ind_4 -0.46 0.04 0.31 0.60
## PP.Ind_7 -0.41 0.38 0.16 0.45
## PP.Ind_8 -0.49 0.12 0.25 0.54
## PP.Ind_1 -0.50 -0.02 0.29 0.55
## PP.Ind_2 -0.44 -0.01 0.29 0.51
## PP.Ind_5 -0.54 0.15 0.21 0.54
## PP.Ind_6 -0.40 -0.02 0.30 0.53
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Nat_1_GFFB 0.50 -0.10 -0.71 -0.70
## PP.Nat_4R_GFFB -0.08 -0.63 0.28 0.39
## PP.Nat_2R_GFFB -0.20 -0.66 0.20 0.33
## PP.Nat_3R_GFFB -0.30 -0.49 0.51 0.63
## PP.Nat_1_GFPRB 0.54 -0.09 -0.08 -0.01
## PP.Nat_4R_GFPRB -0.08 -0.45 0.52 0.66
## PP.Nat_2R_GFPRB -0.02 -0.52 0.40 0.57
## PP.Nat_3R_GFPRB -0.12 -0.52 0.44 0.60
## PP.Nat_1_CBB 0.11 0.37 -0.58 -0.72
## PP.Nat_4R_CBB -0.56 -0.25 0.09 0.06
## PP.Nat_2R_CBB -0.60 -0.41 -0.07 -0.04
## PP.Nat_3R_CBB -0.59 -0.47 -0.06 -0.02
## PP.Nat_1_PBPB 0.02 0.81 0.00 -0.27
## PP.Nat_4R_PBPB -0.55 0.25 0.75 0.57
## PP.Nat_2R_PBPB -0.69 0.08 0.66 0.60
## PP.Nat_3R_PBPB -0.82 -0.26 0.50 0.48
## PP.Nat_1_PBFB 0.03 0.72 -0.20 -0.39
## PP.Nat_4R_PBFB 0.68 -0.40 -0.54 -0.34
## PP.Nat_2R_PBFB 0.87 0.03 -0.55 -0.51
## PP.Nat_3R_PBFB 1.00 0.13 -0.45 -0.44
## PP.Nat_1_VB 0.13 1.00 0.25 0.03
## PP.Nat_4R_VB -0.45 0.25 1.00 0.89
## PP.Nat_2R_VB -0.44 0.03 0.89 1.00
## PP.Nat_3R_VB -0.56 -0.13 0.77 0.87
## PP.BehavInt1_GFFB 0.48 -0.05 -0.67 -0.67
## PP.BehavInt2_GFFB 0.50 -0.09 -0.63 -0.60
## PP.BehavInt3_GFFB 0.45 -0.03 -0.69 -0.69
## PP.BehavInt4_GFFB 0.47 -0.08 -0.67 -0.67
## PP.BehavInt1_GFPRB 0.03 0.81 0.13 -0.10
## PP.BehavInt2_GFPRB 0.01 0.79 0.08 -0.15
## PP.BehavInt3_GFPRB 0.07 0.81 0.08 -0.16
## PP.BehavInt4_GFPRB 0.10 0.83 0.10 -0.14
## PP.BehavInt1_CBB 0.12 0.45 -0.48 -0.61
## PP.BehavInt2_CBB 0.12 0.43 -0.53 -0.66
## PP.BehavInt3_CBB 0.12 0.44 -0.51 -0.64
## PP.BehavInt4_CBB 0.11 0.43 -0.50 -0.64
## PP.BehavInt1_PBPB 0.03 0.81 0.13 -0.10
## PP.BehavInt2_PBPB 0.01 0.79 0.08 -0.15
## PP.BehavInt3_PBPB 0.07 0.81 0.08 -0.16
## PP.BehavInt4_PBPB 0.10 0.83 0.10 -0.14
## PP.BehavInt1_PBFB 0.07 0.76 -0.08 -0.27
## PP.BehavInt2_PBFB 0.04 0.72 -0.12 -0.32
## PP.BehavInt3_PBFB 0.09 0.76 -0.09 -0.29
## PP.BehavInt4_PBFB 0.07 0.75 -0.09 -0.28
## PP.BehavInt1_VB 0.17 0.92 0.21 -0.03
## PP.BehavInt2_VB 0.08 0.88 0.19 -0.07
## PP.BehavInt3_VB 0.16 0.91 0.23 0.00
## PP.BehavInt4_VB 0.15 0.91 0.21 -0.03
## PP.CCB_48 0.45 0.61 0.07 0.00
## PP.CCB_49 0.47 0.61 0.03 -0.02
## PP.CCB_50 0.48 0.61 0.00 -0.09
## PP.CCB_51 0.47 0.67 -0.08 -0.18
## PP.CNS_1 0.39 0.49 -0.35 -0.44
## PP.CNS_2 0.50 0.47 -0.22 -0.26
## PP.CNS_3 0.46 0.49 -0.24 -0.30
## PP.ATNS_1 0.50 0.19 -0.45 -0.45
## PP.ATNS_2R -0.20 -0.39 0.55 0.66
## PP.ATNS_3 0.46 0.32 -0.32 -0.37
## PP.ATNS_4 0.49 0.45 -0.21 -0.29
## PP.ATNS_5 0.53 0.23 -0.30 -0.29
## PP.Ind_3 0.33 0.29 -0.42 -0.56
## PP.Ind_4 0.56 0.22 -0.28 -0.37
## PP.Ind_7 0.37 0.37 -0.40 -0.52
## PP.Ind_8 0.51 0.31 -0.26 -0.34
## PP.Ind_1 0.54 0.28 -0.17 -0.18
## PP.Ind_2 0.50 0.25 -0.21 -0.19
## PP.Ind_5 0.54 0.41 -0.19 -0.26
## PP.Ind_6 0.47 0.20 -0.26 -0.27
## PP.Nat_3R_VB PP.BehavInt1_GFFB PP.BehavInt2_GFFB
## PP.Nat_1_GFFB -0.65 0.92 0.89
## PP.Nat_4R_GFFB 0.40 -0.05 0.03
## PP.Nat_2R_GFFB 0.40 0.00 0.06
## PP.Nat_3R_GFFB 0.64 -0.43 -0.35
## PP.Nat_1_GFPRB -0.04 0.37 0.44
## PP.Nat_4R_GFPRB 0.62 -0.30 -0.20
## PP.Nat_2R_GFPRB 0.55 -0.23 -0.13
## PP.Nat_3R_GFPRB 0.61 -0.33 -0.25
## PP.Nat_1_CBB -0.70 0.50 0.41
## PP.Nat_4R_CBB 0.07 -0.31 -0.35
## PP.Nat_2R_CBB 0.02 -0.16 -0.20
## PP.Nat_3R_CBB 0.04 -0.18 -0.23
## PP.Nat_1_PBPB -0.38 0.05 -0.03
## PP.Nat_4R_PBPB 0.51 -0.65 -0.64
## PP.Nat_2R_PBPB 0.54 -0.77 -0.78
## PP.Nat_3R_PBPB 0.63 -0.52 -0.53
## PP.Nat_1_PBFB -0.47 0.15 0.07
## PP.Nat_4R_PBFB -0.29 0.51 0.55
## PP.Nat_2R_PBFB -0.52 0.55 0.57
## PP.Nat_3R_PBFB -0.56 0.48 0.50
## PP.Nat_1_VB -0.13 -0.05 -0.09
## PP.Nat_4R_VB 0.77 -0.67 -0.63
## PP.Nat_2R_VB 0.87 -0.67 -0.60
## PP.Nat_3R_VB 1.00 -0.62 -0.56
## PP.BehavInt1_GFFB -0.62 1.00 0.98
## PP.BehavInt2_GFFB -0.56 0.98 1.00
## PP.BehavInt3_GFFB -0.64 0.99 0.97
## PP.BehavInt4_GFFB -0.61 0.99 0.97
## PP.BehavInt1_GFPRB -0.22 -0.09 -0.13
## PP.BehavInt2_GFPRB -0.26 -0.08 -0.14
## PP.BehavInt3_GFPRB -0.29 -0.06 -0.12
## PP.BehavInt4_GFPRB -0.26 -0.06 -0.11
## PP.BehavInt1_CBB -0.64 0.39 0.32
## PP.BehavInt2_CBB -0.67 0.44 0.37
## PP.BehavInt3_CBB -0.66 0.41 0.33
## PP.BehavInt4_CBB -0.66 0.40 0.33
## PP.BehavInt1_PBPB -0.22 -0.09 -0.13
## PP.BehavInt2_PBPB -0.26 -0.08 -0.14
## PP.BehavInt3_PBPB -0.29 -0.06 -0.12
## PP.BehavInt4_PBPB -0.26 -0.06 -0.11
## PP.BehavInt1_PBFB -0.35 0.03 -0.03
## PP.BehavInt2_PBFB -0.39 0.09 0.02
## PP.BehavInt3_PBFB -0.38 0.08 0.02
## PP.BehavInt4_PBFB -0.37 0.09 0.02
## PP.BehavInt1_VB -0.18 -0.09 -0.12
## PP.BehavInt2_VB -0.19 -0.11 -0.15
## PP.BehavInt3_VB -0.15 -0.10 -0.12
## PP.BehavInt4_VB -0.18 -0.11 -0.14
## PP.CCB_48 -0.20 -0.14 -0.15
## PP.CCB_49 -0.22 -0.10 -0.10
## PP.CCB_50 -0.29 -0.11 -0.13
## PP.CCB_51 -0.38 0.01 -0.01
## PP.CNS_1 -0.54 0.25 0.22
## PP.CNS_2 -0.40 0.24 0.23
## PP.CNS_3 -0.44 0.20 0.18
## PP.ATNS_1 -0.45 0.43 0.43
## PP.ATNS_2R 0.64 -0.54 -0.47
## PP.ATNS_3 -0.40 0.26 0.25
## PP.ATNS_4 -0.38 0.19 0.18
## PP.ATNS_5 -0.35 0.26 0.26
## PP.Ind_3 -0.60 0.44 0.39
## PP.Ind_4 -0.43 0.40 0.39
## PP.Ind_7 -0.58 0.49 0.46
## PP.Ind_8 -0.40 0.47 0.46
## PP.Ind_1 -0.26 0.23 0.25
## PP.Ind_2 -0.23 0.23 0.25
## PP.Ind_5 -0.35 0.27 0.28
## PP.Ind_6 -0.30 0.31 0.32
## PP.BehavInt3_GFFB PP.BehavInt4_GFFB PP.BehavInt1_GFPRB
## PP.Nat_1_GFFB 0.91 0.91 -0.09
## PP.Nat_4R_GFFB -0.09 -0.02 -0.71
## PP.Nat_2R_GFFB -0.04 0.02 -0.77
## PP.Nat_3R_GFFB -0.46 -0.40 -0.60
## PP.Nat_1_GFPRB 0.33 0.37 -0.20
## PP.Nat_4R_GFPRB -0.33 -0.28 -0.48
## PP.Nat_2R_GFPRB -0.28 -0.22 -0.53
## PP.Nat_3R_GFPRB -0.36 -0.29 -0.60
## PP.Nat_1_CBB 0.54 0.47 0.55
## PP.Nat_4R_CBB -0.30 -0.34 0.08
## PP.Nat_2R_CBB -0.14 -0.17 -0.13
## PP.Nat_3R_CBB -0.16 -0.19 -0.25
## PP.Nat_1_PBPB 0.09 0.01 0.92
## PP.Nat_4R_PBPB -0.63 -0.65 0.33
## PP.Nat_2R_PBPB -0.75 -0.77 0.21
## PP.Nat_3R_PBPB -0.49 -0.50 -0.25
## PP.Nat_1_PBFB 0.19 0.12 0.85
## PP.Nat_4R_PBFB 0.49 0.52 -0.51
## PP.Nat_2R_PBFB 0.53 0.55 -0.13
## PP.Nat_3R_PBFB 0.45 0.47 0.03
## PP.Nat_1_VB -0.03 -0.08 0.81
## PP.Nat_4R_VB -0.69 -0.67 0.13
## PP.Nat_2R_VB -0.69 -0.67 -0.10
## PP.Nat_3R_VB -0.64 -0.61 -0.22
## PP.BehavInt1_GFFB 0.99 0.99 -0.09
## PP.BehavInt2_GFFB 0.97 0.97 -0.13
## PP.BehavInt3_GFFB 1.00 0.99 -0.05
## PP.BehavInt4_GFFB 0.99 1.00 -0.13
## PP.BehavInt1_GFPRB -0.05 -0.13 1.00
## PP.BehavInt2_GFPRB -0.04 -0.12 0.98
## PP.BehavInt3_GFPRB -0.03 -0.10 0.99
## PP.BehavInt4_GFPRB -0.03 -0.10 0.99
## PP.BehavInt1_CBB 0.43 0.35 0.71
## PP.BehavInt2_CBB 0.48 0.41 0.66
## PP.BehavInt3_CBB 0.44 0.37 0.69
## PP.BehavInt4_CBB 0.44 0.37 0.68
## PP.BehavInt1_PBPB -0.05 -0.13 1.00
## PP.BehavInt2_PBPB -0.04 -0.12 0.98
## PP.BehavInt3_PBPB -0.03 -0.10 0.99
## PP.BehavInt4_PBPB -0.03 -0.10 0.99
## PP.BehavInt1_PBFB 0.07 0.00 0.95
## PP.BehavInt2_PBFB 0.13 0.05 0.92
## PP.BehavInt3_PBFB 0.11 0.04 0.94
## PP.BehavInt4_PBFB 0.12 0.04 0.94
## PP.BehavInt1_VB -0.07 -0.13 0.90
## PP.BehavInt2_VB -0.07 -0.14 0.89
## PP.BehavInt3_VB -0.08 -0.13 0.90
## PP.BehavInt4_VB -0.09 -0.14 0.90
## PP.CCB_48 -0.15 -0.13 0.44
## PP.CCB_49 -0.11 -0.09 0.44
## PP.CCB_50 -0.11 -0.10 0.48
## PP.CCB_51 0.02 0.02 0.57
## PP.CNS_1 0.27 0.27 0.32
## PP.CNS_2 0.24 0.28 0.23
## PP.CNS_3 0.20 0.23 0.24
## PP.ATNS_1 0.44 0.47 -0.11
## PP.ATNS_2R -0.57 -0.50 -0.52
## PP.ATNS_3 0.28 0.30 0.03
## PP.ATNS_4 0.20 0.22 0.19
## PP.ATNS_5 0.26 0.30 -0.07
## PP.Ind_3 0.45 0.44 0.19
## PP.Ind_4 0.37 0.41 -0.04
## PP.Ind_7 0.49 0.49 0.22
## PP.Ind_8 0.44 0.48 0.08
## PP.Ind_1 0.19 0.25 -0.04
## PP.Ind_2 0.20 0.24 -0.08
## PP.Ind_5 0.25 0.28 0.12
## PP.Ind_6 0.29 0.33 -0.10
## PP.BehavInt2_GFPRB PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB
## PP.Nat_1_GFFB -0.09 -0.08 -0.08
## PP.Nat_4R_GFFB -0.76 -0.74 -0.75
## PP.Nat_2R_GFFB -0.80 -0.80 -0.80
## PP.Nat_3R_GFFB -0.64 -0.63 -0.63
## PP.Nat_1_GFPRB -0.28 -0.23 -0.20
## PP.Nat_4R_GFPRB -0.56 -0.53 -0.51
## PP.Nat_2R_GFPRB -0.60 -0.58 -0.56
## PP.Nat_3R_GFPRB -0.65 -0.64 -0.62
## PP.Nat_1_CBB 0.61 0.58 0.56
## PP.Nat_4R_CBB 0.12 0.08 0.03
## PP.Nat_2R_CBB -0.08 -0.12 -0.18
## PP.Nat_3R_CBB -0.19 -0.23 -0.28
## PP.Nat_1_PBPB 0.93 0.93 0.92
## PP.Nat_4R_PBPB 0.31 0.29 0.28
## PP.Nat_2R_PBPB 0.20 0.19 0.17
## PP.Nat_3R_PBPB -0.22 -0.27 -0.29
## PP.Nat_1_PBFB 0.88 0.87 0.86
## PP.Nat_4R_PBFB -0.49 -0.47 -0.45
## PP.Nat_2R_PBFB -0.12 -0.09 -0.06
## PP.Nat_3R_PBFB 0.01 0.07 0.10
## PP.Nat_1_VB 0.79 0.81 0.83
## PP.Nat_4R_VB 0.08 0.08 0.10
## PP.Nat_2R_VB -0.15 -0.16 -0.14
## PP.Nat_3R_VB -0.26 -0.29 -0.26
## PP.BehavInt1_GFFB -0.08 -0.06 -0.06
## PP.BehavInt2_GFFB -0.14 -0.12 -0.11
## PP.BehavInt3_GFFB -0.04 -0.03 -0.03
## PP.BehavInt4_GFFB -0.12 -0.10 -0.10
## PP.BehavInt1_GFPRB 0.98 0.99 0.99
## PP.BehavInt2_GFPRB 1.00 0.98 0.98
## PP.BehavInt3_GFPRB 0.98 1.00 0.99
## PP.BehavInt4_GFPRB 0.98 0.99 1.00
## PP.BehavInt1_CBB 0.74 0.73 0.70
## PP.BehavInt2_CBB 0.70 0.69 0.66
## PP.BehavInt3_CBB 0.72 0.71 0.69
## PP.BehavInt4_CBB 0.72 0.71 0.68
## PP.BehavInt1_PBPB 0.98 0.99 0.99
## PP.BehavInt2_PBPB 1.00 0.98 0.98
## PP.BehavInt3_PBPB 0.98 1.00 0.99
## PP.BehavInt4_PBPB 0.98 0.99 1.00
## PP.BehavInt1_PBFB 0.96 0.95 0.95
## PP.BehavInt2_PBFB 0.95 0.93 0.92
## PP.BehavInt3_PBFB 0.94 0.95 0.94
## PP.BehavInt4_PBFB 0.94 0.95 0.94
## PP.BehavInt1_VB 0.88 0.90 0.91
## PP.BehavInt2_VB 0.89 0.90 0.91
## PP.BehavInt3_VB 0.87 0.90 0.91
## PP.BehavInt4_VB 0.89 0.91 0.92
## PP.CCB_48 0.40 0.46 0.48
## PP.CCB_49 0.40 0.46 0.48
## PP.CCB_50 0.45 0.50 0.52
## PP.CCB_51 0.55 0.60 0.61
## PP.CNS_1 0.33 0.34 0.34
## PP.CNS_2 0.22 0.24 0.26
## PP.CNS_3 0.24 0.27 0.27
## PP.ATNS_1 -0.08 -0.08 -0.06
## PP.ATNS_2R -0.57 -0.55 -0.54
## PP.ATNS_3 0.06 0.06 0.08
## PP.ATNS_4 0.19 0.21 0.23
## PP.ATNS_5 -0.07 -0.04 -0.02
## PP.Ind_3 0.22 0.22 0.22
## PP.Ind_4 -0.06 -0.02 0.00
## PP.Ind_7 0.23 0.25 0.25
## PP.Ind_8 0.04 0.09 0.11
## PP.Ind_1 -0.09 -0.04 -0.02
## PP.Ind_2 -0.11 -0.08 -0.06
## PP.Ind_5 0.09 0.13 0.15
## PP.Ind_6 -0.11 -0.09 -0.07
## PP.BehavInt1_CBB PP.BehavInt2_CBB PP.BehavInt3_CBB
## PP.Nat_1_GFFB 0.39 0.44 0.40
## PP.Nat_4R_GFFB -0.73 -0.72 -0.74
## PP.Nat_2R_GFFB -0.71 -0.68 -0.70
## PP.Nat_3R_GFFB -0.80 -0.81 -0.81
## PP.Nat_1_GFPRB -0.24 -0.24 -0.25
## PP.Nat_4R_GFPRB -0.74 -0.77 -0.76
## PP.Nat_2R_GFPRB -0.71 -0.74 -0.73
## PP.Nat_3R_GFPRB -0.82 -0.84 -0.84
## PP.Nat_1_CBB 0.93 0.95 0.94
## PP.Nat_4R_CBB 0.23 0.23 0.22
## PP.Nat_2R_CBB 0.15 0.17 0.15
## PP.Nat_3R_CBB 0.00 0.02 0.01
## PP.Nat_1_PBPB 0.76 0.74 0.75
## PP.Nat_4R_PBPB -0.15 -0.17 -0.16
## PP.Nat_2R_PBPB -0.20 -0.23 -0.20
## PP.Nat_3R_PBPB -0.44 -0.42 -0.43
## PP.Nat_1_PBFB 0.88 0.86 0.87
## PP.Nat_4R_PBFB -0.24 -0.21 -0.22
## PP.Nat_2R_PBFB 0.06 0.08 0.07
## PP.Nat_3R_PBFB 0.12 0.12 0.12
## PP.Nat_1_VB 0.45 0.43 0.44
## PP.Nat_4R_VB -0.48 -0.53 -0.51
## PP.Nat_2R_VB -0.61 -0.66 -0.64
## PP.Nat_3R_VB -0.64 -0.67 -0.66
## PP.BehavInt1_GFFB 0.39 0.44 0.41
## PP.BehavInt2_GFFB 0.32 0.37 0.33
## PP.BehavInt3_GFFB 0.43 0.48 0.44
## PP.BehavInt4_GFFB 0.35 0.41 0.37
## PP.BehavInt1_GFPRB 0.71 0.66 0.69
## PP.BehavInt2_GFPRB 0.74 0.70 0.72
## PP.BehavInt3_GFPRB 0.73 0.69 0.71
## PP.BehavInt4_GFPRB 0.70 0.66 0.69
## PP.BehavInt1_CBB 1.00 0.99 0.99
## PP.BehavInt2_CBB 0.99 1.00 0.99
## PP.BehavInt3_CBB 0.99 0.99 1.00
## PP.BehavInt4_CBB 0.99 0.99 0.99
## PP.BehavInt1_PBPB 0.71 0.66 0.69
## PP.BehavInt2_PBPB 0.74 0.70 0.72
## PP.BehavInt3_PBPB 0.73 0.69 0.71
## PP.BehavInt4_PBPB 0.70 0.66 0.69
## PP.BehavInt1_PBFB 0.82 0.79 0.81
## PP.BehavInt2_PBFB 0.84 0.82 0.83
## PP.BehavInt3_PBFB 0.82 0.79 0.81
## PP.BehavInt4_PBFB 0.83 0.80 0.82
## PP.BehavInt1_VB 0.51 0.47 0.49
## PP.BehavInt2_VB 0.53 0.50 0.51
## PP.BehavInt3_VB 0.49 0.45 0.48
## PP.BehavInt4_VB 0.50 0.47 0.49
## PP.CCB_48 0.20 0.16 0.20
## PP.CCB_49 0.23 0.19 0.22
## PP.CCB_50 0.27 0.23 0.27
## PP.CCB_51 0.44 0.40 0.44
## PP.CNS_1 0.44 0.45 0.44
## PP.CNS_2 0.25 0.25 0.25
## PP.CNS_3 0.29 0.29 0.28
## PP.ATNS_1 0.07 0.11 0.08
## PP.ATNS_2R -0.84 -0.86 -0.85
## PP.ATNS_3 0.08 0.10 0.09
## PP.ATNS_4 0.13 0.13 0.14
## PP.ATNS_5 -0.05 -0.03 -0.03
## PP.Ind_3 0.36 0.40 0.38
## PP.Ind_4 0.02 0.05 0.02
## PP.Ind_7 0.36 0.38 0.38
## PP.Ind_8 0.12 0.14 0.12
## PP.Ind_1 -0.04 -0.04 -0.04
## PP.Ind_2 -0.03 -0.03 -0.03
## PP.Ind_5 0.13 0.12 0.13
## PP.Ind_6 -0.02 -0.01 -0.02
## PP.BehavInt4_CBB PP.BehavInt1_PBPB PP.BehavInt2_PBPB
## PP.Nat_1_GFFB 0.41 -0.09 -0.09
## PP.Nat_4R_GFFB -0.72 -0.71 -0.76
## PP.Nat_2R_GFFB -0.70 -0.77 -0.80
## PP.Nat_3R_GFFB -0.80 -0.60 -0.64
## PP.Nat_1_GFPRB -0.24 -0.20 -0.28
## PP.Nat_4R_GFPRB -0.74 -0.48 -0.56
## PP.Nat_2R_GFPRB -0.72 -0.53 -0.60
## PP.Nat_3R_GFPRB -0.82 -0.60 -0.65
## PP.Nat_1_CBB 0.94 0.55 0.61
## PP.Nat_4R_CBB 0.24 0.08 0.12
## PP.Nat_2R_CBB 0.16 -0.13 -0.08
## PP.Nat_3R_CBB 0.01 -0.25 -0.19
## PP.Nat_1_PBPB 0.76 0.92 0.93
## PP.Nat_4R_PBPB -0.15 0.33 0.31
## PP.Nat_2R_PBPB -0.20 0.21 0.20
## PP.Nat_3R_PBPB -0.43 -0.25 -0.22
## PP.Nat_1_PBFB 0.87 0.85 0.88
## PP.Nat_4R_PBFB -0.23 -0.51 -0.49
## PP.Nat_2R_PBFB 0.06 -0.13 -0.12
## PP.Nat_3R_PBFB 0.11 0.03 0.01
## PP.Nat_1_VB 0.43 0.81 0.79
## PP.Nat_4R_VB -0.50 0.13 0.08
## PP.Nat_2R_VB -0.64 -0.10 -0.15
## PP.Nat_3R_VB -0.66 -0.22 -0.26
## PP.BehavInt1_GFFB 0.40 -0.09 -0.08
## PP.BehavInt2_GFFB 0.33 -0.13 -0.14
## PP.BehavInt3_GFFB 0.44 -0.05 -0.04
## PP.BehavInt4_GFFB 0.37 -0.13 -0.12
## PP.BehavInt1_GFPRB 0.68 1.00 0.98
## PP.BehavInt2_GFPRB 0.72 0.98 1.00
## PP.BehavInt3_GFPRB 0.71 0.99 0.98
## PP.BehavInt4_GFPRB 0.68 0.99 0.98
## PP.BehavInt1_CBB 0.99 0.71 0.74
## PP.BehavInt2_CBB 0.99 0.66 0.70
## PP.BehavInt3_CBB 0.99 0.69 0.72
## PP.BehavInt4_CBB 1.00 0.68 0.72
## PP.BehavInt1_PBPB 0.68 1.00 0.98
## PP.BehavInt2_PBPB 0.72 0.98 1.00
## PP.BehavInt3_PBPB 0.71 0.99 0.98
## PP.BehavInt4_PBPB 0.68 0.99 0.98
## PP.BehavInt1_PBFB 0.80 0.95 0.96
## PP.BehavInt2_PBFB 0.83 0.92 0.95
## PP.BehavInt3_PBFB 0.81 0.94 0.94
## PP.BehavInt4_PBFB 0.82 0.94 0.94
## PP.BehavInt1_VB 0.49 0.90 0.88
## PP.BehavInt2_VB 0.50 0.89 0.89
## PP.BehavInt3_VB 0.48 0.90 0.87
## PP.BehavInt4_VB 0.49 0.90 0.89
## PP.CCB_48 0.19 0.44 0.40
## PP.CCB_49 0.21 0.44 0.40
## PP.CCB_50 0.26 0.48 0.45
## PP.CCB_51 0.43 0.57 0.55
## PP.CNS_1 0.44 0.32 0.33
## PP.CNS_2 0.24 0.23 0.22
## PP.CNS_3 0.28 0.24 0.24
## PP.ATNS_1 0.07 -0.11 -0.08
## PP.ATNS_2R -0.84 -0.52 -0.57
## PP.ATNS_3 0.08 0.03 0.06
## PP.ATNS_4 0.13 0.19 0.19
## PP.ATNS_5 -0.05 -0.07 -0.07
## PP.Ind_3 0.37 0.19 0.22
## PP.Ind_4 0.02 -0.04 -0.06
## PP.Ind_7 0.36 0.22 0.23
## PP.Ind_8 0.11 0.08 0.04
## PP.Ind_1 -0.05 -0.04 -0.09
## PP.Ind_2 -0.04 -0.08 -0.11
## PP.Ind_5 0.13 0.12 0.09
## PP.Ind_6 -0.02 -0.10 -0.11
## PP.BehavInt3_PBPB PP.BehavInt4_PBPB PP.BehavInt1_PBFB
## PP.Nat_1_GFFB -0.08 -0.08 0.01
## PP.Nat_4R_GFFB -0.74 -0.75 -0.83
## PP.Nat_2R_GFFB -0.80 -0.80 -0.83
## PP.Nat_3R_GFFB -0.63 -0.63 -0.74
## PP.Nat_1_GFPRB -0.23 -0.20 -0.31
## PP.Nat_4R_GFPRB -0.53 -0.51 -0.67
## PP.Nat_2R_GFPRB -0.58 -0.56 -0.71
## PP.Nat_3R_GFPRB -0.64 -0.62 -0.75
## PP.Nat_1_CBB 0.58 0.56 0.70
## PP.Nat_4R_CBB 0.08 0.03 0.10
## PP.Nat_2R_CBB -0.12 -0.18 -0.06
## PP.Nat_3R_CBB -0.23 -0.28 -0.18
## PP.Nat_1_PBPB 0.93 0.92 0.92
## PP.Nat_4R_PBPB 0.29 0.28 0.16
## PP.Nat_2R_PBPB 0.19 0.17 0.08
## PP.Nat_3R_PBPB -0.27 -0.29 -0.30
## PP.Nat_1_PBFB 0.87 0.86 0.94
## PP.Nat_4R_PBFB -0.47 -0.45 -0.44
## PP.Nat_2R_PBFB -0.09 -0.06 -0.07
## PP.Nat_3R_PBFB 0.07 0.10 0.07
## PP.Nat_1_VB 0.81 0.83 0.76
## PP.Nat_4R_VB 0.08 0.10 -0.08
## PP.Nat_2R_VB -0.16 -0.14 -0.27
## PP.Nat_3R_VB -0.29 -0.26 -0.35
## PP.BehavInt1_GFFB -0.06 -0.06 0.03
## PP.BehavInt2_GFFB -0.12 -0.11 -0.03
## PP.BehavInt3_GFFB -0.03 -0.03 0.07
## PP.BehavInt4_GFFB -0.10 -0.10 0.00
## PP.BehavInt1_GFPRB 0.99 0.99 0.95
## PP.BehavInt2_GFPRB 0.98 0.98 0.96
## PP.BehavInt3_GFPRB 1.00 0.99 0.95
## PP.BehavInt4_GFPRB 0.99 1.00 0.95
## PP.BehavInt1_CBB 0.73 0.70 0.82
## PP.BehavInt2_CBB 0.69 0.66 0.79
## PP.BehavInt3_CBB 0.71 0.69 0.81
## PP.BehavInt4_CBB 0.71 0.68 0.80
## PP.BehavInt1_PBPB 0.99 0.99 0.95
## PP.BehavInt2_PBPB 0.98 0.98 0.96
## PP.BehavInt3_PBPB 1.00 0.99 0.95
## PP.BehavInt4_PBPB 0.99 1.00 0.95
## PP.BehavInt1_PBFB 0.95 0.95 1.00
## PP.BehavInt2_PBFB 0.93 0.92 0.98
## PP.BehavInt3_PBFB 0.95 0.94 0.99
## PP.BehavInt4_PBFB 0.95 0.94 0.99
## PP.BehavInt1_VB 0.90 0.91 0.85
## PP.BehavInt2_VB 0.90 0.91 0.86
## PP.BehavInt3_VB 0.90 0.91 0.84
## PP.BehavInt4_VB 0.91 0.92 0.85
## PP.CCB_48 0.46 0.48 0.45
## PP.CCB_49 0.46 0.48 0.46
## PP.CCB_50 0.50 0.52 0.50
## PP.CCB_51 0.60 0.61 0.61
## PP.CNS_1 0.34 0.34 0.42
## PP.CNS_2 0.24 0.26 0.30
## PP.CNS_3 0.27 0.27 0.34
## PP.ATNS_1 -0.08 -0.06 -0.01
## PP.ATNS_2R -0.55 -0.54 -0.68
## PP.ATNS_3 0.06 0.08 0.11
## PP.ATNS_4 0.21 0.23 0.23
## PP.ATNS_5 -0.04 -0.02 -0.01
## PP.Ind_3 0.22 0.22 0.25
## PP.Ind_4 -0.02 0.00 0.00
## PP.Ind_7 0.25 0.25 0.31
## PP.Ind_8 0.09 0.11 0.09
## PP.Ind_1 -0.04 -0.02 -0.03
## PP.Ind_2 -0.08 -0.06 -0.05
## PP.Ind_5 0.13 0.15 0.13
## PP.Ind_6 -0.09 -0.07 -0.06
## PP.BehavInt2_PBFB PP.BehavInt3_PBFB PP.BehavInt4_PBFB
## PP.Nat_1_GFFB 0.09 0.06 0.07
## PP.Nat_4R_GFFB -0.83 -0.83 -0.82
## PP.Nat_2R_GFFB -0.80 -0.83 -0.81
## PP.Nat_3R_GFFB -0.77 -0.76 -0.76
## PP.Nat_1_GFPRB -0.32 -0.27 -0.26
## PP.Nat_4R_GFPRB -0.71 -0.67 -0.66
## PP.Nat_2R_GFPRB -0.75 -0.72 -0.72
## PP.Nat_3R_GFPRB -0.79 -0.76 -0.77
## PP.Nat_1_CBB 0.75 0.71 0.72
## PP.Nat_4R_CBB 0.14 0.08 0.12
## PP.Nat_2R_CBB 0.01 -0.07 -0.03
## PP.Nat_3R_CBB -0.10 -0.19 -0.15
## PP.Nat_1_PBPB 0.93 0.92 0.93
## PP.Nat_4R_PBPB 0.15 0.12 0.15
## PP.Nat_2R_PBPB 0.07 0.04 0.08
## PP.Nat_3R_PBPB -0.27 -0.33 -0.31
## PP.Nat_1_PBFB 0.95 0.95 0.95
## PP.Nat_4R_PBFB -0.43 -0.41 -0.42
## PP.Nat_2R_PBFB -0.08 -0.05 -0.08
## PP.Nat_3R_PBFB 0.04 0.09 0.07
## PP.Nat_1_VB 0.72 0.76 0.75
## PP.Nat_4R_VB -0.12 -0.09 -0.09
## PP.Nat_2R_VB -0.32 -0.29 -0.28
## PP.Nat_3R_VB -0.39 -0.38 -0.37
## PP.BehavInt1_GFFB 0.09 0.08 0.09
## PP.BehavInt2_GFFB 0.02 0.02 0.02
## PP.BehavInt3_GFFB 0.13 0.11 0.12
## PP.BehavInt4_GFFB 0.05 0.04 0.04
## PP.BehavInt1_GFPRB 0.92 0.94 0.94
## PP.BehavInt2_GFPRB 0.95 0.94 0.94
## PP.BehavInt3_GFPRB 0.93 0.95 0.95
## PP.BehavInt4_GFPRB 0.92 0.94 0.94
## PP.BehavInt1_CBB 0.84 0.82 0.83
## PP.BehavInt2_CBB 0.82 0.79 0.80
## PP.BehavInt3_CBB 0.83 0.81 0.82
## PP.BehavInt4_CBB 0.83 0.81 0.82
## PP.BehavInt1_PBPB 0.92 0.94 0.94
## PP.BehavInt2_PBPB 0.95 0.94 0.94
## PP.BehavInt3_PBPB 0.93 0.95 0.95
## PP.BehavInt4_PBPB 0.92 0.94 0.94
## PP.BehavInt1_PBFB 0.98 0.99 0.99
## PP.BehavInt2_PBFB 1.00 0.98 0.98
## PP.BehavInt3_PBFB 0.98 1.00 1.00
## PP.BehavInt4_PBFB 0.98 1.00 1.00
## PP.BehavInt1_VB 0.82 0.85 0.84
## PP.BehavInt2_VB 0.85 0.86 0.85
## PP.BehavInt3_VB 0.80 0.85 0.83
## PP.BehavInt4_VB 0.82 0.85 0.84
## PP.CCB_48 0.37 0.44 0.40
## PP.CCB_49 0.37 0.45 0.41
## PP.CCB_50 0.42 0.49 0.45
## PP.CCB_51 0.54 0.60 0.56
## PP.CNS_1 0.38 0.43 0.39
## PP.CNS_2 0.25 0.31 0.27
## PP.CNS_3 0.28 0.34 0.30
## PP.ATNS_1 -0.02 0.01 -0.03
## PP.ATNS_2R -0.73 -0.70 -0.71
## PP.ATNS_3 0.10 0.13 0.09
## PP.ATNS_4 0.19 0.25 0.21
## PP.ATNS_5 -0.04 0.01 -0.04
## PP.Ind_3 0.29 0.26 0.25
## PP.Ind_4 -0.02 0.02 -0.01
## PP.Ind_7 0.33 0.33 0.31
## PP.Ind_8 0.07 0.10 0.07
## PP.Ind_1 -0.09 -0.03 -0.06
## PP.Ind_2 -0.10 -0.04 -0.08
## PP.Ind_5 0.08 0.14 0.11
## PP.Ind_6 -0.10 -0.06 -0.10
## PP.BehavInt1_VB PP.BehavInt2_VB PP.BehavInt3_VB
## PP.Nat_1_GFFB -0.12 -0.13 -0.13
## PP.Nat_4R_GFFB -0.66 -0.70 -0.65
## PP.Nat_2R_GFFB -0.71 -0.71 -0.72
## PP.Nat_3R_GFFB -0.52 -0.55 -0.51
## PP.Nat_1_GFPRB -0.09 -0.19 -0.06
## PP.Nat_4R_GFPRB -0.46 -0.54 -0.42
## PP.Nat_2R_GFPRB -0.53 -0.62 -0.50
## PP.Nat_3R_GFPRB -0.54 -0.61 -0.51
## PP.Nat_1_CBB 0.38 0.43 0.36
## PP.Nat_4R_CBB -0.19 -0.15 -0.22
## PP.Nat_2R_CBB -0.39 -0.31 -0.43
## PP.Nat_3R_CBB -0.46 -0.38 -0.49
## PP.Nat_1_PBPB 0.85 0.87 0.84
## PP.Nat_4R_PBPB 0.28 0.34 0.27
## PP.Nat_2R_PBPB 0.11 0.19 0.11
## PP.Nat_3R_PBPB -0.29 -0.15 -0.28
## PP.Nat_1_PBFB 0.74 0.77 0.74
## PP.Nat_4R_PBFB -0.41 -0.42 -0.39
## PP.Nat_2R_PBFB 0.03 -0.04 0.02
## PP.Nat_3R_PBFB 0.17 0.08 0.16
## PP.Nat_1_VB 0.92 0.88 0.91
## PP.Nat_4R_VB 0.21 0.19 0.23
## PP.Nat_2R_VB -0.03 -0.07 0.00
## PP.Nat_3R_VB -0.18 -0.19 -0.15
## PP.BehavInt1_GFFB -0.09 -0.11 -0.10
## PP.BehavInt2_GFFB -0.12 -0.15 -0.12
## PP.BehavInt3_GFFB -0.07 -0.07 -0.08
## PP.BehavInt4_GFFB -0.13 -0.14 -0.13
## PP.BehavInt1_GFPRB 0.90 0.89 0.90
## PP.BehavInt2_GFPRB 0.88 0.89 0.87
## PP.BehavInt3_GFPRB 0.90 0.90 0.90
## PP.BehavInt4_GFPRB 0.91 0.91 0.91
## PP.BehavInt1_CBB 0.51 0.53 0.49
## PP.BehavInt2_CBB 0.47 0.50 0.45
## PP.BehavInt3_CBB 0.49 0.51 0.48
## PP.BehavInt4_CBB 0.49 0.50 0.48
## PP.BehavInt1_PBPB 0.90 0.89 0.90
## PP.BehavInt2_PBPB 0.88 0.89 0.87
## PP.BehavInt3_PBPB 0.90 0.90 0.90
## PP.BehavInt4_PBPB 0.91 0.91 0.91
## PP.BehavInt1_PBFB 0.85 0.86 0.84
## PP.BehavInt2_PBFB 0.82 0.85 0.80
## PP.BehavInt3_PBFB 0.85 0.86 0.85
## PP.BehavInt4_PBFB 0.84 0.85 0.83
## PP.BehavInt1_VB 1.00 0.96 0.99
## PP.BehavInt2_VB 0.96 1.00 0.95
## PP.BehavInt3_VB 0.99 0.95 1.00
## PP.BehavInt4_VB 0.99 0.96 0.99
## PP.CCB_48 0.62 0.52 0.62
## PP.CCB_49 0.62 0.51 0.62
## PP.CCB_50 0.64 0.55 0.64
## PP.CCB_51 0.69 0.59 0.68
## PP.CNS_1 0.46 0.44 0.46
## PP.CNS_2 0.43 0.37 0.45
## PP.CNS_3 0.46 0.41 0.46
## PP.ATNS_1 0.07 0.08 0.08
## PP.ATNS_2R -0.43 -0.48 -0.41
## PP.ATNS_3 0.24 0.24 0.25
## PP.ATNS_4 0.39 0.36 0.40
## PP.ATNS_5 0.16 0.14 0.16
## PP.Ind_3 0.31 0.33 0.28
## PP.Ind_4 0.21 0.17 0.21
## PP.Ind_7 0.39 0.37 0.37
## PP.Ind_8 0.28 0.23 0.27
## PP.Ind_1 0.21 0.12 0.21
## PP.Ind_2 0.16 0.08 0.16
## PP.Ind_5 0.35 0.27 0.35
## PP.Ind_6 0.13 0.07 0.13
## PP.BehavInt4_VB PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51
## PP.Nat_1_GFFB -0.13 -0.17 -0.13 -0.12 0.00
## PP.Nat_4R_GFFB -0.66 -0.40 -0.41 -0.44 -0.52
## PP.Nat_2R_GFFB -0.72 -0.53 -0.53 -0.56 -0.64
## PP.Nat_3R_GFFB -0.52 -0.26 -0.27 -0.31 -0.43
## PP.Nat_1_GFPRB -0.09 0.03 0.05 0.00 -0.01
## PP.Nat_4R_GFPRB -0.46 -0.22 -0.24 -0.29 -0.39
## PP.Nat_2R_GFPRB -0.53 -0.26 -0.26 -0.32 -0.39
## PP.Nat_3R_GFPRB -0.53 -0.25 -0.27 -0.31 -0.42
## PP.Nat_1_CBB 0.37 0.10 0.13 0.18 0.34
## PP.Nat_4R_CBB -0.20 -0.39 -0.39 -0.35 -0.33
## PP.Nat_2R_CBB -0.39 -0.53 -0.54 -0.50 -0.48
## PP.Nat_3R_CBB -0.45 -0.57 -0.58 -0.54 -0.54
## PP.Nat_1_PBPB 0.85 0.38 0.38 0.43 0.54
## PP.Nat_4R_PBPB 0.28 0.00 -0.05 -0.03 -0.08
## PP.Nat_2R_PBPB 0.13 -0.12 -0.17 -0.15 -0.20
## PP.Nat_3R_PBPB -0.27 -0.46 -0.50 -0.50 -0.56
## PP.Nat_1_PBFB 0.74 0.36 0.38 0.42 0.55
## PP.Nat_4R_PBFB -0.40 -0.08 -0.05 -0.06 -0.09
## PP.Nat_2R_PBFB 0.03 0.30 0.32 0.33 0.32
## PP.Nat_3R_PBFB 0.15 0.45 0.47 0.48 0.47
## PP.Nat_1_VB 0.91 0.61 0.61 0.61 0.67
## PP.Nat_4R_VB 0.21 0.07 0.03 0.00 -0.08
## PP.Nat_2R_VB -0.03 0.00 -0.02 -0.09 -0.18
## PP.Nat_3R_VB -0.18 -0.20 -0.22 -0.29 -0.38
## PP.BehavInt1_GFFB -0.11 -0.14 -0.10 -0.11 0.01
## PP.BehavInt2_GFFB -0.14 -0.15 -0.10 -0.13 -0.01
## PP.BehavInt3_GFFB -0.09 -0.15 -0.11 -0.11 0.02
## PP.BehavInt4_GFFB -0.14 -0.13 -0.09 -0.10 0.02
## PP.BehavInt1_GFPRB 0.90 0.44 0.44 0.48 0.57
## PP.BehavInt2_GFPRB 0.89 0.40 0.40 0.45 0.55
## PP.BehavInt3_GFPRB 0.91 0.46 0.46 0.50 0.60
## PP.BehavInt4_GFPRB 0.92 0.48 0.48 0.52 0.61
## PP.BehavInt1_CBB 0.50 0.20 0.23 0.27 0.44
## PP.BehavInt2_CBB 0.47 0.16 0.19 0.23 0.40
## PP.BehavInt3_CBB 0.49 0.20 0.22 0.27 0.44
## PP.BehavInt4_CBB 0.49 0.19 0.21 0.26 0.43
## PP.BehavInt1_PBPB 0.90 0.44 0.44 0.48 0.57
## PP.BehavInt2_PBPB 0.89 0.40 0.40 0.45 0.55
## PP.BehavInt3_PBPB 0.91 0.46 0.46 0.50 0.60
## PP.BehavInt4_PBPB 0.92 0.48 0.48 0.52 0.61
## PP.BehavInt1_PBFB 0.85 0.45 0.46 0.50 0.61
## PP.BehavInt2_PBFB 0.82 0.37 0.37 0.42 0.54
## PP.BehavInt3_PBFB 0.85 0.44 0.45 0.49 0.60
## PP.BehavInt4_PBFB 0.84 0.40 0.41 0.45 0.56
## PP.BehavInt1_VB 0.99 0.62 0.62 0.64 0.69
## PP.BehavInt2_VB 0.96 0.52 0.51 0.55 0.59
## PP.BehavInt3_VB 0.99 0.62 0.62 0.64 0.68
## PP.BehavInt4_VB 1.00 0.60 0.59 0.62 0.67
## PP.CCB_48 0.60 1.00 0.98 0.97 0.93
## PP.CCB_49 0.59 0.98 1.00 0.96 0.94
## PP.CCB_50 0.62 0.97 0.96 1.00 0.94
## PP.CCB_51 0.67 0.93 0.94 0.94 1.00
## PP.CNS_1 0.46 0.57 0.60 0.62 0.65
## PP.CNS_2 0.42 0.66 0.68 0.66 0.68
## PP.CNS_3 0.45 0.70 0.72 0.72 0.72
## PP.ATNS_1 0.07 0.25 0.28 0.27 0.27
## PP.ATNS_2R -0.43 -0.11 -0.14 -0.19 -0.32
## PP.ATNS_3 0.23 0.38 0.41 0.41 0.40
## PP.ATNS_4 0.38 0.57 0.58 0.59 0.56
## PP.ATNS_5 0.14 0.41 0.44 0.42 0.40
## PP.Ind_3 0.31 0.16 0.17 0.21 0.26
## PP.Ind_4 0.20 0.35 0.36 0.35 0.34
## PP.Ind_7 0.37 0.29 0.31 0.32 0.37
## PP.Ind_8 0.26 0.35 0.37 0.36 0.37
## PP.Ind_1 0.19 0.54 0.55 0.52 0.48
## PP.Ind_2 0.14 0.46 0.48 0.45 0.41
## PP.Ind_5 0.33 0.57 0.58 0.57 0.54
## PP.Ind_6 0.12 0.41 0.42 0.39 0.37
## PP.CNS_1 PP.CNS_2 PP.CNS_3 PP.ATNS_1 PP.ATNS_2R PP.ATNS_3
## PP.Nat_1_GFFB 0.24 0.23 0.17 0.42 -0.54 0.24
## PP.Nat_4R_GFFB -0.51 -0.36 -0.39 -0.25 0.66 -0.35
## PP.Nat_2R_GFFB -0.55 -0.46 -0.48 -0.26 0.55 -0.35
## PP.Nat_3R_GFFB -0.53 -0.40 -0.38 -0.37 0.77 -0.38
## PP.Nat_1_GFPRB 0.08 0.28 0.14 0.26 0.19 0.13
## PP.Nat_4R_GFPRB -0.52 -0.32 -0.39 -0.33 0.77 -0.42
## PP.Nat_2R_GFPRB -0.48 -0.30 -0.38 -0.27 0.75 -0.34
## PP.Nat_3R_GFPRB -0.50 -0.32 -0.36 -0.22 0.80 -0.28
## PP.Nat_1_CBB 0.48 0.26 0.30 0.20 -0.85 0.19
## PP.Nat_4R_CBB -0.36 -0.53 -0.47 -0.62 -0.04 -0.60
## PP.Nat_2R_CBB -0.43 -0.61 -0.54 -0.55 -0.06 -0.58
## PP.Nat_3R_CBB -0.45 -0.59 -0.53 -0.46 0.04 -0.46
## PP.Nat_1_PBPB 0.42 0.29 0.30 0.02 -0.64 0.16
## PP.Nat_4R_PBPB -0.30 -0.33 -0.30 -0.50 0.28 -0.43
## PP.Nat_2R_PBPB -0.41 -0.45 -0.41 -0.59 0.33 -0.50
## PP.Nat_3R_PBPB -0.51 -0.57 -0.52 -0.39 0.39 -0.36
## PP.Nat_1_PBFB 0.49 0.33 0.37 0.07 -0.74 0.19
## PP.Nat_4R_PBFB 0.02 0.14 0.07 0.47 -0.01 0.30
## PP.Nat_2R_PBFB 0.40 0.48 0.43 0.63 -0.22 0.55
## PP.Nat_3R_PBFB 0.39 0.50 0.46 0.50 -0.20 0.46
## PP.Nat_1_VB 0.49 0.47 0.49 0.19 -0.39 0.32
## PP.Nat_4R_VB -0.35 -0.22 -0.24 -0.45 0.55 -0.32
## PP.Nat_2R_VB -0.44 -0.26 -0.30 -0.45 0.66 -0.37
## PP.Nat_3R_VB -0.54 -0.40 -0.44 -0.45 0.64 -0.40
## PP.BehavInt1_GFFB 0.25 0.24 0.20 0.43 -0.54 0.26
## PP.BehavInt2_GFFB 0.22 0.23 0.18 0.43 -0.47 0.25
## PP.BehavInt3_GFFB 0.27 0.24 0.20 0.44 -0.57 0.28
## PP.BehavInt4_GFFB 0.27 0.28 0.23 0.47 -0.50 0.30
## PP.BehavInt1_GFPRB 0.32 0.23 0.24 -0.11 -0.52 0.03
## PP.BehavInt2_GFPRB 0.33 0.22 0.24 -0.08 -0.57 0.06
## PP.BehavInt3_GFPRB 0.34 0.24 0.27 -0.08 -0.55 0.06
## PP.BehavInt4_GFPRB 0.34 0.26 0.27 -0.06 -0.54 0.08
## PP.BehavInt1_CBB 0.44 0.25 0.29 0.07 -0.84 0.08
## PP.BehavInt2_CBB 0.45 0.25 0.29 0.11 -0.86 0.10
## PP.BehavInt3_CBB 0.44 0.25 0.28 0.08 -0.85 0.09
## PP.BehavInt4_CBB 0.44 0.24 0.28 0.07 -0.84 0.08
## PP.BehavInt1_PBPB 0.32 0.23 0.24 -0.11 -0.52 0.03
## PP.BehavInt2_PBPB 0.33 0.22 0.24 -0.08 -0.57 0.06
## PP.BehavInt3_PBPB 0.34 0.24 0.27 -0.08 -0.55 0.06
## PP.BehavInt4_PBPB 0.34 0.26 0.27 -0.06 -0.54 0.08
## PP.BehavInt1_PBFB 0.42 0.30 0.34 -0.01 -0.68 0.11
## PP.BehavInt2_PBFB 0.38 0.25 0.28 -0.02 -0.73 0.10
## PP.BehavInt3_PBFB 0.43 0.31 0.34 0.01 -0.70 0.13
## PP.BehavInt4_PBFB 0.39 0.27 0.30 -0.03 -0.71 0.09
## PP.BehavInt1_VB 0.46 0.43 0.46 0.07 -0.43 0.24
## PP.BehavInt2_VB 0.44 0.37 0.41 0.08 -0.48 0.24
## PP.BehavInt3_VB 0.46 0.45 0.46 0.08 -0.41 0.25
## PP.BehavInt4_VB 0.46 0.42 0.45 0.07 -0.43 0.23
## PP.CCB_48 0.57 0.66 0.70 0.25 -0.11 0.38
## PP.CCB_49 0.60 0.68 0.72 0.28 -0.14 0.41
## PP.CCB_50 0.62 0.66 0.72 0.27 -0.19 0.41
## PP.CCB_51 0.65 0.68 0.72 0.27 -0.32 0.40
## PP.CNS_1 1.00 0.87 0.88 0.62 -0.40 0.67
## PP.CNS_2 0.87 1.00 0.91 0.64 -0.23 0.68
## PP.CNS_3 0.88 0.91 1.00 0.59 -0.25 0.63
## PP.ATNS_1 0.62 0.64 0.59 1.00 -0.24 0.82
## PP.ATNS_2R -0.40 -0.23 -0.25 -0.24 1.00 -0.19
## PP.ATNS_3 0.67 0.68 0.63 0.82 -0.19 1.00
## PP.ATNS_4 0.71 0.76 0.73 0.73 -0.17 0.86
## PP.ATNS_5 0.61 0.67 0.62 0.82 -0.09 0.88
## PP.Ind_3 0.58 0.51 0.49 0.57 -0.47 0.57
## PP.Ind_4 0.62 0.67 0.64 0.63 -0.13 0.61
## PP.Ind_7 0.61 0.60 0.57 0.60 -0.48 0.60
## PP.Ind_8 0.58 0.66 0.61 0.60 -0.20 0.59
## PP.Ind_1 0.63 0.72 0.73 0.55 0.03 0.56
## PP.Ind_2 0.62 0.69 0.69 0.55 0.02 0.57
## PP.Ind_5 0.70 0.76 0.75 0.56 -0.12 0.59
## PP.Ind_6 0.59 0.67 0.65 0.60 -0.03 0.59
## PP.ATNS_4 PP.ATNS_5 PP.Ind_3 PP.Ind_4 PP.Ind_7 PP.Ind_8
## PP.Nat_1_GFFB 0.15 0.23 0.45 0.39 0.44 0.43
## PP.Nat_4R_GFFB -0.34 -0.22 -0.44 -0.10 -0.44 -0.15
## PP.Nat_2R_GFFB -0.40 -0.25 -0.40 -0.16 -0.41 -0.21
## PP.Nat_3R_GFFB -0.36 -0.26 -0.58 -0.25 -0.59 -0.32
## PP.Nat_1_GFPRB 0.17 0.23 0.15 0.46 0.17 0.49
## PP.Nat_4R_GFPRB -0.36 -0.27 -0.56 -0.18 -0.55 -0.21
## PP.Nat_2R_GFPRB -0.30 -0.18 -0.49 -0.14 -0.51 -0.16
## PP.Nat_3R_GFPRB -0.28 -0.12 -0.51 -0.14 -0.51 -0.19
## PP.Nat_1_CBB 0.16 0.05 0.48 0.11 0.45 0.18
## PP.Nat_4R_CBB -0.59 -0.68 -0.35 -0.59 -0.44 -0.62
## PP.Nat_2R_CBB -0.61 -0.64 -0.31 -0.59 -0.40 -0.61
## PP.Nat_3R_CBB -0.54 -0.53 -0.26 -0.51 -0.35 -0.55
## PP.Nat_1_PBPB 0.25 0.00 0.34 0.04 0.36 0.15
## PP.Nat_4R_PBPB -0.31 -0.46 -0.33 -0.42 -0.39 -0.40
## PP.Nat_2R_PBPB -0.41 -0.52 -0.42 -0.57 -0.49 -0.54
## PP.Nat_3R_PBPB -0.44 -0.40 -0.34 -0.46 -0.41 -0.49
## PP.Nat_1_PBFB 0.26 0.04 0.34 0.04 0.38 0.12
## PP.Nat_4R_PBFB 0.22 0.43 0.17 0.31 0.16 0.25
## PP.Nat_2R_PBFB 0.50 0.62 0.43 0.60 0.45 0.54
## PP.Nat_3R_PBFB 0.49 0.53 0.33 0.56 0.37 0.51
## PP.Nat_1_VB 0.45 0.23 0.29 0.22 0.37 0.31
## PP.Nat_4R_VB -0.21 -0.30 -0.42 -0.28 -0.40 -0.26
## PP.Nat_2R_VB -0.29 -0.29 -0.56 -0.37 -0.52 -0.34
## PP.Nat_3R_VB -0.38 -0.35 -0.60 -0.43 -0.58 -0.40
## PP.BehavInt1_GFFB 0.19 0.26 0.44 0.40 0.49 0.47
## PP.BehavInt2_GFFB 0.18 0.26 0.39 0.39 0.46 0.46
## PP.BehavInt3_GFFB 0.20 0.26 0.45 0.37 0.49 0.44
## PP.BehavInt4_GFFB 0.22 0.30 0.44 0.41 0.49 0.48
## PP.BehavInt1_GFPRB 0.19 -0.07 0.19 -0.04 0.22 0.08
## PP.BehavInt2_GFPRB 0.19 -0.07 0.22 -0.06 0.23 0.04
## PP.BehavInt3_GFPRB 0.21 -0.04 0.22 -0.02 0.25 0.09
## PP.BehavInt4_GFPRB 0.23 -0.02 0.22 0.00 0.25 0.11
## PP.BehavInt1_CBB 0.13 -0.05 0.36 0.02 0.36 0.12
## PP.BehavInt2_CBB 0.13 -0.03 0.40 0.05 0.38 0.14
## PP.BehavInt3_CBB 0.14 -0.03 0.38 0.02 0.38 0.12
## PP.BehavInt4_CBB 0.13 -0.05 0.37 0.02 0.36 0.11
## PP.BehavInt1_PBPB 0.19 -0.07 0.19 -0.04 0.22 0.08
## PP.BehavInt2_PBPB 0.19 -0.07 0.22 -0.06 0.23 0.04
## PP.BehavInt3_PBPB 0.21 -0.04 0.22 -0.02 0.25 0.09
## PP.BehavInt4_PBPB 0.23 -0.02 0.22 0.00 0.25 0.11
## PP.BehavInt1_PBFB 0.23 -0.01 0.25 0.00 0.31 0.09
## PP.BehavInt2_PBFB 0.19 -0.04 0.29 -0.02 0.33 0.07
## PP.BehavInt3_PBFB 0.25 0.01 0.26 0.02 0.33 0.10
## PP.BehavInt4_PBFB 0.21 -0.04 0.25 -0.01 0.31 0.07
## PP.BehavInt1_VB 0.39 0.16 0.31 0.21 0.39 0.28
## PP.BehavInt2_VB 0.36 0.14 0.33 0.17 0.37 0.23
## PP.BehavInt3_VB 0.40 0.16 0.28 0.21 0.37 0.27
## PP.BehavInt4_VB 0.38 0.14 0.31 0.20 0.37 0.26
## PP.CCB_48 0.57 0.41 0.16 0.35 0.29 0.35
## PP.CCB_49 0.58 0.44 0.17 0.36 0.31 0.37
## PP.CCB_50 0.59 0.42 0.21 0.35 0.32 0.36
## PP.CCB_51 0.56 0.40 0.26 0.34 0.37 0.37
## PP.CNS_1 0.71 0.61 0.58 0.62 0.61 0.58
## PP.CNS_2 0.76 0.67 0.51 0.67 0.60 0.66
## PP.CNS_3 0.73 0.62 0.49 0.64 0.57 0.61
## PP.ATNS_1 0.73 0.82 0.57 0.63 0.60 0.60
## PP.ATNS_2R -0.17 -0.09 -0.47 -0.13 -0.48 -0.20
## PP.ATNS_3 0.86 0.88 0.57 0.61 0.60 0.59
## PP.ATNS_4 1.00 0.83 0.46 0.56 0.55 0.55
## PP.ATNS_5 0.83 1.00 0.50 0.63 0.57 0.58
## PP.Ind_3 0.46 0.50 1.00 0.72 0.89 0.70
## PP.Ind_4 0.56 0.63 0.72 1.00 0.73 0.87
## PP.Ind_7 0.55 0.57 0.89 0.73 1.00 0.73
## PP.Ind_8 0.55 0.58 0.70 0.87 0.73 1.00
## PP.Ind_1 0.59 0.59 0.45 0.77 0.54 0.74
## PP.Ind_2 0.57 0.58 0.42 0.70 0.51 0.69
## PP.Ind_5 0.63 0.59 0.53 0.74 0.61 0.76
## PP.Ind_6 0.55 0.60 0.55 0.78 0.62 0.78
## PP.Ind_1 PP.Ind_2 PP.Ind_5 PP.Ind_6
## PP.Nat_1_GFFB 0.20 0.21 0.24 0.27
## PP.Nat_4R_GFFB -0.05 -0.08 -0.22 -0.09
## PP.Nat_2R_GFFB -0.16 -0.17 -0.34 -0.16
## PP.Nat_3R_GFFB -0.14 -0.15 -0.30 -0.19
## PP.Nat_1_GFPRB 0.46 0.43 0.39 0.45
## PP.Nat_4R_GFPRB -0.06 -0.10 -0.20 -0.13
## PP.Nat_2R_GFPRB -0.03 -0.07 -0.17 -0.09
## PP.Nat_3R_GFPRB -0.05 -0.08 -0.22 -0.08
## PP.Nat_1_CBB -0.02 -0.01 0.13 0.05
## PP.Nat_4R_CBB -0.61 -0.59 -0.60 -0.55
## PP.Nat_2R_CBB -0.65 -0.60 -0.67 -0.57
## PP.Nat_3R_CBB -0.63 -0.56 -0.65 -0.48
## PP.Nat_1_PBPB -0.03 -0.04 0.16 -0.03
## PP.Nat_4R_PBPB -0.38 -0.44 -0.36 -0.41
## PP.Nat_2R_PBPB -0.50 -0.49 -0.49 -0.52
## PP.Nat_3R_PBPB -0.50 -0.44 -0.54 -0.40
## PP.Nat_1_PBFB -0.02 -0.01 0.15 -0.02
## PP.Nat_4R_PBFB 0.29 0.29 0.21 0.30
## PP.Nat_2R_PBFB 0.55 0.51 0.54 0.53
## PP.Nat_3R_PBFB 0.54 0.50 0.54 0.47
## PP.Nat_1_VB 0.28 0.25 0.41 0.20
## PP.Nat_4R_VB -0.17 -0.21 -0.19 -0.26
## PP.Nat_2R_VB -0.18 -0.19 -0.26 -0.27
## PP.Nat_3R_VB -0.26 -0.23 -0.35 -0.30
## PP.BehavInt1_GFFB 0.23 0.23 0.27 0.31
## PP.BehavInt2_GFFB 0.25 0.25 0.28 0.32
## PP.BehavInt3_GFFB 0.19 0.20 0.25 0.29
## PP.BehavInt4_GFFB 0.25 0.24 0.28 0.33
## PP.BehavInt1_GFPRB -0.04 -0.08 0.12 -0.10
## PP.BehavInt2_GFPRB -0.09 -0.11 0.09 -0.11
## PP.BehavInt3_GFPRB -0.04 -0.08 0.13 -0.09
## PP.BehavInt4_GFPRB -0.02 -0.06 0.15 -0.07
## PP.BehavInt1_CBB -0.04 -0.03 0.13 -0.02
## PP.BehavInt2_CBB -0.04 -0.03 0.12 -0.01
## PP.BehavInt3_CBB -0.04 -0.03 0.13 -0.02
## PP.BehavInt4_CBB -0.05 -0.04 0.13 -0.02
## PP.BehavInt1_PBPB -0.04 -0.08 0.12 -0.10
## PP.BehavInt2_PBPB -0.09 -0.11 0.09 -0.11
## PP.BehavInt3_PBPB -0.04 -0.08 0.13 -0.09
## PP.BehavInt4_PBPB -0.02 -0.06 0.15 -0.07
## PP.BehavInt1_PBFB -0.03 -0.05 0.13 -0.06
## PP.BehavInt2_PBFB -0.09 -0.10 0.08 -0.10
## PP.BehavInt3_PBFB -0.03 -0.04 0.14 -0.06
## PP.BehavInt4_PBFB -0.06 -0.08 0.11 -0.10
## PP.BehavInt1_VB 0.21 0.16 0.35 0.13
## PP.BehavInt2_VB 0.12 0.08 0.27 0.07
## PP.BehavInt3_VB 0.21 0.16 0.35 0.13
## PP.BehavInt4_VB 0.19 0.14 0.33 0.12
## PP.CCB_48 0.54 0.46 0.57 0.41
## PP.CCB_49 0.55 0.48 0.58 0.42
## PP.CCB_50 0.52 0.45 0.57 0.39
## PP.CCB_51 0.48 0.41 0.54 0.37
## PP.CNS_1 0.63 0.62 0.70 0.59
## PP.CNS_2 0.72 0.69 0.76 0.67
## PP.CNS_3 0.73 0.69 0.75 0.65
## PP.ATNS_1 0.55 0.55 0.56 0.60
## PP.ATNS_2R 0.03 0.02 -0.12 -0.03
## PP.ATNS_3 0.56 0.57 0.59 0.59
## PP.ATNS_4 0.59 0.57 0.63 0.55
## PP.ATNS_5 0.59 0.58 0.59 0.60
## PP.Ind_3 0.45 0.42 0.53 0.55
## PP.Ind_4 0.77 0.70 0.74 0.78
## PP.Ind_7 0.54 0.51 0.61 0.62
## PP.Ind_8 0.74 0.69 0.76 0.78
## PP.Ind_1 1.00 0.90 0.90 0.85
## PP.Ind_2 0.90 1.00 0.87 0.82
## PP.Ind_5 0.90 0.87 1.00 0.81
## PP.Ind_6 0.85 0.82 0.81 1.00
##
## n= 68
##
##
## P
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Nat_1_GFFB 0.8446 0.8908 0.0004
## PP.Nat_4R_GFFB 0.8446 0.0000 0.0000
## PP.Nat_2R_GFFB 0.8908 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0004 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0001 0.0046 0.0721 0.3669
## PP.Nat_4R_GFPRB 0.0250 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0683 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0105 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0769 0.6710 0.3989 0.5284
## PP.Nat_2R_CBB 0.4948 0.3310 0.0354 0.4028
## PP.Nat_3R_CBB 0.3601 0.1873 0.0076 0.2164
## PP.Nat_1_PBPB 0.7579 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0000 0.9611 0.8901 0.0632
## PP.Nat_2R_PBPB 0.0000 0.9946 0.7492 0.0200
## PP.Nat_3R_PBPB 0.0000 0.1058 0.0050 0.0007
## PP.Nat_1_PBFB 0.2575 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0686 0.0508 0.9713
## PP.Nat_2R_PBFB 0.0000 0.7049 0.2470 0.0305
## PP.Nat_3R_PBFB 0.0000 0.5294 0.0953 0.0130
## PP.Nat_1_VB 0.4088 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.0000 0.0209 0.0985 0.0000
## PP.Nat_2R_VB 0.0000 0.0009 0.0054 0.0000
## PP.Nat_3R_VB 0.0000 0.0007 0.0007 0.0000
## PP.BehavInt1_GFFB 0.0000 0.6958 0.9854 0.0002
## PP.BehavInt2_GFFB 0.0000 0.7999 0.6078 0.0039
## PP.BehavInt3_GFFB 0.0000 0.4493 0.7700 0.0000
## PP.BehavInt4_GFFB 0.0000 0.8577 0.8744 0.0007
## PP.BehavInt1_GFPRB 0.4558 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.4866 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.5389 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.5219 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0011 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0002 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0006 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0006 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.4558 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.4866 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.5389 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.5219 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.9221 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.4798 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.6266 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.5611 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.3418 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.2906 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.3024 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.2989 0.0000 0.0000 0.0000
## PP.CCB_48 0.1575 0.0008 0.0000 0.0351
## PP.CCB_49 0.2762 0.0005 0.0000 0.0243
## PP.CCB_50 0.3391 0.0002 0.0000 0.0095
## PP.CCB_51 0.9993 0.0000 0.0000 0.0003
## PP.CNS_1 0.0452 0.0000 0.0000 0.0000
## PP.CNS_2 0.0636 0.0025 0.0000 0.0008
## PP.CNS_3 0.1556 0.0009 0.0000 0.0012
## PP.ATNS_1 0.0004 0.0398 0.0348 0.0021
## PP.ATNS_2R 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_3 0.0531 0.0039 0.0035 0.0015
## PP.ATNS_4 0.2079 0.0040 0.0007 0.0024
## PP.ATNS_5 0.0595 0.0709 0.0383 0.0317
## PP.Ind_3 0.0001 0.0002 0.0008 0.0000
## PP.Ind_4 0.0010 0.4363 0.1926 0.0389
## PP.Ind_7 0.0002 0.0001 0.0005 0.0000
## PP.Ind_8 0.0003 0.2254 0.0883 0.0087
## PP.Ind_1 0.1021 0.6618 0.1830 0.2610
## PP.Ind_2 0.0852 0.5195 0.1549 0.2347
## PP.Ind_5 0.0512 0.0719 0.0051 0.0122
## PP.Ind_6 0.0261 0.4567 0.2028 0.1224
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Nat_1_GFFB 0.0001 0.0250 0.0683
## PP.Nat_4R_GFFB 0.0046 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0721 0.0000 0.0000
## PP.Nat_3R_GFFB 0.3669 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0001 0.0003
## PP.Nat_4R_GFPRB 0.0001 0.0000
## PP.Nat_2R_GFPRB 0.0003 0.0000
## PP.Nat_3R_GFPRB 0.0114 0.0000 0.0000
## PP.Nat_1_CBB 0.0343 0.0000 0.0000
## PP.Nat_4R_CBB 0.0001 0.9530 0.6969
## PP.Nat_2R_CBB 0.0000 0.7173 0.5609
## PP.Nat_3R_CBB 0.0002 0.9557 0.8117
## PP.Nat_1_PBPB 0.0163 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0091 0.1055 0.6681
## PP.Nat_2R_PBPB 0.0019 0.1197 0.5770
## PP.Nat_3R_PBPB 0.0000 0.1549 0.4847
## PP.Nat_1_PBFB 0.0042 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.3579 0.0857
## PP.Nat_2R_PBFB 0.0000 0.2977 0.9140
## PP.Nat_3R_PBFB 0.0000 0.4908 0.8756
## PP.Nat_1_VB 0.4852 0.0000 0.0000
## PP.Nat_4R_VB 0.5047 0.0000 0.0006
## PP.Nat_2R_VB 0.9601 0.0000 0.0000
## PP.Nat_3R_VB 0.7210 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0017 0.0142 0.0546
## PP.BehavInt2_GFFB 0.0002 0.1079 0.2747
## PP.BehavInt3_GFFB 0.0053 0.0053 0.0229
## PP.BehavInt4_GFFB 0.0017 0.0204 0.0740
## PP.BehavInt1_GFPRB 0.1092 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0207 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0642 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0973 0.0000 0.0000
## PP.BehavInt1_CBB 0.0449 0.0000 0.0000
## PP.BehavInt2_CBB 0.0486 0.0000 0.0000
## PP.BehavInt3_CBB 0.0381 0.0000 0.0000
## PP.BehavInt4_CBB 0.0447 0.0000 0.0000
## PP.BehavInt1_PBPB 0.1092 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0207 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0642 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0973 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0107 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0084 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0267 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0291 0.0000 0.0000
## PP.BehavInt1_VB 0.4447 0.0000 0.0000
## PP.BehavInt2_VB 0.1199 0.0000 0.0000
## PP.BehavInt3_VB 0.6061 0.0003 0.0000
## PP.BehavInt4_VB 0.4630 0.0000 0.0000
## PP.CCB_48 0.7977 0.0660 0.0329
## PP.CCB_49 0.6970 0.0458 0.0308
## PP.CCB_50 0.9745 0.0159 0.0085
## PP.CCB_51 0.9558 0.0009 0.0010
## PP.CNS_1 0.5228 0.0000 0.0000
## PP.CNS_2 0.0208 0.0079 0.0132
## PP.CNS_3 0.2428 0.0010 0.0012
## PP.ATNS_1 0.0316 0.0059 0.0250
## PP.ATNS_2R 0.1120 0.0000 0.0000
## PP.ATNS_3 0.2982 0.0004 0.0049
## PP.ATNS_4 0.1755 0.0026 0.0118
## PP.ATNS_5 0.0546 0.0261 0.1322
## PP.Ind_3 0.2126 0.0000 0.0000
## PP.Ind_4 0.0000 0.1399 0.2646
## PP.Ind_7 0.1717 0.0000 0.0000
## PP.Ind_8 0.0000 0.0829 0.1874
## PP.Ind_1 0.0000 0.6318 0.7991
## PP.Ind_2 0.0003 0.3962 0.5704
## PP.Ind_5 0.0009 0.0967 0.1752
## PP.Ind_6 0.0001 0.2829 0.4690
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Nat_1_GFFB 0.0105 0.0000 0.0769 0.4948
## PP.Nat_4R_GFFB 0.0000 0.0000 0.6710 0.3310
## PP.Nat_2R_GFFB 0.0000 0.0000 0.3989 0.0354
## PP.Nat_3R_GFFB 0.0000 0.0000 0.5284 0.4028
## PP.Nat_1_GFPRB 0.0114 0.0343 0.0001 0.0000
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.9530 0.7173
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.6969 0.5609
## PP.Nat_3R_GFPRB 0.0000 0.4993 0.5916
## PP.Nat_1_CBB 0.0000 0.0861 0.1258
## PP.Nat_4R_CBB 0.4993 0.0861 0.0000
## PP.Nat_2R_CBB 0.5916 0.1258 0.0000
## PP.Nat_3R_CBB 0.7788 0.5531 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.4981 0.5982
## PP.Nat_4R_PBPB 0.6308 0.0636 0.0015 0.0687
## PP.Nat_2R_PBPB 0.3508 0.0219 0.0000 0.0003
## PP.Nat_3R_PBPB 0.0348 0.0029 0.0067 0.0003
## PP.Nat_1_PBFB 0.0000 0.0000 0.2264 0.7796
## PP.Nat_4R_PBFB 0.1575 0.1825 0.0000 0.0011
## PP.Nat_2R_PBFB 0.5092 0.4071 0.0000 0.0000
## PP.Nat_3R_PBFB 0.3336 0.3766 0.0000 0.0000
## PP.Nat_1_VB 0.0000 0.0020 0.0426 0.0006
## PP.Nat_4R_VB 0.0001 0.0000 0.4759 0.5536
## PP.Nat_2R_VB 0.0000 0.0000 0.6403 0.7751
## PP.Nat_3R_VB 0.0000 0.0000 0.5774 0.8438
## PP.BehavInt1_GFFB 0.0062 0.0000 0.0097 0.1949
## PP.BehavInt2_GFFB 0.0424 0.0005 0.0032 0.0957
## PP.BehavInt3_GFFB 0.0023 0.0000 0.0145 0.2588
## PP.BehavInt4_GFFB 0.0181 0.0000 0.0050 0.1675
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.5091 0.3064
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.3501 0.5338
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.5284 0.3225
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.8186 0.1499
## PP.BehavInt1_CBB 0.0000 0.0000 0.0568 0.2309
## PP.BehavInt2_CBB 0.0000 0.0000 0.0637 0.1704
## PP.BehavInt3_CBB 0.0000 0.0000 0.0691 0.2089
## PP.BehavInt4_CBB 0.0000 0.0000 0.0489 0.1902
## PP.BehavInt1_PBPB 0.0000 0.0000 0.5091 0.3064
## PP.BehavInt2_PBPB 0.0000 0.0000 0.3501 0.5338
## PP.BehavInt3_PBPB 0.0000 0.0000 0.5284 0.3225
## PP.BehavInt4_PBPB 0.0000 0.0000 0.8186 0.1499
## PP.BehavInt1_PBFB 0.0000 0.0000 0.4389 0.6407
## PP.BehavInt2_PBFB 0.0000 0.0000 0.2491 0.9360
## PP.BehavInt3_PBFB 0.0000 0.0000 0.5382 0.5527
## PP.BehavInt4_PBFB 0.0000 0.0000 0.3441 0.8124
## PP.BehavInt1_VB 0.0000 0.0012 0.1152 0.0010
## PP.BehavInt2_VB 0.0000 0.0003 0.2370 0.0097
## PP.BehavInt3_VB 0.0000 0.0027 0.0723 0.0003
## PP.BehavInt4_VB 0.0000 0.0016 0.1051 0.0011
## PP.CCB_48 0.0416 0.4049 0.0012 0.0000
## PP.CCB_49 0.0269 0.2777 0.0009 0.0000
## PP.CCB_50 0.0107 0.1500 0.0033 0.0000
## PP.CCB_51 0.0004 0.0051 0.0054 0.0000
## PP.CNS_1 0.0000 0.0000 0.0025 0.0002
## PP.CNS_2 0.0074 0.0299 0.0000 0.0000
## PP.CNS_3 0.0024 0.0133 0.0000 0.0000
## PP.ATNS_1 0.0772 0.0938 0.0000 0.0000
## PP.ATNS_2R 0.0000 0.0000 0.7531 0.6518
## PP.ATNS_3 0.0223 0.1135 0.0000 0.0000
## PP.ATNS_4 0.0218 0.1937 0.0000 0.0000
## PP.ATNS_5 0.3213 0.6879 0.0000 0.0000
## PP.Ind_3 0.0000 0.0000 0.0034 0.0093
## PP.Ind_4 0.2684 0.3737 0.0000 0.0000
## PP.Ind_7 0.0000 0.0001 0.0002 0.0007
## PP.Ind_8 0.1217 0.1332 0.0000 0.0000
## PP.Ind_1 0.6965 0.8438 0.0000 0.0000
## PP.Ind_2 0.5355 0.9307 0.0000 0.0000
## PP.Ind_5 0.0752 0.2848 0.0000 0.0000
## PP.Ind_6 0.5155 0.6812 0.0000 0.0000
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Nat_1_GFFB 0.3601 0.7579 0.0000 0.0000
## PP.Nat_4R_GFFB 0.1873 0.0000 0.9611 0.9946
## PP.Nat_2R_GFFB 0.0076 0.0000 0.8901 0.7492
## PP.Nat_3R_GFFB 0.2164 0.0000 0.0632 0.0200
## PP.Nat_1_GFPRB 0.0002 0.0163 0.0091 0.0019
## PP.Nat_4R_GFPRB 0.9557 0.0000 0.1055 0.1197
## PP.Nat_2R_GFPRB 0.8117 0.0000 0.6681 0.5770
## PP.Nat_3R_GFPRB 0.7788 0.0000 0.6308 0.3508
## PP.Nat_1_CBB 0.5531 0.0000 0.0636 0.0219
## PP.Nat_4R_CBB 0.0000 0.4981 0.0015 0.0000
## PP.Nat_2R_CBB 0.0000 0.5982 0.0687 0.0003
## PP.Nat_3R_CBB 0.1734 0.1929 0.0007
## PP.Nat_1_PBPB 0.1734 0.0229 0.1988
## PP.Nat_4R_PBPB 0.1929 0.0229 0.0000
## PP.Nat_2R_PBPB 0.0007 0.1988 0.0000
## PP.Nat_3R_PBPB 0.0000 0.1029 0.0000 0.0000
## PP.Nat_1_PBFB 0.6233 0.0000 0.5607 0.8555
## PP.Nat_4R_PBFB 0.0086 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.4890 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.8514 0.0000 0.0000
## PP.Nat_1_VB 0.0000 0.0000 0.0414 0.5126
## PP.Nat_4R_VB 0.6005 0.9911 0.0000 0.0000
## PP.Nat_2R_VB 0.8863 0.0246 0.0000 0.0000
## PP.Nat_3R_VB 0.7252 0.0013 0.0000 0.0000
## PP.BehavInt1_GFFB 0.1370 0.7074 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0553 0.8096 0.0000 0.0000
## PP.BehavInt3_GFFB 0.1818 0.4798 0.0000 0.0000
## PP.BehavInt4_GFFB 0.1272 0.9063 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0378 0.0000 0.0068 0.0788
## PP.BehavInt2_GFPRB 0.1173 0.0000 0.0110 0.0945
## PP.BehavInt3_GFPRB 0.0549 0.0000 0.0154 0.1231
## PP.BehavInt4_GFPRB 0.0186 0.0000 0.0193 0.1761
## PP.BehavInt1_CBB 0.9713 0.0000 0.2162 0.1080
## PP.BehavInt2_CBB 0.8531 0.0000 0.1551 0.0624
## PP.BehavInt3_CBB 0.9601 0.0000 0.1858 0.0979
## PP.BehavInt4_CBB 0.9044 0.0000 0.2256 0.1088
## PP.BehavInt1_PBPB 0.0378 0.0000 0.0068 0.0788
## PP.BehavInt2_PBPB 0.1173 0.0000 0.0110 0.0945
## PP.BehavInt3_PBPB 0.0549 0.0000 0.0154 0.1231
## PP.BehavInt4_PBPB 0.0186 0.0000 0.0193 0.1761
## PP.BehavInt1_PBFB 0.1434 0.0000 0.1972 0.5137
## PP.BehavInt2_PBFB 0.4022 0.0000 0.2147 0.5560
## PP.BehavInt3_PBFB 0.1239 0.0000 0.3248 0.7166
## PP.BehavInt4_PBFB 0.2259 0.0000 0.2377 0.5291
## PP.BehavInt1_VB 0.0000 0.0000 0.0222 0.3927
## PP.BehavInt2_VB 0.0015 0.0000 0.0043 0.1284
## PP.BehavInt3_VB 0.0000 0.0000 0.0239 0.3608
## PP.BehavInt4_VB 0.0001 0.0000 0.0188 0.2822
## PP.CCB_48 0.0000 0.0013 0.9701 0.3137
## PP.CCB_49 0.0000 0.0014 0.7033 0.1682
## PP.CCB_50 0.0000 0.0002 0.8303 0.2353
## PP.CCB_51 0.0000 0.0000 0.5316 0.1070
## PP.CNS_1 0.0001 0.0004 0.0141 0.0005
## PP.CNS_2 0.0000 0.0181 0.0052 0.0001
## PP.CNS_3 0.0000 0.0121 0.0119 0.0005
## PP.ATNS_1 0.0000 0.8487 0.0000 0.0000
## PP.ATNS_2R 0.7356 0.0000 0.0227 0.0068
## PP.ATNS_3 0.0000 0.2018 0.0002 0.0000
## PP.ATNS_4 0.0000 0.0416 0.0095 0.0005
## PP.ATNS_5 0.0000 0.9729 0.0000 0.0000
## PP.Ind_3 0.0292 0.0043 0.0054 0.0003
## PP.Ind_4 0.0000 0.7243 0.0004 0.0000
## PP.Ind_7 0.0034 0.0029 0.0010 0.0000
## PP.Ind_8 0.0000 0.2272 0.0006 0.0000
## PP.Ind_1 0.0000 0.8164 0.0016 0.0000
## PP.Ind_2 0.0000 0.7555 0.0002 0.0000
## PP.Ind_5 0.0000 0.1826 0.0023 0.0000
## PP.Ind_6 0.0000 0.8031 0.0005 0.0000
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Nat_1_GFFB 0.0000 0.2575 0.0000 0.0000
## PP.Nat_4R_GFFB 0.1058 0.0000 0.0686 0.7049
## PP.Nat_2R_GFFB 0.0050 0.0000 0.0508 0.2470
## PP.Nat_3R_GFFB 0.0007 0.0000 0.9713 0.0305
## PP.Nat_1_GFPRB 0.0000 0.0042 0.0000 0.0000
## PP.Nat_4R_GFPRB 0.1549 0.0000 0.3579 0.2977
## PP.Nat_2R_GFPRB 0.4847 0.0000 0.0857 0.9140
## PP.Nat_3R_GFPRB 0.0348 0.0000 0.1575 0.5092
## PP.Nat_1_CBB 0.0029 0.0000 0.1825 0.4071
## PP.Nat_4R_CBB 0.0067 0.2264 0.0000 0.0000
## PP.Nat_2R_CBB 0.0003 0.7796 0.0011 0.0000
## PP.Nat_3R_CBB 0.0000 0.6233 0.0086 0.0000
## PP.Nat_1_PBPB 0.1029 0.0000 0.0000 0.4890
## PP.Nat_4R_PBPB 0.0000 0.5607 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.8555 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0245 0.0005 0.0000
## PP.Nat_1_PBFB 0.0245 0.0002 0.4891
## PP.Nat_4R_PBFB 0.0005 0.0002 0.0000
## PP.Nat_2R_PBFB 0.0000 0.4891 0.0000
## PP.Nat_3R_PBFB 0.0000 0.8390 0.0000 0.0000
## PP.Nat_1_VB 0.0349 0.0000 0.0008 0.7995
## PP.Nat_4R_VB 0.0000 0.1053 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0010 0.0040 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0148 0.0000
## PP.BehavInt1_GFFB 0.0000 0.2098 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.5594 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.1174 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.3261 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0396 0.0000 0.0000 0.3069
## PP.BehavInt2_GFPRB 0.0698 0.0000 0.0000 0.3322
## PP.BehavInt3_GFPRB 0.0233 0.0000 0.0000 0.4639
## PP.BehavInt4_GFPRB 0.0184 0.0000 0.0001 0.6058
## PP.BehavInt1_CBB 0.0002 0.0000 0.0536 0.6444
## PP.BehavInt2_CBB 0.0003 0.0000 0.0869 0.5397
## PP.BehavInt3_CBB 0.0002 0.0000 0.0743 0.5727
## PP.BehavInt4_CBB 0.0003 0.0000 0.0569 0.6339
## PP.BehavInt1_PBPB 0.0396 0.0000 0.0000 0.3069
## PP.BehavInt2_PBPB 0.0698 0.0000 0.0000 0.3322
## PP.BehavInt3_PBPB 0.0233 0.0000 0.0000 0.4639
## PP.BehavInt4_PBPB 0.0184 0.0000 0.0001 0.6058
## PP.BehavInt1_PBFB 0.0129 0.0000 0.0002 0.5451
## PP.BehavInt2_PBFB 0.0284 0.0000 0.0003 0.4922
## PP.BehavInt3_PBFB 0.0064 0.0000 0.0005 0.6802
## PP.BehavInt4_PBFB 0.0111 0.0000 0.0003 0.5125
## PP.BehavInt1_VB 0.0166 0.0000 0.0005 0.8003
## PP.BehavInt2_VB 0.2263 0.0000 0.0004 0.7411
## PP.BehavInt3_VB 0.0211 0.0000 0.0009 0.8488
## PP.BehavInt4_VB 0.0286 0.0000 0.0008 0.8307
## PP.CCB_48 0.0000 0.0023 0.5318 0.0141
## PP.CCB_49 0.0000 0.0013 0.6659 0.0085
## PP.CCB_50 0.0000 0.0004 0.6150 0.0067
## PP.CCB_51 0.0000 0.0000 0.4664 0.0076
## PP.CNS_1 0.0000 0.0000 0.8426 0.0007
## PP.CNS_2 0.0000 0.0063 0.2630 0.0000
## PP.CNS_3 0.0000 0.0020 0.5629 0.0003
## PP.ATNS_1 0.0009 0.5788 0.0000 0.0000
## PP.ATNS_2R 0.0010 0.0000 0.9186 0.0673
## PP.ATNS_3 0.0028 0.1152 0.0145 0.0000
## PP.ATNS_4 0.0002 0.0304 0.0696 0.0000
## PP.ATNS_5 0.0007 0.7314 0.0003 0.0000
## PP.Ind_3 0.0051 0.0044 0.1558 0.0002
## PP.Ind_4 0.0000 0.7513 0.0101 0.0000
## PP.Ind_7 0.0005 0.0013 0.1838 0.0001
## PP.Ind_8 0.0000 0.3163 0.0405 0.0000
## PP.Ind_1 0.0000 0.8717 0.0182 0.0000
## PP.Ind_2 0.0002 0.9220 0.0178 0.0000
## PP.Ind_5 0.0000 0.2275 0.0808 0.0000
## PP.Ind_6 0.0007 0.8578 0.0137 0.0000
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Nat_1_GFFB 0.0000 0.4088 0.0000 0.0000
## PP.Nat_4R_GFFB 0.5294 0.0000 0.0209 0.0009
## PP.Nat_2R_GFFB 0.0953 0.0000 0.0985 0.0054
## PP.Nat_3R_GFFB 0.0130 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0000 0.4852 0.5047 0.9601
## PP.Nat_4R_GFPRB 0.4908 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.8756 0.0000 0.0006 0.0000
## PP.Nat_3R_GFPRB 0.3336 0.0000 0.0001 0.0000
## PP.Nat_1_CBB 0.3766 0.0020 0.0000 0.0000
## PP.Nat_4R_CBB 0.0000 0.0426 0.4759 0.6403
## PP.Nat_2R_CBB 0.0000 0.0006 0.5536 0.7751
## PP.Nat_3R_CBB 0.0000 0.0000 0.6005 0.8863
## PP.Nat_1_PBPB 0.8514 0.0000 0.9911 0.0246
## PP.Nat_4R_PBPB 0.0000 0.0414 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.5126 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0349 0.0000 0.0000
## PP.Nat_1_PBFB 0.8390 0.0000 0.1053 0.0010
## PP.Nat_4R_PBFB 0.0000 0.0008 0.0000 0.0040
## PP.Nat_2R_PBFB 0.0000 0.7995 0.0000 0.0000
## PP.Nat_3R_PBFB 0.2946 0.0001 0.0002
## PP.Nat_1_VB 0.2946 0.0385 0.8168
## PP.Nat_4R_VB 0.0001 0.0385 0.0000
## PP.Nat_2R_VB 0.0002 0.8168 0.0000
## PP.Nat_3R_VB 0.0000 0.3023 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0000 0.6640 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.4795 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0001 0.7812 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.4956 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.7821 0.0000 0.2762 0.4225
## PP.BehavInt2_GFPRB 0.9536 0.0000 0.5107 0.2231
## PP.BehavInt3_GFPRB 0.5689 0.0000 0.5037 0.1925
## PP.BehavInt4_GFPRB 0.4068 0.0000 0.4147 0.2634
## PP.BehavInt1_CBB 0.3463 0.0001 0.0000 0.0000
## PP.BehavInt2_CBB 0.3348 0.0003 0.0000 0.0000
## PP.BehavInt3_CBB 0.3247 0.0002 0.0000 0.0000
## PP.BehavInt4_CBB 0.3518 0.0003 0.0000 0.0000
## PP.BehavInt1_PBPB 0.7821 0.0000 0.2762 0.4225
## PP.BehavInt2_PBPB 0.9536 0.0000 0.5107 0.2231
## PP.BehavInt3_PBPB 0.5689 0.0000 0.5037 0.1925
## PP.BehavInt4_PBPB 0.4068 0.0000 0.4147 0.2634
## PP.BehavInt1_PBFB 0.5976 0.0000 0.5388 0.0248
## PP.BehavInt2_PBFB 0.7570 0.0000 0.3384 0.0077
## PP.BehavInt3_PBFB 0.4411 0.0000 0.4616 0.0173
## PP.BehavInt4_PBFB 0.5755 0.0000 0.4858 0.0196
## PP.BehavInt1_VB 0.1772 0.0000 0.0872 0.8096
## PP.BehavInt2_VB 0.5317 0.0000 0.1168 0.5824
## PP.BehavInt3_VB 0.1873 0.0000 0.0572 0.9854
## PP.BehavInt4_VB 0.2192 0.0000 0.0900 0.7806
## PP.CCB_48 0.0001 0.0000 0.5742 0.9960
## PP.CCB_49 0.0000 0.0000 0.8025 0.8942
## PP.CCB_50 0.0000 0.0000 0.9716 0.4797
## PP.CCB_51 0.0000 0.0000 0.4994 0.1518
## PP.CNS_1 0.0010 0.0000 0.0034 0.0002
## PP.CNS_2 0.0000 0.0000 0.0735 0.0292
## PP.CNS_3 0.0000 0.0000 0.0443 0.0135
## PP.ATNS_1 0.0000 0.1299 0.0001 0.0001
## PP.ATNS_2R 0.1059 0.0010 0.0000 0.0000
## PP.ATNS_3 0.0000 0.0087 0.0075 0.0021
## PP.ATNS_4 0.0000 0.0001 0.0792 0.0179
## PP.ATNS_5 0.0000 0.0641 0.0124 0.0153
## PP.Ind_3 0.0060 0.0158 0.0003 0.0000
## PP.Ind_4 0.0000 0.0674 0.0213 0.0020
## PP.Ind_7 0.0018 0.0017 0.0006 0.0000
## PP.Ind_8 0.0000 0.0091 0.0312 0.0041
## PP.Ind_1 0.0000 0.0215 0.1741 0.1514
## PP.Ind_2 0.0000 0.0432 0.0788 0.1117
## PP.Ind_5 0.0000 0.0005 0.1253 0.0341
## PP.Ind_6 0.0000 0.1109 0.0343 0.0235
## PP.Nat_3R_VB PP.BehavInt1_GFFB PP.BehavInt2_GFFB
## PP.Nat_1_GFFB 0.0000 0.0000 0.0000
## PP.Nat_4R_GFFB 0.0007 0.6958 0.7999
## PP.Nat_2R_GFFB 0.0007 0.9854 0.6078
## PP.Nat_3R_GFFB 0.0000 0.0002 0.0039
## PP.Nat_1_GFPRB 0.7210 0.0017 0.0002
## PP.Nat_4R_GFPRB 0.0000 0.0142 0.1079
## PP.Nat_2R_GFPRB 0.0000 0.0546 0.2747
## PP.Nat_3R_GFPRB 0.0000 0.0062 0.0424
## PP.Nat_1_CBB 0.0000 0.0000 0.0005
## PP.Nat_4R_CBB 0.5774 0.0097 0.0032
## PP.Nat_2R_CBB 0.8438 0.1949 0.0957
## PP.Nat_3R_CBB 0.7252 0.1370 0.0553
## PP.Nat_1_PBPB 0.0013 0.7074 0.8096
## PP.Nat_4R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_1_PBFB 0.0000 0.2098 0.5594
## PP.Nat_4R_PBFB 0.0148 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_1_VB 0.3023 0.6640 0.4795
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0752 0.4678 0.2823
## PP.BehavInt2_GFPRB 0.0350 0.5077 0.2574
## PP.BehavInt3_GFPRB 0.0159 0.6042 0.3434
## PP.BehavInt4_GFPRB 0.0293 0.6208 0.3708
## PP.BehavInt1_CBB 0.0000 0.0010 0.0081
## PP.BehavInt2_CBB 0.0000 0.0002 0.0022
## PP.BehavInt3_CBB 0.0000 0.0006 0.0053
## PP.BehavInt4_CBB 0.0000 0.0006 0.0063
## PP.BehavInt1_PBPB 0.0752 0.4678 0.2823
## PP.BehavInt2_PBPB 0.0350 0.5077 0.2574
## PP.BehavInt3_PBPB 0.0159 0.6042 0.3434
## PP.BehavInt4_PBPB 0.0293 0.6208 0.3708
## PP.BehavInt1_PBFB 0.0032 0.7896 0.7934
## PP.BehavInt2_PBFB 0.0010 0.4489 0.8755
## PP.BehavInt3_PBFB 0.0014 0.5183 0.9012
## PP.BehavInt4_PBFB 0.0018 0.4849 0.8689
## PP.BehavInt1_VB 0.1363 0.4421 0.3103
## PP.BehavInt2_VB 0.1249 0.3915 0.2180
## PP.BehavInt3_VB 0.2100 0.4268 0.3195
## PP.BehavInt4_VB 0.1412 0.3789 0.2492
## PP.CCB_48 0.1082 0.2430 0.2231
## PP.CCB_49 0.0720 0.4182 0.4077
## PP.CCB_50 0.0154 0.3527 0.3066
## PP.CCB_51 0.0015 0.9205 0.9530
## PP.CNS_1 0.0000 0.0397 0.0706
## PP.CNS_2 0.0007 0.0456 0.0642
## PP.CNS_3 0.0002 0.0967 0.1444
## PP.ATNS_1 0.0001 0.0002 0.0003
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.0007 0.0290 0.0420
## PP.ATNS_4 0.0014 0.1226 0.1516
## PP.ATNS_5 0.0032 0.0344 0.0306
## PP.Ind_3 0.0000 0.0002 0.0009
## PP.Ind_4 0.0003 0.0007 0.0011
## PP.Ind_7 0.0000 0.0000 0.0000
## PP.Ind_8 0.0007 0.0000 0.0000
## PP.Ind_1 0.0344 0.0594 0.0389
## PP.Ind_2 0.0615 0.0572 0.0365
## PP.Ind_5 0.0039 0.0236 0.0217
## PP.Ind_6 0.0132 0.0090 0.0079
## PP.BehavInt3_GFFB PP.BehavInt4_GFFB PP.BehavInt1_GFPRB
## PP.Nat_1_GFFB 0.0000 0.0000 0.4558
## PP.Nat_4R_GFFB 0.4493 0.8577 0.0000
## PP.Nat_2R_GFFB 0.7700 0.8744 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0007 0.0000
## PP.Nat_1_GFPRB 0.0053 0.0017 0.1092
## PP.Nat_4R_GFPRB 0.0053 0.0204 0.0000
## PP.Nat_2R_GFPRB 0.0229 0.0740 0.0000
## PP.Nat_3R_GFPRB 0.0023 0.0181 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0145 0.0050 0.5091
## PP.Nat_2R_CBB 0.2588 0.1675 0.3064
## PP.Nat_3R_CBB 0.1818 0.1272 0.0378
## PP.Nat_1_PBPB 0.4798 0.9063 0.0000
## PP.Nat_4R_PBPB 0.0000 0.0000 0.0068
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0788
## PP.Nat_3R_PBPB 0.0000 0.0000 0.0396
## PP.Nat_1_PBFB 0.1174 0.3261 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.0000 0.3069
## PP.Nat_3R_PBFB 0.0001 0.0000 0.7821
## PP.Nat_1_VB 0.7812 0.4956 0.0000
## PP.Nat_4R_VB 0.0000 0.0000 0.2762
## PP.Nat_2R_VB 0.0000 0.0000 0.4225
## PP.Nat_3R_VB 0.0000 0.0000 0.0752
## PP.BehavInt1_GFFB 0.0000 0.0000 0.4678
## PP.BehavInt2_GFFB 0.0000 0.0000 0.2823
## PP.BehavInt3_GFFB 0.0000 0.6567
## PP.BehavInt4_GFFB 0.0000 0.2929
## PP.BehavInt1_GFPRB 0.6567 0.2929
## PP.BehavInt2_GFPRB 0.7465 0.3110 0.0000
## PP.BehavInt3_GFPRB 0.8299 0.3991 0.0000
## PP.BehavInt4_GFPRB 0.8350 0.4001 0.0000
## PP.BehavInt1_CBB 0.0003 0.0033 0.0000
## PP.BehavInt2_CBB 0.0000 0.0006 0.0000
## PP.BehavInt3_CBB 0.0001 0.0019 0.0000
## PP.BehavInt4_CBB 0.0002 0.0020 0.0000
## PP.BehavInt1_PBPB 0.6567 0.2929 0.0000
## PP.BehavInt2_PBPB 0.7465 0.3110 0.0000
## PP.BehavInt3_PBPB 0.8299 0.3991 0.0000
## PP.BehavInt4_PBPB 0.8350 0.4001 0.0000
## PP.BehavInt1_PBFB 0.5847 0.9681 0.0000
## PP.BehavInt2_PBFB 0.2873 0.6779 0.0000
## PP.BehavInt3_PBFB 0.3584 0.7547 0.0000
## PP.BehavInt4_PBFB 0.3251 0.7337 0.0000
## PP.BehavInt1_VB 0.5511 0.3057 0.0000
## PP.BehavInt2_VB 0.5461 0.2702 0.0000
## PP.BehavInt3_VB 0.5303 0.2965 0.0000
## PP.BehavInt4_VB 0.4883 0.2498 0.0000
## PP.CCB_48 0.2245 0.3019 0.0002
## PP.CCB_49 0.3818 0.4759 0.0002
## PP.CCB_50 0.3601 0.4214 0.0000
## PP.CCB_51 0.8813 0.8424 0.0000
## PP.CNS_1 0.0285 0.0248 0.0083
## PP.CNS_2 0.0501 0.0222 0.0541
## PP.CNS_3 0.1058 0.0558 0.0517
## PP.ATNS_1 0.0002 0.0000 0.3720
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.0223 0.0139 0.7787
## PP.ATNS_4 0.1028 0.0654 0.1281
## PP.ATNS_5 0.0347 0.0136 0.5459
## PP.Ind_3 0.0001 0.0002 0.1219
## PP.Ind_4 0.0019 0.0004 0.7716
## PP.Ind_7 0.0000 0.0000 0.0734
## PP.Ind_8 0.0002 0.0000 0.5186
## PP.Ind_1 0.1150 0.0403 0.7170
## PP.Ind_2 0.1088 0.0488 0.5226
## PP.Ind_5 0.0405 0.0194 0.3201
## PP.Ind_6 0.0164 0.0060 0.4200
## PP.BehavInt2_GFPRB PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB
## PP.Nat_1_GFFB 0.4866 0.5389 0.5219
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0207 0.0642 0.0973
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.3501 0.5284 0.8186
## PP.Nat_2R_CBB 0.5338 0.3225 0.1499
## PP.Nat_3R_CBB 0.1173 0.0549 0.0186
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0110 0.0154 0.0193
## PP.Nat_2R_PBPB 0.0945 0.1231 0.1761
## PP.Nat_3R_PBPB 0.0698 0.0233 0.0184
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0001
## PP.Nat_2R_PBFB 0.3322 0.4639 0.6058
## PP.Nat_3R_PBFB 0.9536 0.5689 0.4068
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.5107 0.5037 0.4147
## PP.Nat_2R_VB 0.2231 0.1925 0.2634
## PP.Nat_3R_VB 0.0350 0.0159 0.0293
## PP.BehavInt1_GFFB 0.5077 0.6042 0.6208
## PP.BehavInt2_GFFB 0.2574 0.3434 0.3708
## PP.BehavInt3_GFFB 0.7465 0.8299 0.8350
## PP.BehavInt4_GFFB 0.3110 0.3991 0.4001
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.0007 0.0000 0.0000
## PP.CCB_49 0.0007 0.0000 0.0000
## PP.CCB_50 0.0001 0.0000 0.0000
## PP.CCB_51 0.0000 0.0000 0.0000
## PP.CNS_1 0.0065 0.0047 0.0040
## PP.CNS_2 0.0748 0.0448 0.0337
## PP.CNS_3 0.0521 0.0277 0.0232
## PP.ATNS_1 0.4942 0.5269 0.6322
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.6279 0.6312 0.5006
## PP.ATNS_4 0.1261 0.0895 0.0640
## PP.ATNS_5 0.5960 0.7192 0.8689
## PP.Ind_3 0.0712 0.0710 0.0692
## PP.Ind_4 0.6162 0.8857 0.9744
## PP.Ind_7 0.0612 0.0419 0.0382
## PP.Ind_8 0.7217 0.4904 0.3679
## PP.Ind_1 0.4727 0.7401 0.8765
## PP.Ind_2 0.3806 0.5288 0.6327
## PP.Ind_5 0.4729 0.2779 0.2253
## PP.Ind_6 0.3575 0.4722 0.5916
## PP.BehavInt1_CBB PP.BehavInt2_CBB PP.BehavInt3_CBB
## PP.Nat_1_GFFB 0.0011 0.0002 0.0006
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0449 0.0486 0.0381
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0568 0.0637 0.0691
## PP.Nat_2R_CBB 0.2309 0.1704 0.2089
## PP.Nat_3R_CBB 0.9713 0.8531 0.9601
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.2162 0.1551 0.1858
## PP.Nat_2R_PBPB 0.1080 0.0624 0.0979
## PP.Nat_3R_PBPB 0.0002 0.0003 0.0002
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0536 0.0869 0.0743
## PP.Nat_2R_PBFB 0.6444 0.5397 0.5727
## PP.Nat_3R_PBFB 0.3463 0.3348 0.3247
## PP.Nat_1_VB 0.0001 0.0003 0.0002
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0010 0.0002 0.0006
## PP.BehavInt2_GFFB 0.0081 0.0022 0.0053
## PP.BehavInt3_GFFB 0.0003 0.0000 0.0001
## PP.BehavInt4_GFFB 0.0033 0.0006 0.0019
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0001 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.1039 0.1959 0.1100
## PP.CCB_49 0.0636 0.1247 0.0677
## PP.CCB_50 0.0254 0.0543 0.0268
## PP.CCB_51 0.0002 0.0006 0.0002
## PP.CNS_1 0.0002 0.0001 0.0002
## PP.CNS_2 0.0363 0.0414 0.0432
## PP.CNS_3 0.0175 0.0178 0.0205
## PP.ATNS_1 0.5605 0.3939 0.5134
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.5279 0.4236 0.4574
## PP.ATNS_4 0.2970 0.3082 0.2699
## PP.ATNS_5 0.6884 0.7899 0.7890
## PP.Ind_3 0.0025 0.0008 0.0016
## PP.Ind_4 0.8824 0.7066 0.8514
## PP.Ind_7 0.0028 0.0013 0.0015
## PP.Ind_8 0.3255 0.2416 0.3309
## PP.Ind_1 0.7604 0.7346 0.7288
## PP.Ind_2 0.7981 0.8079 0.7858
## PP.Ind_5 0.2901 0.3138 0.2844
## PP.Ind_6 0.8666 0.9528 0.8522
## PP.BehavInt4_CBB PP.BehavInt1_PBPB PP.BehavInt2_PBPB
## PP.Nat_1_GFFB 0.0006 0.4558 0.4866
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0447 0.1092 0.0207
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0489 0.5091 0.3501
## PP.Nat_2R_CBB 0.1902 0.3064 0.5338
## PP.Nat_3R_CBB 0.9044 0.0378 0.1173
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.2256 0.0068 0.0110
## PP.Nat_2R_PBPB 0.1088 0.0788 0.0945
## PP.Nat_3R_PBPB 0.0003 0.0396 0.0698
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0569 0.0000 0.0000
## PP.Nat_2R_PBFB 0.6339 0.3069 0.3322
## PP.Nat_3R_PBFB 0.3518 0.7821 0.9536
## PP.Nat_1_VB 0.0003 0.0000 0.0000
## PP.Nat_4R_VB 0.0000 0.2762 0.5107
## PP.Nat_2R_VB 0.0000 0.4225 0.2231
## PP.Nat_3R_VB 0.0000 0.0752 0.0350
## PP.BehavInt1_GFFB 0.0006 0.4678 0.5077
## PP.BehavInt2_GFFB 0.0063 0.2823 0.2574
## PP.BehavInt3_GFFB 0.0002 0.6567 0.7465
## PP.BehavInt4_GFFB 0.0020 0.2929 0.3110
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.1212 0.0002 0.0007
## PP.CCB_49 0.0815 0.0002 0.0007
## PP.CCB_50 0.0303 0.0000 0.0001
## PP.CCB_51 0.0003 0.0000 0.0000
## PP.CNS_1 0.0002 0.0083 0.0065
## PP.CNS_2 0.0451 0.0541 0.0748
## PP.CNS_3 0.0203 0.0517 0.0521
## PP.ATNS_1 0.5740 0.3720 0.4942
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.5258 0.7787 0.6279
## PP.ATNS_4 0.3061 0.1281 0.1261
## PP.ATNS_5 0.7145 0.5459 0.5960
## PP.Ind_3 0.0021 0.1219 0.0712
## PP.Ind_4 0.8645 0.7716 0.6162
## PP.Ind_7 0.0025 0.0734 0.0612
## PP.Ind_8 0.3651 0.5186 0.7217
## PP.Ind_1 0.7020 0.7170 0.4727
## PP.Ind_2 0.7596 0.5226 0.3806
## PP.Ind_5 0.3060 0.3201 0.4729
## PP.Ind_6 0.8506 0.4200 0.3575
## PP.BehavInt3_PBPB PP.BehavInt4_PBPB PP.BehavInt1_PBFB
## PP.Nat_1_GFFB 0.5389 0.5219 0.9221
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0642 0.0973 0.0107
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.5284 0.8186 0.4389
## PP.Nat_2R_CBB 0.3225 0.1499 0.6407
## PP.Nat_3R_CBB 0.0549 0.0186 0.1434
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0154 0.0193 0.1972
## PP.Nat_2R_PBPB 0.1231 0.1761 0.5137
## PP.Nat_3R_PBPB 0.0233 0.0184 0.0129
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0001 0.0002
## PP.Nat_2R_PBFB 0.4639 0.6058 0.5451
## PP.Nat_3R_PBFB 0.5689 0.4068 0.5976
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.5037 0.4147 0.5388
## PP.Nat_2R_VB 0.1925 0.2634 0.0248
## PP.Nat_3R_VB 0.0159 0.0293 0.0032
## PP.BehavInt1_GFFB 0.6042 0.6208 0.7896
## PP.BehavInt2_GFFB 0.3434 0.3708 0.7934
## PP.BehavInt3_GFFB 0.8299 0.8350 0.5847
## PP.BehavInt4_GFFB 0.3991 0.4001 0.9681
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.0000 0.0000 0.0001
## PP.CCB_49 0.0000 0.0000 0.0000
## PP.CCB_50 0.0000 0.0000 0.0000
## PP.CCB_51 0.0000 0.0000 0.0000
## PP.CNS_1 0.0047 0.0040 0.0004
## PP.CNS_2 0.0448 0.0337 0.0123
## PP.CNS_3 0.0277 0.0232 0.0050
## PP.ATNS_1 0.5269 0.6322 0.9126
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.6312 0.5006 0.3613
## PP.ATNS_4 0.0895 0.0640 0.0564
## PP.ATNS_5 0.7192 0.8689 0.9410
## PP.Ind_3 0.0710 0.0692 0.0376
## PP.Ind_4 0.8857 0.9744 0.9785
## PP.Ind_7 0.0419 0.0382 0.0099
## PP.Ind_8 0.4904 0.3679 0.4878
## PP.Ind_1 0.7401 0.8765 0.8368
## PP.Ind_2 0.5288 0.6327 0.7076
## PP.Ind_5 0.2779 0.2253 0.2813
## PP.Ind_6 0.4722 0.5916 0.6013
## PP.BehavInt2_PBFB PP.BehavInt3_PBFB PP.BehavInt4_PBFB
## PP.Nat_1_GFFB 0.4798 0.6266 0.5611
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0084 0.0267 0.0291
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.2491 0.5382 0.3441
## PP.Nat_2R_CBB 0.9360 0.5527 0.8124
## PP.Nat_3R_CBB 0.4022 0.1239 0.2259
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.2147 0.3248 0.2377
## PP.Nat_2R_PBPB 0.5560 0.7166 0.5291
## PP.Nat_3R_PBPB 0.0284 0.0064 0.0111
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0003 0.0005 0.0003
## PP.Nat_2R_PBFB 0.4922 0.6802 0.5125
## PP.Nat_3R_PBFB 0.7570 0.4411 0.5755
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.3384 0.4616 0.4858
## PP.Nat_2R_VB 0.0077 0.0173 0.0196
## PP.Nat_3R_VB 0.0010 0.0014 0.0018
## PP.BehavInt1_GFFB 0.4489 0.5183 0.4849
## PP.BehavInt2_GFFB 0.8755 0.9012 0.8689
## PP.BehavInt3_GFFB 0.2873 0.3584 0.3251
## PP.BehavInt4_GFFB 0.6779 0.7547 0.7337
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.0021 0.0002 0.0007
## PP.CCB_49 0.0017 0.0001 0.0005
## PP.CCB_50 0.0004 0.0000 0.0001
## PP.CCB_51 0.0000 0.0000 0.0000
## PP.CNS_1 0.0014 0.0003 0.0011
## PP.CNS_2 0.0431 0.0099 0.0264
## PP.CNS_3 0.0188 0.0041 0.0132
## PP.ATNS_1 0.8475 0.9246 0.8386
## PP.ATNS_2R 0.0000 0.0000 0.0000
## PP.ATNS_3 0.4259 0.2782 0.4693
## PP.ATNS_4 0.1141 0.0412 0.0931
## PP.ATNS_5 0.7678 0.9393 0.7664
## PP.Ind_3 0.0167 0.0313 0.0382
## PP.Ind_4 0.8681 0.8692 0.9129
## PP.Ind_7 0.0056 0.0059 0.0107
## PP.Ind_8 0.5845 0.4130 0.5571
## PP.Ind_1 0.4682 0.8318 0.6013
## PP.Ind_2 0.4219 0.7483 0.5272
## PP.Ind_5 0.5113 0.2552 0.3938
## PP.Ind_6 0.4090 0.6262 0.4196
## PP.BehavInt1_VB PP.BehavInt2_VB PP.BehavInt3_VB
## PP.Nat_1_GFFB 0.3418 0.2906 0.3024
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.4447 0.1199 0.6061
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0003
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0012 0.0003 0.0027
## PP.Nat_4R_CBB 0.1152 0.2370 0.0723
## PP.Nat_2R_CBB 0.0010 0.0097 0.0003
## PP.Nat_3R_CBB 0.0000 0.0015 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0222 0.0043 0.0239
## PP.Nat_2R_PBPB 0.3927 0.1284 0.3608
## PP.Nat_3R_PBPB 0.0166 0.2263 0.0211
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0005 0.0004 0.0009
## PP.Nat_2R_PBFB 0.8003 0.7411 0.8488
## PP.Nat_3R_PBFB 0.1772 0.5317 0.1873
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.0872 0.1168 0.0572
## PP.Nat_2R_VB 0.8096 0.5824 0.9854
## PP.Nat_3R_VB 0.1363 0.1249 0.2100
## PP.BehavInt1_GFFB 0.4421 0.3915 0.4268
## PP.BehavInt2_GFFB 0.3103 0.2180 0.3195
## PP.BehavInt3_GFFB 0.5511 0.5461 0.5303
## PP.BehavInt4_GFFB 0.3057 0.2702 0.2965
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0001
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.CCB_48 0.0000 0.0000 0.0000
## PP.CCB_49 0.0000 0.0000 0.0000
## PP.CCB_50 0.0000 0.0000 0.0000
## PP.CCB_51 0.0000 0.0000 0.0000
## PP.CNS_1 0.0000 0.0001 0.0000
## PP.CNS_2 0.0002 0.0017 0.0001
## PP.CNS_3 0.0000 0.0005 0.0000
## PP.ATNS_1 0.5499 0.5164 0.5079
## PP.ATNS_2R 0.0002 0.0000 0.0005
## PP.ATNS_3 0.0455 0.0520 0.0419
## PP.ATNS_4 0.0012 0.0025 0.0008
## PP.ATNS_5 0.2041 0.2513 0.1838
## PP.Ind_3 0.0110 0.0058 0.0203
## PP.Ind_4 0.0834 0.1760 0.0923
## PP.Ind_7 0.0010 0.0018 0.0020
## PP.Ind_8 0.0223 0.0588 0.0274
## PP.Ind_1 0.0871 0.3395 0.0835
## PP.Ind_2 0.1976 0.5038 0.1901
## PP.Ind_5 0.0030 0.0284 0.0032
## PP.Ind_6 0.2923 0.5868 0.3084
## PP.BehavInt4_VB PP.CCB_48 PP.CCB_49 PP.CCB_50 PP.CCB_51
## PP.Nat_1_GFFB 0.2989 0.1575 0.2762 0.3391 0.9993
## PP.Nat_4R_GFFB 0.0000 0.0008 0.0005 0.0002 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0351 0.0243 0.0095 0.0003
## PP.Nat_1_GFPRB 0.4630 0.7977 0.6970 0.9745 0.9558
## PP.Nat_4R_GFPRB 0.0000 0.0660 0.0458 0.0159 0.0009
## PP.Nat_2R_GFPRB 0.0000 0.0329 0.0308 0.0085 0.0010
## PP.Nat_3R_GFPRB 0.0000 0.0416 0.0269 0.0107 0.0004
## PP.Nat_1_CBB 0.0016 0.4049 0.2777 0.1500 0.0051
## PP.Nat_4R_CBB 0.1051 0.0012 0.0009 0.0033 0.0054
## PP.Nat_2R_CBB 0.0011 0.0000 0.0000 0.0000 0.0000
## PP.Nat_3R_CBB 0.0001 0.0000 0.0000 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.0013 0.0014 0.0002 0.0000
## PP.Nat_4R_PBPB 0.0188 0.9701 0.7033 0.8303 0.5316
## PP.Nat_2R_PBPB 0.2822 0.3137 0.1682 0.2353 0.1070
## PP.Nat_3R_PBPB 0.0286 0.0000 0.0000 0.0000 0.0000
## PP.Nat_1_PBFB 0.0000 0.0023 0.0013 0.0004 0.0000
## PP.Nat_4R_PBFB 0.0008 0.5318 0.6659 0.6150 0.4664
## PP.Nat_2R_PBFB 0.8307 0.0141 0.0085 0.0067 0.0076
## PP.Nat_3R_PBFB 0.2192 0.0001 0.0000 0.0000 0.0000
## PP.Nat_1_VB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.0900 0.5742 0.8025 0.9716 0.4994
## PP.Nat_2R_VB 0.7806 0.9960 0.8942 0.4797 0.1518
## PP.Nat_3R_VB 0.1412 0.1082 0.0720 0.0154 0.0015
## PP.BehavInt1_GFFB 0.3789 0.2430 0.4182 0.3527 0.9205
## PP.BehavInt2_GFFB 0.2492 0.2231 0.4077 0.3066 0.9530
## PP.BehavInt3_GFFB 0.4883 0.2245 0.3818 0.3601 0.8813
## PP.BehavInt4_GFFB 0.2498 0.3019 0.4759 0.4214 0.8424
## PP.BehavInt1_GFPRB 0.0000 0.0002 0.0002 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0007 0.0007 0.0001 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.1039 0.0636 0.0254 0.0002
## PP.BehavInt2_CBB 0.0000 0.1959 0.1247 0.0543 0.0006
## PP.BehavInt3_CBB 0.0000 0.1100 0.0677 0.0268 0.0002
## PP.BehavInt4_CBB 0.0000 0.1212 0.0815 0.0303 0.0003
## PP.BehavInt1_PBPB 0.0000 0.0002 0.0002 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0007 0.0007 0.0001 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0001 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0021 0.0017 0.0004 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0002 0.0001 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0007 0.0005 0.0001 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000 0.0000
## PP.CCB_48 0.0000 0.0000 0.0000 0.0000
## PP.CCB_49 0.0000 0.0000 0.0000 0.0000
## PP.CCB_50 0.0000 0.0000 0.0000 0.0000
## PP.CCB_51 0.0000 0.0000 0.0000 0.0000
## PP.CNS_1 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.CNS_2 0.0003 0.0000 0.0000 0.0000 0.0000
## PP.CNS_3 0.0001 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_1 0.5795 0.0375 0.0208 0.0242 0.0258
## PP.ATNS_2R 0.0003 0.3555 0.2537 0.1264 0.0070
## PP.ATNS_3 0.0543 0.0014 0.0005 0.0005 0.0008
## PP.ATNS_4 0.0014 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_5 0.2390 0.0005 0.0002 0.0003 0.0008
## PP.Ind_3 0.0109 0.1913 0.1541 0.0847 0.0343
## PP.Ind_4 0.0950 0.0036 0.0029 0.0032 0.0052
## PP.Ind_7 0.0016 0.0152 0.0102 0.0071 0.0021
## PP.Ind_8 0.0319 0.0030 0.0020 0.0026 0.0020
## PP.Ind_1 0.1202 0.0000 0.0000 0.0000 0.0000
## PP.Ind_2 0.2504 0.0000 0.0000 0.0001 0.0006
## PP.Ind_5 0.0054 0.0000 0.0000 0.0000 0.0000
## PP.Ind_6 0.3326 0.0006 0.0003 0.0009 0.0021
## PP.CNS_1 PP.CNS_2 PP.CNS_3 PP.ATNS_1 PP.ATNS_2R PP.ATNS_3
## PP.Nat_1_GFFB 0.0452 0.0636 0.1556 0.0004 0.0000 0.0531
## PP.Nat_4R_GFFB 0.0000 0.0025 0.0009 0.0398 0.0000 0.0039
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000 0.0348 0.0000 0.0035
## PP.Nat_3R_GFFB 0.0000 0.0008 0.0012 0.0021 0.0000 0.0015
## PP.Nat_1_GFPRB 0.5228 0.0208 0.2428 0.0316 0.1120 0.2982
## PP.Nat_4R_GFPRB 0.0000 0.0079 0.0010 0.0059 0.0000 0.0004
## PP.Nat_2R_GFPRB 0.0000 0.0132 0.0012 0.0250 0.0000 0.0049
## PP.Nat_3R_GFPRB 0.0000 0.0074 0.0024 0.0772 0.0000 0.0223
## PP.Nat_1_CBB 0.0000 0.0299 0.0133 0.0938 0.0000 0.1135
## PP.Nat_4R_CBB 0.0025 0.0000 0.0000 0.0000 0.7531 0.0000
## PP.Nat_2R_CBB 0.0002 0.0000 0.0000 0.0000 0.6518 0.0000
## PP.Nat_3R_CBB 0.0001 0.0000 0.0000 0.0000 0.7356 0.0000
## PP.Nat_1_PBPB 0.0004 0.0181 0.0121 0.8487 0.0000 0.2018
## PP.Nat_4R_PBPB 0.0141 0.0052 0.0119 0.0000 0.0227 0.0002
## PP.Nat_2R_PBPB 0.0005 0.0001 0.0005 0.0000 0.0068 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0000 0.0000 0.0009 0.0010 0.0028
## PP.Nat_1_PBFB 0.0000 0.0063 0.0020 0.5788 0.0000 0.1152
## PP.Nat_4R_PBFB 0.8426 0.2630 0.5629 0.0000 0.9186 0.0145
## PP.Nat_2R_PBFB 0.0007 0.0000 0.0003 0.0000 0.0673 0.0000
## PP.Nat_3R_PBFB 0.0010 0.0000 0.0000 0.0000 0.1059 0.0000
## PP.Nat_1_VB 0.0000 0.0000 0.0000 0.1299 0.0010 0.0087
## PP.Nat_4R_VB 0.0034 0.0735 0.0443 0.0001 0.0000 0.0075
## PP.Nat_2R_VB 0.0002 0.0292 0.0135 0.0001 0.0000 0.0021
## PP.Nat_3R_VB 0.0000 0.0007 0.0002 0.0001 0.0000 0.0007
## PP.BehavInt1_GFFB 0.0397 0.0456 0.0967 0.0002 0.0000 0.0290
## PP.BehavInt2_GFFB 0.0706 0.0642 0.1444 0.0003 0.0000 0.0420
## PP.BehavInt3_GFFB 0.0285 0.0501 0.1058 0.0002 0.0000 0.0223
## PP.BehavInt4_GFFB 0.0248 0.0222 0.0558 0.0000 0.0000 0.0139
## PP.BehavInt1_GFPRB 0.0083 0.0541 0.0517 0.3720 0.0000 0.7787
## PP.BehavInt2_GFPRB 0.0065 0.0748 0.0521 0.4942 0.0000 0.6279
## PP.BehavInt3_GFPRB 0.0047 0.0448 0.0277 0.5269 0.0000 0.6312
## PP.BehavInt4_GFPRB 0.0040 0.0337 0.0232 0.6322 0.0000 0.5006
## PP.BehavInt1_CBB 0.0002 0.0363 0.0175 0.5605 0.0000 0.5279
## PP.BehavInt2_CBB 0.0001 0.0414 0.0178 0.3939 0.0000 0.4236
## PP.BehavInt3_CBB 0.0002 0.0432 0.0205 0.5134 0.0000 0.4574
## PP.BehavInt4_CBB 0.0002 0.0451 0.0203 0.5740 0.0000 0.5258
## PP.BehavInt1_PBPB 0.0083 0.0541 0.0517 0.3720 0.0000 0.7787
## PP.BehavInt2_PBPB 0.0065 0.0748 0.0521 0.4942 0.0000 0.6279
## PP.BehavInt3_PBPB 0.0047 0.0448 0.0277 0.5269 0.0000 0.6312
## PP.BehavInt4_PBPB 0.0040 0.0337 0.0232 0.6322 0.0000 0.5006
## PP.BehavInt1_PBFB 0.0004 0.0123 0.0050 0.9126 0.0000 0.3613
## PP.BehavInt2_PBFB 0.0014 0.0431 0.0188 0.8475 0.0000 0.4259
## PP.BehavInt3_PBFB 0.0003 0.0099 0.0041 0.9246 0.0000 0.2782
## PP.BehavInt4_PBFB 0.0011 0.0264 0.0132 0.8386 0.0000 0.4693
## PP.BehavInt1_VB 0.0000 0.0002 0.0000 0.5499 0.0002 0.0455
## PP.BehavInt2_VB 0.0001 0.0017 0.0005 0.5164 0.0000 0.0520
## PP.BehavInt3_VB 0.0000 0.0001 0.0000 0.5079 0.0005 0.0419
## PP.BehavInt4_VB 0.0000 0.0003 0.0001 0.5795 0.0003 0.0543
## PP.CCB_48 0.0000 0.0000 0.0000 0.0375 0.3555 0.0014
## PP.CCB_49 0.0000 0.0000 0.0000 0.0208 0.2537 0.0005
## PP.CCB_50 0.0000 0.0000 0.0000 0.0242 0.1264 0.0005
## PP.CCB_51 0.0000 0.0000 0.0000 0.0258 0.0070 0.0008
## PP.CNS_1 0.0000 0.0000 0.0000 0.0007 0.0000
## PP.CNS_2 0.0000 0.0000 0.0000 0.0547 0.0000
## PP.CNS_3 0.0000 0.0000 0.0000 0.0361 0.0000
## PP.ATNS_1 0.0000 0.0000 0.0000 0.0447 0.0000
## PP.ATNS_2R 0.0007 0.0547 0.0361 0.0447 0.1253
## PP.ATNS_3 0.0000 0.0000 0.0000 0.0000 0.1253
## PP.ATNS_4 0.0000 0.0000 0.0000 0.0000 0.1596 0.0000
## PP.ATNS_5 0.0000 0.0000 0.0000 0.0000 0.4737 0.0000
## PP.Ind_3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_4 0.0000 0.0000 0.0000 0.0000 0.2908 0.0000
## PP.Ind_7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_8 0.0000 0.0000 0.0000 0.0000 0.1037 0.0000
## PP.Ind_1 0.0000 0.0000 0.0000 0.0000 0.8249 0.0000
## PP.Ind_2 0.0000 0.0000 0.0000 0.0000 0.8826 0.0000
## PP.Ind_5 0.0000 0.0000 0.0000 0.0000 0.3117 0.0000
## PP.Ind_6 0.0000 0.0000 0.0000 0.0000 0.8007 0.0000
## PP.ATNS_4 PP.ATNS_5 PP.Ind_3 PP.Ind_4 PP.Ind_7 PP.Ind_8
## PP.Nat_1_GFFB 0.2079 0.0595 0.0001 0.0010 0.0002 0.0003
## PP.Nat_4R_GFFB 0.0040 0.0709 0.0002 0.4363 0.0001 0.2254
## PP.Nat_2R_GFFB 0.0007 0.0383 0.0008 0.1926 0.0005 0.0883
## PP.Nat_3R_GFFB 0.0024 0.0317 0.0000 0.0389 0.0000 0.0087
## PP.Nat_1_GFPRB 0.1755 0.0546 0.2126 0.0000 0.1717 0.0000
## PP.Nat_4R_GFPRB 0.0026 0.0261 0.0000 0.1399 0.0000 0.0829
## PP.Nat_2R_GFPRB 0.0118 0.1322 0.0000 0.2646 0.0000 0.1874
## PP.Nat_3R_GFPRB 0.0218 0.3213 0.0000 0.2684 0.0000 0.1217
## PP.Nat_1_CBB 0.1937 0.6879 0.0000 0.3737 0.0001 0.1332
## PP.Nat_4R_CBB 0.0000 0.0000 0.0034 0.0000 0.0002 0.0000
## PP.Nat_2R_CBB 0.0000 0.0000 0.0093 0.0000 0.0007 0.0000
## PP.Nat_3R_CBB 0.0000 0.0000 0.0292 0.0000 0.0034 0.0000
## PP.Nat_1_PBPB 0.0416 0.9729 0.0043 0.7243 0.0029 0.2272
## PP.Nat_4R_PBPB 0.0095 0.0000 0.0054 0.0004 0.0010 0.0006
## PP.Nat_2R_PBPB 0.0005 0.0000 0.0003 0.0000 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0002 0.0007 0.0051 0.0000 0.0005 0.0000
## PP.Nat_1_PBFB 0.0304 0.7314 0.0044 0.7513 0.0013 0.3163
## PP.Nat_4R_PBFB 0.0696 0.0003 0.1558 0.0101 0.1838 0.0405
## PP.Nat_2R_PBFB 0.0000 0.0000 0.0002 0.0000 0.0001 0.0000
## PP.Nat_3R_PBFB 0.0000 0.0000 0.0060 0.0000 0.0018 0.0000
## PP.Nat_1_VB 0.0001 0.0641 0.0158 0.0674 0.0017 0.0091
## PP.Nat_4R_VB 0.0792 0.0124 0.0003 0.0213 0.0006 0.0312
## PP.Nat_2R_VB 0.0179 0.0153 0.0000 0.0020 0.0000 0.0041
## PP.Nat_3R_VB 0.0014 0.0032 0.0000 0.0003 0.0000 0.0007
## PP.BehavInt1_GFFB 0.1226 0.0344 0.0002 0.0007 0.0000 0.0000
## PP.BehavInt2_GFFB 0.1516 0.0306 0.0009 0.0011 0.0000 0.0000
## PP.BehavInt3_GFFB 0.1028 0.0347 0.0001 0.0019 0.0000 0.0002
## PP.BehavInt4_GFFB 0.0654 0.0136 0.0002 0.0004 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.1281 0.5459 0.1219 0.7716 0.0734 0.5186
## PP.BehavInt2_GFPRB 0.1261 0.5960 0.0712 0.6162 0.0612 0.7217
## PP.BehavInt3_GFPRB 0.0895 0.7192 0.0710 0.8857 0.0419 0.4904
## PP.BehavInt4_GFPRB 0.0640 0.8689 0.0692 0.9744 0.0382 0.3679
## PP.BehavInt1_CBB 0.2970 0.6884 0.0025 0.8824 0.0028 0.3255
## PP.BehavInt2_CBB 0.3082 0.7899 0.0008 0.7066 0.0013 0.2416
## PP.BehavInt3_CBB 0.2699 0.7890 0.0016 0.8514 0.0015 0.3309
## PP.BehavInt4_CBB 0.3061 0.7145 0.0021 0.8645 0.0025 0.3651
## PP.BehavInt1_PBPB 0.1281 0.5459 0.1219 0.7716 0.0734 0.5186
## PP.BehavInt2_PBPB 0.1261 0.5960 0.0712 0.6162 0.0612 0.7217
## PP.BehavInt3_PBPB 0.0895 0.7192 0.0710 0.8857 0.0419 0.4904
## PP.BehavInt4_PBPB 0.0640 0.8689 0.0692 0.9744 0.0382 0.3679
## PP.BehavInt1_PBFB 0.0564 0.9410 0.0376 0.9785 0.0099 0.4878
## PP.BehavInt2_PBFB 0.1141 0.7678 0.0167 0.8681 0.0056 0.5845
## PP.BehavInt3_PBFB 0.0412 0.9393 0.0313 0.8692 0.0059 0.4130
## PP.BehavInt4_PBFB 0.0931 0.7664 0.0382 0.9129 0.0107 0.5571
## PP.BehavInt1_VB 0.0012 0.2041 0.0110 0.0834 0.0010 0.0223
## PP.BehavInt2_VB 0.0025 0.2513 0.0058 0.1760 0.0018 0.0588
## PP.BehavInt3_VB 0.0008 0.1838 0.0203 0.0923 0.0020 0.0274
## PP.BehavInt4_VB 0.0014 0.2390 0.0109 0.0950 0.0016 0.0319
## PP.CCB_48 0.0000 0.0005 0.1913 0.0036 0.0152 0.0030
## PP.CCB_49 0.0000 0.0002 0.1541 0.0029 0.0102 0.0020
## PP.CCB_50 0.0000 0.0003 0.0847 0.0032 0.0071 0.0026
## PP.CCB_51 0.0000 0.0008 0.0343 0.0052 0.0021 0.0020
## PP.CNS_1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.CNS_2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.CNS_3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_2R 0.1596 0.4737 0.0000 0.2908 0.0000 0.1037
## PP.ATNS_3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_4 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_5 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_3 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_4 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_7 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_8 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_1 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
## PP.Ind_2 0.0000 0.0000 0.0004 0.0000 0.0000 0.0000
## PP.Ind_5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## PP.Ind_1 PP.Ind_2 PP.Ind_5 PP.Ind_6
## PP.Nat_1_GFFB 0.1021 0.0852 0.0512 0.0261
## PP.Nat_4R_GFFB 0.6618 0.5195 0.0719 0.4567
## PP.Nat_2R_GFFB 0.1830 0.1549 0.0051 0.2028
## PP.Nat_3R_GFFB 0.2610 0.2347 0.0122 0.1224
## PP.Nat_1_GFPRB 0.0000 0.0003 0.0009 0.0001
## PP.Nat_4R_GFPRB 0.6318 0.3962 0.0967 0.2829
## PP.Nat_2R_GFPRB 0.7991 0.5704 0.1752 0.4690
## PP.Nat_3R_GFPRB 0.6965 0.5355 0.0752 0.5155
## PP.Nat_1_CBB 0.8438 0.9307 0.2848 0.6812
## PP.Nat_4R_CBB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_2R_CBB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_3R_CBB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_1_PBPB 0.8164 0.7555 0.1826 0.8031
## PP.Nat_4R_PBPB 0.0016 0.0002 0.0023 0.0005
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0002 0.0000 0.0007
## PP.Nat_1_PBFB 0.8717 0.9220 0.2275 0.8578
## PP.Nat_4R_PBFB 0.0182 0.0178 0.0808 0.0137
## PP.Nat_2R_PBFB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.0000 0.0000 0.0000
## PP.Nat_1_VB 0.0215 0.0432 0.0005 0.1109
## PP.Nat_4R_VB 0.1741 0.0788 0.1253 0.0343
## PP.Nat_2R_VB 0.1514 0.1117 0.0341 0.0235
## PP.Nat_3R_VB 0.0344 0.0615 0.0039 0.0132
## PP.BehavInt1_GFFB 0.0594 0.0572 0.0236 0.0090
## PP.BehavInt2_GFFB 0.0389 0.0365 0.0217 0.0079
## PP.BehavInt3_GFFB 0.1150 0.1088 0.0405 0.0164
## PP.BehavInt4_GFFB 0.0403 0.0488 0.0194 0.0060
## PP.BehavInt1_GFPRB 0.7170 0.5226 0.3201 0.4200
## PP.BehavInt2_GFPRB 0.4727 0.3806 0.4729 0.3575
## PP.BehavInt3_GFPRB 0.7401 0.5288 0.2779 0.4722
## PP.BehavInt4_GFPRB 0.8765 0.6327 0.2253 0.5916
## PP.BehavInt1_CBB 0.7604 0.7981 0.2901 0.8666
## PP.BehavInt2_CBB 0.7346 0.8079 0.3138 0.9528
## PP.BehavInt3_CBB 0.7288 0.7858 0.2844 0.8522
## PP.BehavInt4_CBB 0.7020 0.7596 0.3060 0.8506
## PP.BehavInt1_PBPB 0.7170 0.5226 0.3201 0.4200
## PP.BehavInt2_PBPB 0.4727 0.3806 0.4729 0.3575
## PP.BehavInt3_PBPB 0.7401 0.5288 0.2779 0.4722
## PP.BehavInt4_PBPB 0.8765 0.6327 0.2253 0.5916
## PP.BehavInt1_PBFB 0.8368 0.7076 0.2813 0.6013
## PP.BehavInt2_PBFB 0.4682 0.4219 0.5113 0.4090
## PP.BehavInt3_PBFB 0.8318 0.7483 0.2552 0.6262
## PP.BehavInt4_PBFB 0.6013 0.5272 0.3938 0.4196
## PP.BehavInt1_VB 0.0871 0.1976 0.0030 0.2923
## PP.BehavInt2_VB 0.3395 0.5038 0.0284 0.5868
## PP.BehavInt3_VB 0.0835 0.1901 0.0032 0.3084
## PP.BehavInt4_VB 0.1202 0.2504 0.0054 0.3326
## PP.CCB_48 0.0000 0.0000 0.0000 0.0006
## PP.CCB_49 0.0000 0.0000 0.0000 0.0003
## PP.CCB_50 0.0000 0.0001 0.0000 0.0009
## PP.CCB_51 0.0000 0.0006 0.0000 0.0021
## PP.CNS_1 0.0000 0.0000 0.0000 0.0000
## PP.CNS_2 0.0000 0.0000 0.0000 0.0000
## PP.CNS_3 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_1 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_2R 0.8249 0.8826 0.3117 0.8007
## PP.ATNS_3 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_4 0.0000 0.0000 0.0000 0.0000
## PP.ATNS_5 0.0000 0.0000 0.0000 0.0000
## PP.Ind_3 0.0001 0.0004 0.0000 0.0000
## PP.Ind_4 0.0000 0.0000 0.0000 0.0000
## PP.Ind_7 0.0000 0.0000 0.0000 0.0000
## PP.Ind_8 0.0000 0.0000 0.0000 0.0000
## PP.Ind_1 0.0000 0.0000 0.0000
## PP.Ind_2 0.0000 0.0000 0.0000
## PP.Ind_5 0.0000 0.0000 0.0000
## PP.Ind_6 0.0000 0.0000 0.0000library(corrplot)
corrplot(mydata.cor6, method="color")
corrplot(mydata.cor6, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Naturalness (TOTAL SCALE), Risk, Benefit, Support
PP$corGroup <- data.frame(PP$Risk_Score_GFFB, PP$Risk_Score_GFPRB, PP$Risk_Score_CBB, PP$Risk_Score_PBFB, PP$Risk_Score_PBPB, PP$Risk_Score_VB, PP$Ben_Score_GFFB, PP$Ben_Score_GFPRB, PP$Ben_Score_CBB, PP$Ben_Score_PBFB, PP$Ben_Score_PBPB, PP$Ben_Score_VB, PP$Behav_Scale_GFFB, PP$Behav_Scale_GFPRB, PP$Behav_Scale_CBB, PP$Behav_Scale_PBPB, PP$Behav_Scale_PBFB, PP$Behav_Scale_VB, PP$Naturalness_Scale_GFFB_Tot, PP$Naturalness_Scale_GFPRB_Tot, PP$Naturalness_Scale_CBB_Tot, PP$Naturalness_Scale_PBPB_Tot, PP$Naturalness_Scale_PBFB_Tot, PP$Naturalness_Scale_VB_Tot)
mydata.cor7 = cor(PP$corGroup, use = "pairwise.complete.obs")
head(round(mydata.cor7,2))## PP.Risk_Score_GFFB PP.Risk_Score_GFPRB PP.Risk_Score_CBB
## PP.Risk_Score_GFFB 1.00 0.59 0.31
## PP.Risk_Score_GFPRB 0.59 1.00 0.35
## PP.Risk_Score_CBB 0.31 0.35 1.00
## PP.Risk_Score_PBFB 0.21 0.18 0.37
## PP.Risk_Score_PBPB 0.27 0.28 0.32
## PP.Risk_Score_VB 0.33 0.59 0.26
## PP.Risk_Score_PBFB PP.Risk_Score_PBPB PP.Risk_Score_VB
## PP.Risk_Score_GFFB 0.21 0.27 0.33
## PP.Risk_Score_GFPRB 0.18 0.28 0.59
## PP.Risk_Score_CBB 0.37 0.32 0.26
## PP.Risk_Score_PBFB 1.00 0.97 0.42
## PP.Risk_Score_PBPB 0.97 1.00 0.61
## PP.Risk_Score_VB 0.42 0.61 1.00
## PP.Ben_Score_GFFB PP.Ben_Score_GFPRB PP.Ben_Score_CBB
## PP.Risk_Score_GFFB -0.16 -0.16 0.28
## PP.Risk_Score_GFPRB 0.05 -0.21 0.36
## PP.Risk_Score_CBB 0.21 0.02 -0.17
## PP.Risk_Score_PBFB 0.23 0.20 0.03
## PP.Risk_Score_PBPB 0.33 0.13 0.22
## PP.Risk_Score_VB 0.28 0.11 0.39
## PP.Ben_Score_PBFB PP.Ben_Score_PBPB PP.Ben_Score_VB
## PP.Risk_Score_GFFB 0.32 0.30 0.15
## PP.Risk_Score_GFPRB 0.37 0.22 0.14
## PP.Risk_Score_CBB -0.07 -0.02 0.16
## PP.Risk_Score_PBFB -0.32 -0.25 -0.20
## PP.Risk_Score_PBPB -0.23 -0.28 -0.24
## PP.Risk_Score_VB 0.08 -0.13 -0.21
## PP.BehavInt1_GFFB PP.BehavInt2_GFFB PP.BehavInt3_GFFB
## PP.Risk_Score_GFFB -0.26 -0.24 -0.22
## PP.Risk_Score_GFPRB -0.06 -0.11 -0.02
## PP.Risk_Score_CBB 0.24 0.23 0.26
## PP.Risk_Score_PBFB 0.23 0.15 0.22
## PP.Risk_Score_PBPB 0.30 0.15 0.31
## PP.Risk_Score_VB 0.26 0.20 0.27
## PP.BehavInt4_GFFB PP.BehavInt1_GFPRB PP.BehavInt2_GFPRB
## PP.Risk_Score_GFFB -0.26 0.29 0.29
## PP.Risk_Score_GFPRB -0.08 0.21 0.27
## PP.Risk_Score_CBB 0.25 -0.04 0.02
## PP.Risk_Score_PBFB 0.22 -0.32 -0.22
## PP.Risk_Score_PBPB 0.27 -0.33 -0.24
## PP.Risk_Score_VB 0.26 -0.12 -0.06
## PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB PP.BehavInt1_CBB
## PP.Risk_Score_GFFB 0.29 0.28 0.30
## PP.Risk_Score_GFPRB 0.25 0.20 0.29
## PP.Risk_Score_CBB -0.04 -0.02 -0.25
## PP.Risk_Score_PBFB -0.28 -0.27 0.00
## PP.Risk_Score_PBPB -0.31 -0.29 0.16
## PP.Risk_Score_VB -0.05 -0.07 0.30
## PP.BehavInt2_CBB PP.BehavInt3_CBB PP.BehavInt4_CBB
## PP.Risk_Score_GFFB 0.28 0.31 0.32
## PP.Risk_Score_GFPRB 0.30 0.35 0.30
## PP.Risk_Score_CBB -0.21 -0.24 -0.23
## PP.Risk_Score_PBFB 0.02 -0.01 0.00
## PP.Risk_Score_PBPB 0.14 0.15 0.17
## PP.Risk_Score_VB 0.40 0.34 0.31
## PP.BehavInt1_PBPB PP.BehavInt2_PBPB PP.BehavInt3_PBPB
## PP.Risk_Score_GFFB 0.29 0.29 0.29
## PP.Risk_Score_GFPRB 0.21 0.27 0.25
## PP.Risk_Score_CBB -0.04 0.02 -0.04
## PP.Risk_Score_PBFB -0.32 -0.22 -0.28
## PP.Risk_Score_PBPB -0.33 -0.24 -0.31
## PP.Risk_Score_VB -0.12 -0.06 -0.05
## PP.BehavInt4_PBPB PP.BehavInt1_PBFB PP.BehavInt2_PBFB
## PP.Risk_Score_GFFB 0.28 0.40 0.37
## PP.Risk_Score_GFPRB 0.20 0.47 0.45
## PP.Risk_Score_CBB -0.02 -0.04 -0.04
## PP.Risk_Score_PBFB -0.27 -0.31 -0.26
## PP.Risk_Score_PBPB -0.29 -0.14 -0.10
## PP.Risk_Score_VB -0.07 0.15 0.12
## PP.BehavInt3_PBFB PP.BehavInt4_PBFB PP.BehavInt1_VB
## PP.Risk_Score_GFFB 0.37 0.38 0.18
## PP.Risk_Score_GFPRB 0.35 0.37 0.20
## PP.Risk_Score_CBB -0.07 -0.08 0.09
## PP.Risk_Score_PBFB -0.30 -0.30 -0.22
## PP.Risk_Score_PBPB -0.11 -0.16 -0.30
## PP.Risk_Score_VB 0.06 0.11 -0.17
## PP.BehavInt2_VB PP.BehavInt3_VB PP.BehavInt4_VB
## PP.Risk_Score_GFFB 0.23 0.16 0.17
## PP.Risk_Score_GFPRB 0.31 0.13 0.19
## PP.Risk_Score_CBB 0.18 0.18 0.14
## PP.Risk_Score_PBFB -0.17 -0.26 -0.20
## PP.Risk_Score_PBPB -0.26 -0.33 -0.28
## PP.Risk_Score_VB -0.06 -0.21 -0.15
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Risk_Score_GFFB -0.20 -0.58 -0.54 -0.35
## PP.Risk_Score_GFPRB -0.10 -0.45 -0.31 -0.35
## PP.Risk_Score_CBB 0.14 -0.12 -0.09 -0.14
## PP.Risk_Score_PBFB 0.18 -0.07 -0.02 -0.12
## PP.Risk_Score_PBPB 0.25 -0.14 -0.10 -0.22
## PP.Risk_Score_VB 0.27 -0.32 -0.25 -0.39
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Risk_Score_GFFB -0.17 -0.46 -0.50
## PP.Risk_Score_GFPRB -0.35 -0.64 -0.60
## PP.Risk_Score_CBB -0.04 -0.25 -0.27
## PP.Risk_Score_PBFB -0.07 -0.27 -0.18
## PP.Risk_Score_PBPB -0.26 -0.39 -0.33
## PP.Risk_Score_VB -0.16 -0.67 -0.48
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Risk_Score_GFFB -0.40 0.34 -0.02 -0.09
## PP.Risk_Score_GFPRB -0.43 0.33 -0.12 0.01
## PP.Risk_Score_CBB -0.13 -0.10 -0.48 -0.36
## PP.Risk_Score_PBFB -0.09 0.22 -0.19 -0.02
## PP.Risk_Score_PBPB -0.15 0.30 -0.12 0.05
## PP.Risk_Score_VB -0.45 0.45 0.00 0.16
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Risk_Score_GFFB -0.08 0.24 -0.06 -0.03
## PP.Risk_Score_GFPRB -0.04 0.33 0.03 -0.02
## PP.Risk_Score_CBB -0.20 -0.05 -0.28 -0.23
## PP.Risk_Score_PBFB 0.07 -0.09 -0.38 -0.32
## PP.Risk_Score_PBPB 0.16 -0.12 -0.41 -0.36
## PP.Risk_Score_VB 0.15 0.02 -0.30 -0.27
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Risk_Score_GFFB -0.12 0.33 0.06 0.09
## PP.Risk_Score_GFPRB 0.08 0.42 0.00 0.10
## PP.Risk_Score_CBB -0.08 -0.10 0.27 0.24
## PP.Risk_Score_PBFB 0.05 -0.21 0.39 0.30
## PP.Risk_Score_PBPB 0.02 -0.10 0.29 0.12
## PP.Risk_Score_VB -0.04 0.22 0.17 0.25
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Risk_Score_GFFB 0.07 0.11 -0.21 -0.13
## PP.Risk_Score_GFPRB 0.04 0.14 -0.27 -0.21
## PP.Risk_Score_CBB 0.09 0.13 -0.12 -0.09
## PP.Risk_Score_PBFB 0.12 -0.13 -0.26 -0.24
## PP.Risk_Score_PBPB -0.05 -0.24 -0.44 -0.35
## PP.Risk_Score_VB 0.02 -0.11 -0.55 -0.47
## PP.Nat_3R_VB
## PP.Risk_Score_GFFB -0.07
## PP.Risk_Score_GFPRB -0.20
## PP.Risk_Score_CBB 0.00
## PP.Risk_Score_PBFB -0.05
## PP.Risk_Score_PBPB -0.07
## PP.Risk_Score_VB -0.23library("Hmisc")
mydata.rcorr7 = rcorr(as.matrix(mydata.cor7))
mydata.rcorr7## PP.Risk_Score_GFFB PP.Risk_Score_GFPRB PP.Risk_Score_CBB
## PP.Risk_Score_GFFB 1.00 0.92 0.22
## PP.Risk_Score_GFPRB 0.92 1.00 0.30
## PP.Risk_Score_CBB 0.22 0.30 1.00
## PP.Risk_Score_PBFB 0.00 0.09 0.57
## PP.Risk_Score_PBPB 0.15 0.27 0.48
## PP.Risk_Score_VB 0.51 0.67 0.39
## PP.Ben_Score_GFFB 0.05 0.23 0.35
## PP.Ben_Score_GFPRB -0.24 -0.19 0.32
## PP.Ben_Score_CBB 0.66 0.71 -0.06
## PP.Ben_Score_PBFB 0.69 0.67 -0.09
## PP.Ben_Score_PBPB 0.63 0.55 -0.09
## PP.Ben_Score_VB 0.46 0.39 0.14
## PP.BehavInt1_GFFB -0.16 0.01 0.37
## PP.BehavInt2_GFFB -0.23 -0.08 0.35
## PP.BehavInt3_GFFB -0.12 0.06 0.38
## PP.BehavInt4_GFFB -0.19 -0.01 0.37
## PP.BehavInt1_GFPRB 0.60 0.51 -0.15
## PP.BehavInt2_GFPRB 0.66 0.58 -0.13
## PP.BehavInt3_GFPRB 0.63 0.56 -0.13
## PP.BehavInt4_GFPRB 0.63 0.54 -0.10
## PP.BehavInt1_CBB 0.65 0.67 -0.15
## PP.BehavInt2_CBB 0.63 0.68 -0.11
## PP.BehavInt3_CBB 0.65 0.69 -0.13
## PP.BehavInt4_CBB 0.65 0.67 -0.14
## PP.BehavInt1_PBPB 0.60 0.51 -0.15
## PP.BehavInt2_PBPB 0.66 0.58 -0.13
## PP.BehavInt3_PBPB 0.63 0.56 -0.13
## PP.BehavInt4_PBPB 0.63 0.54 -0.10
## PP.BehavInt1_PBFB 0.75 0.72 -0.07
## PP.BehavInt2_PBFB 0.74 0.73 -0.07
## PP.BehavInt3_PBFB 0.72 0.69 -0.07
## PP.BehavInt4_PBFB 0.72 0.68 -0.09
## PP.BehavInt1_VB 0.52 0.45 0.05
## PP.BehavInt2_VB 0.58 0.55 0.09
## PP.BehavInt3_VB 0.49 0.41 0.05
## PP.BehavInt4_VB 0.52 0.45 0.06
## PP.Nat_1_GFFB -0.16 -0.03 0.27
## PP.Nat_4R_GFFB -0.94 -0.90 -0.20
## PP.Nat_2R_GFFB -0.90 -0.80 -0.13
## PP.Nat_3R_GFFB -0.77 -0.80 -0.26
## PP.Nat_1_GFPRB -0.49 -0.59 0.02
## PP.Nat_4R_GFPRB -0.81 -0.93 -0.35
## PP.Nat_2R_GFPRB -0.82 -0.92 -0.33
## PP.Nat_3R_GFPRB -0.79 -0.87 -0.19
## PP.Nat_1_CBB 0.64 0.71 0.01
## PP.Nat_4R_CBB -0.02 -0.07 -0.82
## PP.Nat_2R_CBB -0.10 -0.03 -0.60
## PP.Nat_3R_CBB -0.16 -0.08 -0.44
## PP.Nat_1_PBPB 0.67 0.66 -0.06
## PP.Nat_4R_PBPB -0.06 -0.12 -0.51
## PP.Nat_2R_PBPB -0.02 -0.10 -0.54
## PP.Nat_3R_PBPB -0.21 -0.14 -0.18
## PP.Nat_1_PBFB 0.73 0.75 -0.10
## PP.Nat_4R_PBFB -0.19 -0.16 0.57
## PP.Nat_2R_PBFB 0.07 0.10 0.59
## PP.Nat_3R_PBFB 0.08 0.06 0.40
## PP.Nat_1_VB 0.46 0.41 0.07
## PP.Nat_4R_VB -0.32 -0.43 -0.32
## PP.Nat_2R_VB -0.40 -0.53 -0.30
## PP.Nat_3R_VB -0.39 -0.50 -0.16
## PP.Risk_Score_PBFB PP.Risk_Score_PBPB PP.Risk_Score_VB
## PP.Risk_Score_GFFB 0.00 0.15 0.51
## PP.Risk_Score_GFPRB 0.09 0.27 0.67
## PP.Risk_Score_CBB 0.57 0.48 0.39
## PP.Risk_Score_PBFB 1.00 0.94 0.66
## PP.Risk_Score_PBPB 0.94 1.00 0.82
## PP.Risk_Score_VB 0.66 0.82 1.00
## PP.Ben_Score_GFFB 0.41 0.52 0.62
## PP.Ben_Score_GFPRB 0.36 0.31 0.25
## PP.Ben_Score_CBB -0.03 0.22 0.62
## PP.Ben_Score_PBFB -0.50 -0.31 0.17
## PP.Ben_Score_PBPB -0.60 -0.48 -0.05
## PP.Ben_Score_VB -0.55 -0.53 -0.21
## PP.BehavInt1_GFFB 0.48 0.53 0.52
## PP.BehavInt2_GFFB 0.41 0.45 0.42
## PP.BehavInt3_GFFB 0.48 0.54 0.55
## PP.BehavInt4_GFFB 0.47 0.53 0.51
## PP.BehavInt1_GFPRB -0.66 -0.54 -0.11
## PP.BehavInt2_GFPRB -0.59 -0.46 -0.03
## PP.BehavInt3_GFPRB -0.61 -0.49 -0.05
## PP.BehavInt4_GFPRB -0.61 -0.49 -0.07
## PP.BehavInt1_CBB -0.12 0.13 0.54
## PP.BehavInt2_CBB -0.06 0.19 0.60
## PP.BehavInt3_CBB -0.09 0.16 0.57
## PP.BehavInt4_CBB -0.09 0.16 0.56
## PP.BehavInt1_PBPB -0.66 -0.54 -0.11
## PP.BehavInt2_PBPB -0.59 -0.46 -0.03
## PP.BehavInt3_PBPB -0.61 -0.49 -0.05
## PP.BehavInt4_PBPB -0.61 -0.49 -0.07
## PP.BehavInt1_PBFB -0.49 -0.29 0.18
## PP.BehavInt2_PBFB -0.45 -0.24 0.23
## PP.BehavInt3_PBFB -0.48 -0.29 0.18
## PP.BehavInt4_PBFB -0.49 -0.30 0.18
## PP.BehavInt1_VB -0.60 -0.55 -0.19
## PP.BehavInt2_VB -0.56 -0.49 -0.11
## PP.BehavInt3_VB -0.63 -0.58 -0.23
## PP.BehavInt4_VB -0.59 -0.55 -0.19
## PP.Nat_1_GFFB 0.45 0.51 0.52
## PP.Nat_4R_GFFB 0.03 -0.15 -0.54
## PP.Nat_2R_GFFB 0.14 -0.01 -0.39
## PP.Nat_3R_GFFB -0.14 -0.32 -0.69
## PP.Nat_1_GFPRB -0.12 -0.27 -0.36
## PP.Nat_4R_GFPRB -0.32 -0.51 -0.83
## PP.Nat_2R_GFPRB -0.20 -0.39 -0.74
## PP.Nat_3R_GFPRB -0.09 -0.29 -0.71
## PP.Nat_1_CBB 0.15 0.38 0.73
## PP.Nat_4R_CBB -0.36 -0.21 -0.08
## PP.Nat_2R_CBB -0.01 0.14 0.18
## PP.Nat_3R_CBB 0.12 0.24 0.18
## PP.Nat_1_PBPB -0.47 -0.33 0.12
## PP.Nat_4R_PBPB -0.76 -0.79 -0.65
## PP.Nat_2R_PBPB -0.67 -0.68 -0.58
## PP.Nat_3R_PBPB -0.04 -0.06 -0.22
## PP.Nat_1_PBFB -0.38 -0.16 0.33
## PP.Nat_4R_PBFB 0.73 0.60 0.36
## PP.Nat_2R_PBFB 0.51 0.41 0.39
## PP.Nat_3R_PBFB 0.20 0.12 0.18
## PP.Nat_1_VB -0.56 -0.52 -0.19
## PP.Nat_4R_VB -0.68 -0.81 -0.89
## PP.Nat_2R_VB -0.56 -0.70 -0.88
## PP.Nat_3R_VB -0.27 -0.41 -0.67
## PP.Ben_Score_GFFB PP.Ben_Score_GFPRB PP.Ben_Score_CBB
## PP.Risk_Score_GFFB 0.05 -0.24 0.66
## PP.Risk_Score_GFPRB 0.23 -0.19 0.71
## PP.Risk_Score_CBB 0.35 0.32 -0.06
## PP.Risk_Score_PBFB 0.41 0.36 -0.03
## PP.Risk_Score_PBPB 0.52 0.31 0.22
## PP.Risk_Score_VB 0.62 0.25 0.62
## PP.Ben_Score_GFFB 1.00 0.72 0.60
## PP.Ben_Score_GFPRB 0.72 1.00 0.19
## PP.Ben_Score_CBB 0.60 0.19 1.00
## PP.Ben_Score_PBFB 0.24 -0.04 0.78
## PP.Ben_Score_PBPB 0.09 -0.07 0.63
## PP.Ben_Score_VB 0.06 0.08 0.36
## PP.BehavInt1_GFFB 0.96 0.78 0.42
## PP.BehavInt2_GFFB 0.92 0.78 0.35
## PP.BehavInt3_GFFB 0.97 0.77 0.45
## PP.BehavInt4_GFFB 0.95 0.78 0.39
## PP.BehavInt1_GFPRB 0.02 -0.13 0.59
## PP.BehavInt2_GFPRB 0.04 -0.15 0.63
## PP.BehavInt3_GFPRB 0.05 -0.11 0.63
## PP.BehavInt4_GFPRB 0.05 -0.10 0.60
## PP.BehavInt1_CBB 0.52 0.12 0.98
## PP.BehavInt2_CBB 0.58 0.16 0.98
## PP.BehavInt3_CBB 0.54 0.13 0.99
## PP.BehavInt4_CBB 0.54 0.14 0.99
## PP.BehavInt1_PBPB 0.02 -0.13 0.59
## PP.BehavInt2_PBPB 0.04 -0.15 0.63
## PP.BehavInt3_PBPB 0.05 -0.11 0.63
## PP.BehavInt4_PBPB 0.05 -0.10 0.60
## PP.BehavInt1_PBFB 0.17 -0.12 0.76
## PP.BehavInt2_PBFB 0.23 -0.09 0.79
## PP.BehavInt3_PBFB 0.23 -0.05 0.77
## PP.BehavInt4_PBFB 0.23 -0.06 0.78
## PP.BehavInt1_VB 0.00 -0.01 0.41
## PP.BehavInt2_VB 0.01 -0.07 0.43
## PP.BehavInt3_VB 0.00 0.00 0.40
## PP.BehavInt4_VB 0.00 -0.01 0.41
## PP.Nat_1_GFFB 0.89 0.77 0.41
## PP.Nat_4R_GFFB -0.20 0.07 -0.73
## PP.Nat_2R_GFFB -0.15 0.05 -0.72
## PP.Nat_3R_GFFB -0.56 -0.24 -0.84
## PP.Nat_1_GFPRB 0.23 0.63 -0.20
## PP.Nat_4R_GFPRB -0.41 0.00 -0.72
## PP.Nat_2R_GFPRB -0.37 0.01 -0.70
## PP.Nat_3R_GFPRB -0.46 -0.09 -0.84
## PP.Nat_1_CBB 0.64 0.21 0.94
## PP.Nat_4R_CBB -0.31 -0.41 0.15
## PP.Nat_2R_CBB -0.13 -0.34 0.10
## PP.Nat_3R_CBB -0.16 -0.31 -0.06
## PP.Nat_1_PBPB 0.18 -0.06 0.70
## PP.Nat_4R_PBPB -0.60 -0.44 -0.23
## PP.Nat_2R_PBPB -0.73 -0.60 -0.28
## PP.Nat_3R_PBPB -0.52 -0.45 -0.48
## PP.Nat_1_PBFB 0.32 -0.04 0.85
## PP.Nat_4R_PBFB 0.41 0.50 -0.16
## PP.Nat_2R_PBFB 0.52 0.52 0.15
## PP.Nat_3R_PBFB 0.48 0.51 0.21
## PP.Nat_1_VB 0.04 0.02 0.38
## PP.Nat_4R_VB -0.63 -0.35 -0.54
## PP.Nat_2R_VB -0.71 -0.39 -0.67
## PP.Nat_3R_VB -0.71 -0.37 -0.74
## PP.Ben_Score_PBFB PP.Ben_Score_PBPB PP.Ben_Score_VB
## PP.Risk_Score_GFFB 0.69 0.63 0.46
## PP.Risk_Score_GFPRB 0.67 0.55 0.39
## PP.Risk_Score_CBB -0.09 -0.09 0.14
## PP.Risk_Score_PBFB -0.50 -0.60 -0.55
## PP.Risk_Score_PBPB -0.31 -0.48 -0.53
## PP.Risk_Score_VB 0.17 -0.05 -0.21
## PP.Ben_Score_GFFB 0.24 0.09 0.06
## PP.Ben_Score_GFPRB -0.04 -0.07 0.08
## PP.Ben_Score_CBB 0.78 0.63 0.36
## PP.Ben_Score_PBFB 1.00 0.95 0.79
## PP.Ben_Score_PBPB 0.95 1.00 0.89
## PP.Ben_Score_VB 0.79 0.89 1.00
## PP.BehavInt1_GFFB 0.04 -0.10 -0.08
## PP.BehavInt2_GFFB 0.00 -0.11 -0.07
## PP.BehavInt3_GFFB 0.07 -0.07 -0.06
## PP.BehavInt4_GFFB 0.01 -0.12 -0.09
## PP.BehavInt1_GFPRB 0.94 0.98 0.86
## PP.BehavInt2_GFPRB 0.94 0.98 0.84
## PP.BehavInt3_GFPRB 0.95 0.99 0.86
## PP.BehavInt4_GFPRB 0.94 0.99 0.88
## PP.BehavInt1_CBB 0.81 0.67 0.39
## PP.BehavInt2_CBB 0.77 0.62 0.34
## PP.BehavInt3_CBB 0.80 0.65 0.37
## PP.BehavInt4_CBB 0.79 0.65 0.37
## PP.BehavInt1_PBPB 0.94 0.98 0.86
## PP.BehavInt2_PBPB 0.94 0.98 0.84
## PP.BehavInt3_PBPB 0.95 0.99 0.86
## PP.BehavInt4_PBPB 0.94 0.99 0.88
## PP.BehavInt1_PBFB 0.99 0.94 0.78
## PP.BehavInt2_PBFB 0.98 0.91 0.75
## PP.BehavInt3_PBFB 0.99 0.94 0.79
## PP.BehavInt4_PBFB 0.99 0.94 0.78
## PP.BehavInt1_VB 0.84 0.93 0.98
## PP.BehavInt2_VB 0.85 0.92 0.93
## PP.BehavInt3_VB 0.84 0.93 0.98
## PP.BehavInt4_VB 0.85 0.93 0.98
## PP.Nat_1_GFFB 0.02 -0.12 -0.12
## PP.Nat_4R_GFFB -0.75 -0.66 -0.49
## PP.Nat_2R_GFFB -0.80 -0.76 -0.59
## PP.Nat_3R_GFFB -0.71 -0.58 -0.38
## PP.Nat_1_GFPRB -0.14 -0.06 0.11
## PP.Nat_4R_GFPRB -0.53 -0.37 -0.21
## PP.Nat_2R_GFPRB -0.59 -0.43 -0.31
## PP.Nat_3R_GFPRB -0.69 -0.55 -0.36
## PP.Nat_1_CBB 0.66 0.49 0.22
## PP.Nat_4R_CBB 0.08 0.02 -0.27
## PP.Nat_2R_CBB -0.13 -0.27 -0.55
## PP.Nat_3R_CBB -0.27 -0.40 -0.62
## PP.Nat_1_PBPB 0.94 0.94 0.82
## PP.Nat_4R_PBPB 0.19 0.34 0.34
## PP.Nat_2R_PBPB 0.09 0.20 0.12
## PP.Nat_3R_PBPB -0.39 -0.36 -0.35
## PP.Nat_1_PBFB 0.96 0.87 0.67
## PP.Nat_4R_PBFB -0.48 -0.50 -0.40
## PP.Nat_2R_PBFB -0.06 -0.05 0.06
## PP.Nat_3R_PBFB 0.15 0.18 0.26
## PP.Nat_1_VB 0.78 0.88 0.93
## PP.Nat_4R_VB -0.03 0.17 0.33
## PP.Nat_2R_VB -0.23 -0.06 0.11
## PP.Nat_3R_VB -0.42 -0.30 -0.13
## PP.BehavInt1_GFFB PP.BehavInt2_GFFB PP.BehavInt3_GFFB
## PP.Risk_Score_GFFB -0.16 -0.23 -0.12
## PP.Risk_Score_GFPRB 0.01 -0.08 0.06
## PP.Risk_Score_CBB 0.37 0.35 0.38
## PP.Risk_Score_PBFB 0.48 0.41 0.48
## PP.Risk_Score_PBPB 0.53 0.45 0.54
## PP.Risk_Score_VB 0.52 0.42 0.55
## PP.Ben_Score_GFFB 0.96 0.92 0.97
## PP.Ben_Score_GFPRB 0.78 0.78 0.77
## PP.Ben_Score_CBB 0.42 0.35 0.45
## PP.Ben_Score_PBFB 0.04 0.00 0.07
## PP.Ben_Score_PBPB -0.10 -0.11 -0.07
## PP.Ben_Score_VB -0.08 -0.07 -0.06
## PP.BehavInt1_GFFB 1.00 0.98 0.99
## PP.BehavInt2_GFFB 0.98 1.00 0.97
## PP.BehavInt3_GFFB 0.99 0.97 1.00
## PP.BehavInt4_GFFB 0.99 0.97 0.99
## PP.BehavInt1_GFPRB -0.16 -0.17 -0.14
## PP.BehavInt2_GFPRB -0.16 -0.19 -0.13
## PP.BehavInt3_GFPRB -0.14 -0.15 -0.10
## PP.BehavInt4_GFPRB -0.13 -0.15 -0.10
## PP.BehavInt1_CBB 0.34 0.28 0.38
## PP.BehavInt2_CBB 0.40 0.33 0.43
## PP.BehavInt3_CBB 0.36 0.30 0.39
## PP.BehavInt4_CBB 0.36 0.29 0.39
## PP.BehavInt1_PBPB -0.16 -0.17 -0.14
## PP.BehavInt2_PBPB -0.16 -0.19 -0.13
## PP.BehavInt3_PBPB -0.14 -0.15 -0.10
## PP.BehavInt4_PBPB -0.13 -0.15 -0.10
## PP.BehavInt1_PBFB -0.04 -0.08 -0.01
## PP.BehavInt2_PBFB 0.01 -0.04 0.05
## PP.BehavInt3_PBFB 0.01 -0.03 0.04
## PP.BehavInt4_PBFB 0.01 -0.03 0.04
## PP.BehavInt1_VB -0.16 -0.15 -0.14
## PP.BehavInt2_VB -0.17 -0.19 -0.14
## PP.BehavInt3_VB -0.15 -0.14 -0.13
## PP.BehavInt4_VB -0.16 -0.16 -0.14
## PP.Nat_1_GFFB 0.92 0.90 0.92
## PP.Nat_4R_GFFB 0.02 0.10 -0.03
## PP.Nat_2R_GFFB 0.06 0.12 0.01
## PP.Nat_3R_GFFB -0.38 -0.29 -0.41
## PP.Nat_1_GFPRB 0.36 0.45 0.33
## PP.Nat_4R_GFPRB -0.22 -0.11 -0.26
## PP.Nat_2R_GFPRB -0.16 -0.06 -0.20
## PP.Nat_3R_GFPRB -0.26 -0.18 -0.30
## PP.Nat_1_CBB 0.46 0.37 0.50
## PP.Nat_4R_CBB -0.35 -0.37 -0.35
## PP.Nat_2R_CBB -0.16 -0.21 -0.15
## PP.Nat_3R_CBB -0.16 -0.23 -0.16
## PP.Nat_1_PBPB -0.03 -0.08 0.01
## PP.Nat_4R_PBPB -0.64 -0.60 -0.63
## PP.Nat_2R_PBPB -0.78 -0.77 -0.77
## PP.Nat_3R_PBPB -0.51 -0.54 -0.50
## PP.Nat_1_PBFB 0.09 0.03 0.12
## PP.Nat_4R_PBFB 0.53 0.54 0.52
## PP.Nat_2R_PBFB 0.57 0.59 0.56
## PP.Nat_3R_PBFB 0.51 0.54 0.49
## PP.Nat_1_VB -0.10 -0.10 -0.08
## PP.Nat_4R_VB -0.60 -0.54 -0.62
## PP.Nat_2R_VB -0.63 -0.54 -0.66
## PP.Nat_3R_VB -0.61 -0.55 -0.63
## PP.BehavInt4_GFFB PP.BehavInt1_GFPRB PP.BehavInt2_GFPRB
## PP.Risk_Score_GFFB -0.19 0.60 0.66
## PP.Risk_Score_GFPRB -0.01 0.51 0.58
## PP.Risk_Score_CBB 0.37 -0.15 -0.13
## PP.Risk_Score_PBFB 0.47 -0.66 -0.59
## PP.Risk_Score_PBPB 0.53 -0.54 -0.46
## PP.Risk_Score_VB 0.51 -0.11 -0.03
## PP.Ben_Score_GFFB 0.95 0.02 0.04
## PP.Ben_Score_GFPRB 0.78 -0.13 -0.15
## PP.Ben_Score_CBB 0.39 0.59 0.63
## PP.Ben_Score_PBFB 0.01 0.94 0.94
## PP.Ben_Score_PBPB -0.12 0.98 0.98
## PP.Ben_Score_VB -0.09 0.86 0.84
## PP.BehavInt1_GFFB 0.99 -0.16 -0.16
## PP.BehavInt2_GFFB 0.97 -0.17 -0.19
## PP.BehavInt3_GFFB 0.99 -0.14 -0.13
## PP.BehavInt4_GFFB 1.00 -0.18 -0.18
## PP.BehavInt1_GFPRB -0.18 1.00 0.98
## PP.BehavInt2_GFPRB -0.18 0.98 1.00
## PP.BehavInt3_GFPRB -0.15 0.99 0.98
## PP.BehavInt4_GFPRB -0.15 0.99 0.98
## PP.BehavInt1_CBB 0.31 0.65 0.69
## PP.BehavInt2_CBB 0.37 0.60 0.65
## PP.BehavInt3_CBB 0.33 0.63 0.66
## PP.BehavInt4_CBB 0.33 0.62 0.67
## PP.BehavInt1_PBPB -0.18 1.00 0.98
## PP.BehavInt2_PBPB -0.18 0.98 1.00
## PP.BehavInt3_PBPB -0.15 0.99 0.98
## PP.BehavInt4_PBPB -0.15 0.99 0.98
## PP.BehavInt1_PBFB -0.07 0.94 0.95
## PP.BehavInt2_PBFB -0.01 0.91 0.94
## PP.BehavInt3_PBFB -0.01 0.93 0.94
## PP.BehavInt4_PBFB -0.01 0.93 0.94
## PP.BehavInt1_VB -0.17 0.92 0.91
## PP.BehavInt2_VB -0.19 0.91 0.91
## PP.BehavInt3_VB -0.17 0.92 0.89
## PP.BehavInt4_VB -0.18 0.92 0.91
## PP.Nat_1_GFFB 0.92 -0.17 -0.17
## PP.Nat_4R_GFFB 0.04 -0.63 -0.69
## PP.Nat_2R_GFFB 0.08 -0.73 -0.77
## PP.Nat_3R_GFFB -0.35 -0.52 -0.57
## PP.Nat_1_GFPRB 0.37 -0.05 -0.14
## PP.Nat_4R_GFPRB -0.20 -0.31 -0.40
## PP.Nat_2R_GFPRB -0.15 -0.38 -0.46
## PP.Nat_3R_GFPRB -0.23 -0.50 -0.56
## PP.Nat_1_CBB 0.44 0.45 0.51
## PP.Nat_4R_CBB -0.35 0.07 0.09
## PP.Nat_2R_CBB -0.15 -0.23 -0.19
## PP.Nat_3R_CBB -0.15 -0.37 -0.32
## PP.Nat_1_PBPB -0.05 0.92 0.93
## PP.Nat_4R_PBPB -0.63 0.40 0.37
## PP.Nat_2R_PBPB -0.77 0.27 0.25
## PP.Nat_3R_PBPB -0.48 -0.31 -0.29
## PP.Nat_1_PBFB 0.07 0.85 0.87
## PP.Nat_4R_PBFB 0.52 -0.57 -0.56
## PP.Nat_2R_PBFB 0.55 -0.14 -0.12
## PP.Nat_3R_PBFB 0.49 0.10 0.08
## PP.Nat_1_VB -0.12 0.85 0.84
## PP.Nat_4R_VB -0.59 0.23 0.17
## PP.Nat_2R_VB -0.62 0.01 -0.05
## PP.Nat_3R_VB -0.59 -0.22 -0.26
## PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB PP.BehavInt1_CBB
## PP.Risk_Score_GFFB 0.63 0.63 0.65
## PP.Risk_Score_GFPRB 0.56 0.54 0.67
## PP.Risk_Score_CBB -0.13 -0.10 -0.15
## PP.Risk_Score_PBFB -0.61 -0.61 -0.12
## PP.Risk_Score_PBPB -0.49 -0.49 0.13
## PP.Risk_Score_VB -0.05 -0.07 0.54
## PP.Ben_Score_GFFB 0.05 0.05 0.52
## PP.Ben_Score_GFPRB -0.11 -0.10 0.12
## PP.Ben_Score_CBB 0.63 0.60 0.98
## PP.Ben_Score_PBFB 0.95 0.94 0.81
## PP.Ben_Score_PBPB 0.99 0.99 0.67
## PP.Ben_Score_VB 0.86 0.88 0.39
## PP.BehavInt1_GFFB -0.14 -0.13 0.34
## PP.BehavInt2_GFFB -0.15 -0.15 0.28
## PP.BehavInt3_GFFB -0.10 -0.10 0.38
## PP.BehavInt4_GFFB -0.15 -0.15 0.31
## PP.BehavInt1_GFPRB 0.99 0.99 0.65
## PP.BehavInt2_GFPRB 0.98 0.98 0.69
## PP.BehavInt3_GFPRB 1.00 0.99 0.68
## PP.BehavInt4_GFPRB 0.99 1.00 0.66
## PP.BehavInt1_CBB 0.68 0.66 1.00
## PP.BehavInt2_CBB 0.63 0.61 0.99
## PP.BehavInt3_CBB 0.66 0.63 0.99
## PP.BehavInt4_CBB 0.66 0.63 0.99
## PP.BehavInt1_PBPB 0.99 0.99 0.65
## PP.BehavInt2_PBPB 0.98 0.98 0.69
## PP.BehavInt3_PBPB 1.00 0.99 0.68
## PP.BehavInt4_PBPB 0.99 1.00 0.66
## PP.BehavInt1_PBFB 0.95 0.94 0.79
## PP.BehavInt2_PBFB 0.92 0.91 0.82
## PP.BehavInt3_PBFB 0.94 0.94 0.80
## PP.BehavInt4_PBFB 0.95 0.94 0.81
## PP.BehavInt1_VB 0.92 0.93 0.45
## PP.BehavInt2_VB 0.91 0.92 0.47
## PP.BehavInt3_VB 0.92 0.93 0.43
## PP.BehavInt4_VB 0.92 0.93 0.45
## PP.Nat_1_GFFB -0.15 -0.15 0.34
## PP.Nat_4R_GFFB -0.66 -0.66 -0.71
## PP.Nat_2R_GFFB -0.76 -0.76 -0.71
## PP.Nat_3R_GFFB -0.56 -0.56 -0.79
## PP.Nat_1_GFPRB -0.08 -0.06 -0.20
## PP.Nat_4R_GFPRB -0.37 -0.35 -0.66
## PP.Nat_2R_GFPRB -0.44 -0.41 -0.64
## PP.Nat_3R_GFPRB -0.54 -0.52 -0.79
## PP.Nat_1_CBB 0.49 0.47 0.92
## PP.Nat_4R_CBB 0.07 0.02 0.23
## PP.Nat_2R_CBB -0.22 -0.27 0.13
## PP.Nat_3R_CBB -0.34 -0.39 -0.04
## PP.Nat_1_PBPB 0.94 0.93 0.72
## PP.Nat_4R_PBPB 0.36 0.35 -0.15
## PP.Nat_2R_PBPB 0.24 0.22 -0.21
## PP.Nat_3R_PBPB -0.33 -0.34 -0.48
## PP.Nat_1_PBFB 0.87 0.85 0.87
## PP.Nat_4R_PBFB -0.54 -0.51 -0.24
## PP.Nat_2R_PBFB -0.10 -0.08 0.07
## PP.Nat_3R_PBFB 0.13 0.16 0.16
## PP.Nat_1_VB 0.86 0.87 0.42
## PP.Nat_4R_VB 0.18 0.20 -0.46
## PP.Nat_2R_VB -0.06 -0.04 -0.59
## PP.Nat_3R_VB -0.29 -0.26 -0.70
## PP.BehavInt2_CBB PP.BehavInt3_CBB PP.BehavInt4_CBB
## PP.Risk_Score_GFFB 0.63 0.65 0.65
## PP.Risk_Score_GFPRB 0.68 0.69 0.67
## PP.Risk_Score_CBB -0.11 -0.13 -0.14
## PP.Risk_Score_PBFB -0.06 -0.09 -0.09
## PP.Risk_Score_PBPB 0.19 0.16 0.16
## PP.Risk_Score_VB 0.60 0.57 0.56
## PP.Ben_Score_GFFB 0.58 0.54 0.54
## PP.Ben_Score_GFPRB 0.16 0.13 0.14
## PP.Ben_Score_CBB 0.98 0.99 0.99
## PP.Ben_Score_PBFB 0.77 0.80 0.79
## PP.Ben_Score_PBPB 0.62 0.65 0.65
## PP.Ben_Score_VB 0.34 0.37 0.37
## PP.BehavInt1_GFFB 0.40 0.36 0.36
## PP.BehavInt2_GFFB 0.33 0.30 0.29
## PP.BehavInt3_GFFB 0.43 0.39 0.39
## PP.BehavInt4_GFFB 0.37 0.33 0.33
## PP.BehavInt1_GFPRB 0.60 0.63 0.62
## PP.BehavInt2_GFPRB 0.65 0.66 0.67
## PP.BehavInt3_GFPRB 0.63 0.66 0.66
## PP.BehavInt4_GFPRB 0.61 0.63 0.63
## PP.BehavInt1_CBB 0.99 0.99 0.99
## PP.BehavInt2_CBB 1.00 0.99 0.99
## PP.BehavInt3_CBB 0.99 1.00 0.99
## PP.BehavInt4_CBB 0.99 0.99 1.00
## PP.BehavInt1_PBPB 0.60 0.63 0.62
## PP.BehavInt2_PBPB 0.65 0.66 0.67
## PP.BehavInt3_PBPB 0.63 0.66 0.66
## PP.BehavInt4_PBPB 0.61 0.63 0.63
## PP.BehavInt1_PBFB 0.76 0.78 0.77
## PP.BehavInt2_PBFB 0.79 0.81 0.80
## PP.BehavInt3_PBFB 0.76 0.79 0.78
## PP.BehavInt4_PBFB 0.77 0.79 0.79
## PP.BehavInt1_VB 0.40 0.43 0.43
## PP.BehavInt2_VB 0.43 0.45 0.44
## PP.BehavInt3_VB 0.38 0.41 0.41
## PP.BehavInt4_VB 0.40 0.43 0.43
## PP.Nat_1_GFFB 0.40 0.36 0.36
## PP.Nat_4R_GFFB -0.70 -0.72 -0.71
## PP.Nat_2R_GFFB -0.69 -0.71 -0.71
## PP.Nat_3R_GFFB -0.81 -0.81 -0.81
## PP.Nat_1_GFPRB -0.21 -0.22 -0.21
## PP.Nat_4R_GFPRB -0.70 -0.69 -0.68
## PP.Nat_2R_GFPRB -0.67 -0.66 -0.65
## PP.Nat_3R_GFPRB -0.81 -0.81 -0.80
## PP.Nat_1_CBB 0.95 0.93 0.93
## PP.Nat_4R_CBB 0.22 0.22 0.23
## PP.Nat_2R_CBB 0.16 0.14 0.14
## PP.Nat_3R_CBB -0.01 -0.02 -0.02
## PP.Nat_1_PBPB 0.70 0.71 0.72
## PP.Nat_4R_PBPB -0.19 -0.17 -0.16
## PP.Nat_2R_PBPB -0.25 -0.22 -0.22
## PP.Nat_3R_PBPB -0.46 -0.47 -0.47
## PP.Nat_1_PBFB 0.85 0.86 0.85
## PP.Nat_4R_PBFB -0.21 -0.22 -0.23
## PP.Nat_2R_PBFB 0.10 0.09 0.08
## PP.Nat_3R_PBFB 0.16 0.16 0.16
## PP.Nat_1_VB 0.38 0.39 0.39
## PP.Nat_4R_VB -0.51 -0.50 -0.48
## PP.Nat_2R_VB -0.65 -0.63 -0.63
## PP.Nat_3R_VB -0.73 -0.72 -0.72
## PP.BehavInt1_PBPB PP.BehavInt2_PBPB PP.BehavInt3_PBPB
## PP.Risk_Score_GFFB 0.60 0.66 0.63
## PP.Risk_Score_GFPRB 0.51 0.58 0.56
## PP.Risk_Score_CBB -0.15 -0.13 -0.13
## PP.Risk_Score_PBFB -0.66 -0.59 -0.61
## PP.Risk_Score_PBPB -0.54 -0.46 -0.49
## PP.Risk_Score_VB -0.11 -0.03 -0.05
## PP.Ben_Score_GFFB 0.02 0.04 0.05
## PP.Ben_Score_GFPRB -0.13 -0.15 -0.11
## PP.Ben_Score_CBB 0.59 0.63 0.63
## PP.Ben_Score_PBFB 0.94 0.94 0.95
## PP.Ben_Score_PBPB 0.98 0.98 0.99
## PP.Ben_Score_VB 0.86 0.84 0.86
## PP.BehavInt1_GFFB -0.16 -0.16 -0.14
## PP.BehavInt2_GFFB -0.17 -0.19 -0.15
## PP.BehavInt3_GFFB -0.14 -0.13 -0.10
## PP.BehavInt4_GFFB -0.18 -0.18 -0.15
## PP.BehavInt1_GFPRB 1.00 0.98 0.99
## PP.BehavInt2_GFPRB 0.98 1.00 0.98
## PP.BehavInt3_GFPRB 0.99 0.98 1.00
## PP.BehavInt4_GFPRB 0.99 0.98 0.99
## PP.BehavInt1_CBB 0.65 0.69 0.68
## PP.BehavInt2_CBB 0.60 0.65 0.63
## PP.BehavInt3_CBB 0.63 0.66 0.66
## PP.BehavInt4_CBB 0.62 0.67 0.66
## PP.BehavInt1_PBPB 1.00 0.98 0.99
## PP.BehavInt2_PBPB 0.98 1.00 0.98
## PP.BehavInt3_PBPB 0.99 0.98 1.00
## PP.BehavInt4_PBPB 0.99 0.98 0.99
## PP.BehavInt1_PBFB 0.94 0.95 0.95
## PP.BehavInt2_PBFB 0.91 0.94 0.92
## PP.BehavInt3_PBFB 0.93 0.94 0.94
## PP.BehavInt4_PBFB 0.93 0.94 0.95
## PP.BehavInt1_VB 0.92 0.91 0.92
## PP.BehavInt2_VB 0.91 0.91 0.91
## PP.BehavInt3_VB 0.92 0.89 0.92
## PP.BehavInt4_VB 0.92 0.91 0.92
## PP.Nat_1_GFFB -0.17 -0.17 -0.15
## PP.Nat_4R_GFFB -0.63 -0.69 -0.66
## PP.Nat_2R_GFFB -0.73 -0.77 -0.76
## PP.Nat_3R_GFFB -0.52 -0.57 -0.56
## PP.Nat_1_GFPRB -0.05 -0.14 -0.08
## PP.Nat_4R_GFPRB -0.31 -0.40 -0.37
## PP.Nat_2R_GFPRB -0.38 -0.46 -0.44
## PP.Nat_3R_GFPRB -0.50 -0.56 -0.54
## PP.Nat_1_CBB 0.45 0.51 0.49
## PP.Nat_4R_CBB 0.07 0.09 0.07
## PP.Nat_2R_CBB -0.23 -0.19 -0.22
## PP.Nat_3R_CBB -0.37 -0.32 -0.34
## PP.Nat_1_PBPB 0.92 0.93 0.94
## PP.Nat_4R_PBPB 0.40 0.37 0.36
## PP.Nat_2R_PBPB 0.27 0.25 0.24
## PP.Nat_3R_PBPB -0.31 -0.29 -0.33
## PP.Nat_1_PBFB 0.85 0.87 0.87
## PP.Nat_4R_PBFB -0.57 -0.56 -0.54
## PP.Nat_2R_PBFB -0.14 -0.12 -0.10
## PP.Nat_3R_PBFB 0.10 0.08 0.13
## PP.Nat_1_VB 0.85 0.84 0.86
## PP.Nat_4R_VB 0.23 0.17 0.18
## PP.Nat_2R_VB 0.01 -0.05 -0.06
## PP.Nat_3R_VB -0.22 -0.26 -0.29
## PP.BehavInt4_PBPB PP.BehavInt1_PBFB PP.BehavInt2_PBFB
## PP.Risk_Score_GFFB 0.63 0.75 0.74
## PP.Risk_Score_GFPRB 0.54 0.72 0.73
## PP.Risk_Score_CBB -0.10 -0.07 -0.07
## PP.Risk_Score_PBFB -0.61 -0.49 -0.45
## PP.Risk_Score_PBPB -0.49 -0.29 -0.24
## PP.Risk_Score_VB -0.07 0.18 0.23
## PP.Ben_Score_GFFB 0.05 0.17 0.23
## PP.Ben_Score_GFPRB -0.10 -0.12 -0.09
## PP.Ben_Score_CBB 0.60 0.76 0.79
## PP.Ben_Score_PBFB 0.94 0.99 0.98
## PP.Ben_Score_PBPB 0.99 0.94 0.91
## PP.Ben_Score_VB 0.88 0.78 0.75
## PP.BehavInt1_GFFB -0.13 -0.04 0.01
## PP.BehavInt2_GFFB -0.15 -0.08 -0.04
## PP.BehavInt3_GFFB -0.10 -0.01 0.05
## PP.BehavInt4_GFFB -0.15 -0.07 -0.01
## PP.BehavInt1_GFPRB 0.99 0.94 0.91
## PP.BehavInt2_GFPRB 0.98 0.95 0.94
## PP.BehavInt3_GFPRB 0.99 0.95 0.92
## PP.BehavInt4_GFPRB 1.00 0.94 0.91
## PP.BehavInt1_CBB 0.66 0.79 0.82
## PP.BehavInt2_CBB 0.61 0.76 0.79
## PP.BehavInt3_CBB 0.63 0.78 0.81
## PP.BehavInt4_CBB 0.63 0.77 0.80
## PP.BehavInt1_PBPB 0.99 0.94 0.91
## PP.BehavInt2_PBPB 0.98 0.95 0.94
## PP.BehavInt3_PBPB 0.99 0.95 0.92
## PP.BehavInt4_PBPB 1.00 0.94 0.91
## PP.BehavInt1_PBFB 0.94 1.00 0.99
## PP.BehavInt2_PBFB 0.91 0.99 1.00
## PP.BehavInt3_PBFB 0.94 0.99 0.98
## PP.BehavInt4_PBFB 0.94 0.99 0.98
## PP.BehavInt1_VB 0.93 0.84 0.81
## PP.BehavInt2_VB 0.92 0.86 0.84
## PP.BehavInt3_VB 0.93 0.83 0.80
## PP.BehavInt4_VB 0.93 0.84 0.81
## PP.Nat_1_GFFB -0.15 -0.06 0.00
## PP.Nat_4R_GFFB -0.66 -0.79 -0.79
## PP.Nat_2R_GFFB -0.76 -0.83 -0.80
## PP.Nat_3R_GFFB -0.56 -0.71 -0.74
## PP.Nat_1_GFPRB -0.06 -0.23 -0.23
## PP.Nat_4R_GFPRB -0.35 -0.56 -0.60
## PP.Nat_2R_GFPRB -0.41 -0.62 -0.66
## PP.Nat_3R_GFPRB -0.52 -0.70 -0.74
## PP.Nat_1_CBB 0.47 0.64 0.69
## PP.Nat_4R_CBB 0.02 0.08 0.11
## PP.Nat_2R_CBB -0.27 -0.13 -0.07
## PP.Nat_3R_CBB -0.39 -0.26 -0.20
## PP.Nat_1_PBPB 0.93 0.93 0.93
## PP.Nat_4R_PBPB 0.35 0.21 0.19
## PP.Nat_2R_PBPB 0.22 0.12 0.09
## PP.Nat_3R_PBPB -0.34 -0.34 -0.32
## PP.Nat_1_PBFB 0.85 0.95 0.96
## PP.Nat_4R_PBFB -0.51 -0.49 -0.48
## PP.Nat_2R_PBFB -0.08 -0.09 -0.08
## PP.Nat_3R_PBFB 0.16 0.10 0.09
## PP.Nat_1_VB 0.87 0.77 0.74
## PP.Nat_4R_VB 0.20 -0.02 -0.06
## PP.Nat_2R_VB -0.04 -0.21 -0.26
## PP.Nat_3R_VB -0.26 -0.37 -0.42
## PP.BehavInt3_PBFB PP.BehavInt4_PBFB PP.BehavInt1_VB
## PP.Risk_Score_GFFB 0.72 0.72 0.52
## PP.Risk_Score_GFPRB 0.69 0.68 0.45
## PP.Risk_Score_CBB -0.07 -0.09 0.05
## PP.Risk_Score_PBFB -0.48 -0.49 -0.60
## PP.Risk_Score_PBPB -0.29 -0.30 -0.55
## PP.Risk_Score_VB 0.18 0.18 -0.19
## PP.Ben_Score_GFFB 0.23 0.23 0.00
## PP.Ben_Score_GFPRB -0.05 -0.06 -0.01
## PP.Ben_Score_CBB 0.77 0.78 0.41
## PP.Ben_Score_PBFB 0.99 0.99 0.84
## PP.Ben_Score_PBPB 0.94 0.94 0.93
## PP.Ben_Score_VB 0.79 0.78 0.98
## PP.BehavInt1_GFFB 0.01 0.01 -0.16
## PP.BehavInt2_GFFB -0.03 -0.03 -0.15
## PP.BehavInt3_GFFB 0.04 0.04 -0.14
## PP.BehavInt4_GFFB -0.01 -0.01 -0.17
## PP.BehavInt1_GFPRB 0.93 0.93 0.92
## PP.BehavInt2_GFPRB 0.94 0.94 0.91
## PP.BehavInt3_GFPRB 0.94 0.95 0.92
## PP.BehavInt4_GFPRB 0.94 0.94 0.93
## PP.BehavInt1_CBB 0.80 0.81 0.45
## PP.BehavInt2_CBB 0.76 0.77 0.40
## PP.BehavInt3_CBB 0.79 0.79 0.43
## PP.BehavInt4_CBB 0.78 0.79 0.43
## PP.BehavInt1_PBPB 0.93 0.93 0.92
## PP.BehavInt2_PBPB 0.94 0.94 0.91
## PP.BehavInt3_PBPB 0.94 0.95 0.92
## PP.BehavInt4_PBPB 0.94 0.94 0.93
## PP.BehavInt1_PBFB 0.99 0.99 0.84
## PP.BehavInt2_PBFB 0.98 0.98 0.81
## PP.BehavInt3_PBFB 1.00 1.00 0.85
## PP.BehavInt4_PBFB 1.00 1.00 0.84
## PP.BehavInt1_VB 0.85 0.84 1.00
## PP.BehavInt2_VB 0.86 0.85 0.97
## PP.BehavInt3_VB 0.84 0.84 0.99
## PP.BehavInt4_VB 0.85 0.84 0.99
## PP.Nat_1_GFFB -0.01 0.00 -0.18
## PP.Nat_4R_GFFB -0.78 -0.77 -0.54
## PP.Nat_2R_GFFB -0.81 -0.80 -0.63
## PP.Nat_3R_GFFB -0.72 -0.72 -0.42
## PP.Nat_1_GFPRB -0.17 -0.16 0.03
## PP.Nat_4R_GFPRB -0.54 -0.54 -0.26
## PP.Nat_2R_GFPRB -0.61 -0.61 -0.35
## PP.Nat_3R_GFPRB -0.70 -0.71 -0.42
## PP.Nat_1_CBB 0.65 0.66 0.27
## PP.Nat_4R_CBB 0.07 0.09 -0.15
## PP.Nat_2R_CBB -0.14 -0.12 -0.44
## PP.Nat_3R_CBB -0.28 -0.25 -0.51
## PP.Nat_1_PBPB 0.93 0.93 0.86
## PP.Nat_4R_PBPB 0.17 0.19 0.40
## PP.Nat_2R_PBPB 0.08 0.10 0.21
## PP.Nat_3R_PBPB -0.38 -0.37 -0.30
## PP.Nat_1_PBFB 0.95 0.96 0.73
## PP.Nat_4R_PBFB -0.47 -0.48 -0.49
## PP.Nat_2R_PBFB -0.06 -0.08 -0.04
## PP.Nat_3R_PBFB 0.14 0.13 0.18
## PP.Nat_1_VB 0.78 0.78 0.93
## PP.Nat_4R_VB -0.02 -0.02 0.33
## PP.Nat_2R_VB -0.22 -0.22 0.10
## PP.Nat_3R_VB -0.40 -0.40 -0.13
## PP.BehavInt2_VB PP.BehavInt3_VB PP.BehavInt4_VB
## PP.Risk_Score_GFFB 0.58 0.49 0.52
## PP.Risk_Score_GFPRB 0.55 0.41 0.45
## PP.Risk_Score_CBB 0.09 0.05 0.06
## PP.Risk_Score_PBFB -0.56 -0.63 -0.59
## PP.Risk_Score_PBPB -0.49 -0.58 -0.55
## PP.Risk_Score_VB -0.11 -0.23 -0.19
## PP.Ben_Score_GFFB 0.01 0.00 0.00
## PP.Ben_Score_GFPRB -0.07 0.00 -0.01
## PP.Ben_Score_CBB 0.43 0.40 0.41
## PP.Ben_Score_PBFB 0.85 0.84 0.85
## PP.Ben_Score_PBPB 0.92 0.93 0.93
## PP.Ben_Score_VB 0.93 0.98 0.98
## PP.BehavInt1_GFFB -0.17 -0.15 -0.16
## PP.BehavInt2_GFFB -0.19 -0.14 -0.16
## PP.BehavInt3_GFFB -0.14 -0.13 -0.14
## PP.BehavInt4_GFFB -0.19 -0.17 -0.18
## PP.BehavInt1_GFPRB 0.91 0.92 0.92
## PP.BehavInt2_GFPRB 0.91 0.89 0.91
## PP.BehavInt3_GFPRB 0.91 0.92 0.92
## PP.BehavInt4_GFPRB 0.92 0.93 0.93
## PP.BehavInt1_CBB 0.47 0.43 0.45
## PP.BehavInt2_CBB 0.43 0.38 0.40
## PP.BehavInt3_CBB 0.45 0.41 0.43
## PP.BehavInt4_CBB 0.44 0.41 0.43
## PP.BehavInt1_PBPB 0.91 0.92 0.92
## PP.BehavInt2_PBPB 0.91 0.89 0.91
## PP.BehavInt3_PBPB 0.91 0.92 0.92
## PP.BehavInt4_PBPB 0.92 0.93 0.93
## PP.BehavInt1_PBFB 0.86 0.83 0.84
## PP.BehavInt2_PBFB 0.84 0.80 0.81
## PP.BehavInt3_PBFB 0.86 0.84 0.85
## PP.BehavInt4_PBFB 0.85 0.84 0.84
## PP.BehavInt1_VB 0.97 0.99 0.99
## PP.BehavInt2_VB 1.00 0.95 0.97
## PP.BehavInt3_VB 0.95 1.00 0.99
## PP.BehavInt4_VB 0.97 0.99 1.00
## PP.Nat_1_GFFB -0.20 -0.18 -0.18
## PP.Nat_4R_GFFB -0.61 -0.53 -0.56
## PP.Nat_2R_GFFB -0.66 -0.63 -0.64
## PP.Nat_3R_GFFB -0.47 -0.40 -0.42
## PP.Nat_1_GFPRB -0.08 0.07 0.03
## PP.Nat_4R_GFPRB -0.37 -0.22 -0.27
## PP.Nat_2R_GFPRB -0.47 -0.32 -0.36
## PP.Nat_3R_GFPRB -0.51 -0.38 -0.42
## PP.Nat_1_CBB 0.32 0.25 0.27
## PP.Nat_4R_CBB -0.13 -0.17 -0.17
## PP.Nat_2R_CBB -0.38 -0.47 -0.45
## PP.Nat_3R_CBB -0.46 -0.55 -0.52
## PP.Nat_1_PBPB 0.88 0.85 0.86
## PP.Nat_4R_PBPB 0.43 0.40 0.39
## PP.Nat_2R_PBPB 0.26 0.22 0.22
## PP.Nat_3R_PBPB -0.18 -0.29 -0.28
## PP.Nat_1_PBFB 0.76 0.72 0.72
## PP.Nat_4R_PBFB -0.48 -0.48 -0.47
## PP.Nat_2R_PBFB -0.09 -0.05 -0.03
## PP.Nat_3R_PBFB 0.11 0.17 0.17
## PP.Nat_1_VB 0.91 0.93 0.93
## PP.Nat_4R_VB 0.29 0.35 0.32
## PP.Nat_2R_VB 0.05 0.13 0.09
## PP.Nat_3R_VB -0.15 -0.10 -0.13
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Risk_Score_GFFB -0.16 -0.94 -0.90 -0.77
## PP.Risk_Score_GFPRB -0.03 -0.90 -0.80 -0.80
## PP.Risk_Score_CBB 0.27 -0.20 -0.13 -0.26
## PP.Risk_Score_PBFB 0.45 0.03 0.14 -0.14
## PP.Risk_Score_PBPB 0.51 -0.15 -0.01 -0.32
## PP.Risk_Score_VB 0.52 -0.54 -0.39 -0.69
## PP.Ben_Score_GFFB 0.89 -0.20 -0.15 -0.56
## PP.Ben_Score_GFPRB 0.77 0.07 0.05 -0.24
## PP.Ben_Score_CBB 0.41 -0.73 -0.72 -0.84
## PP.Ben_Score_PBFB 0.02 -0.75 -0.80 -0.71
## PP.Ben_Score_PBPB -0.12 -0.66 -0.76 -0.58
## PP.Ben_Score_VB -0.12 -0.49 -0.59 -0.38
## PP.BehavInt1_GFFB 0.92 0.02 0.06 -0.38
## PP.BehavInt2_GFFB 0.90 0.10 0.12 -0.29
## PP.BehavInt3_GFFB 0.92 -0.03 0.01 -0.41
## PP.BehavInt4_GFFB 0.92 0.04 0.08 -0.35
## PP.BehavInt1_GFPRB -0.17 -0.63 -0.73 -0.52
## PP.BehavInt2_GFPRB -0.17 -0.69 -0.77 -0.57
## PP.BehavInt3_GFPRB -0.15 -0.66 -0.76 -0.56
## PP.BehavInt4_GFPRB -0.15 -0.66 -0.76 -0.56
## PP.BehavInt1_CBB 0.34 -0.71 -0.71 -0.79
## PP.BehavInt2_CBB 0.40 -0.70 -0.69 -0.81
## PP.BehavInt3_CBB 0.36 -0.72 -0.71 -0.81
## PP.BehavInt4_CBB 0.36 -0.71 -0.71 -0.81
## PP.BehavInt1_PBPB -0.17 -0.63 -0.73 -0.52
## PP.BehavInt2_PBPB -0.17 -0.69 -0.77 -0.57
## PP.BehavInt3_PBPB -0.15 -0.66 -0.76 -0.56
## PP.BehavInt4_PBPB -0.15 -0.66 -0.76 -0.56
## PP.BehavInt1_PBFB -0.06 -0.79 -0.83 -0.71
## PP.BehavInt2_PBFB 0.00 -0.79 -0.80 -0.74
## PP.BehavInt3_PBFB -0.01 -0.78 -0.81 -0.72
## PP.BehavInt4_PBFB 0.00 -0.77 -0.80 -0.72
## PP.BehavInt1_VB -0.18 -0.54 -0.63 -0.42
## PP.BehavInt2_VB -0.20 -0.61 -0.66 -0.47
## PP.BehavInt3_VB -0.18 -0.53 -0.63 -0.40
## PP.BehavInt4_VB -0.18 -0.56 -0.64 -0.42
## PP.Nat_1_GFFB 1.00 0.05 0.07 -0.36
## PP.Nat_4R_GFFB 0.05 1.00 0.93 0.85
## PP.Nat_2R_GFFB 0.07 0.93 1.00 0.80
## PP.Nat_3R_GFFB -0.36 0.85 0.80 1.00
## PP.Nat_1_GFPRB 0.45 0.41 0.28 0.19
## PP.Nat_4R_GFPRB -0.18 0.82 0.66 0.79
## PP.Nat_2R_GFPRB -0.13 0.84 0.69 0.79
## PP.Nat_3R_GFPRB -0.24 0.83 0.71 0.88
## PP.Nat_1_CBB 0.45 -0.72 -0.67 -0.85
## PP.Nat_4R_CBB -0.24 0.07 0.08 0.10
## PP.Nat_2R_CBB -0.08 0.11 0.23 0.09
## PP.Nat_3R_CBB -0.10 0.16 0.30 0.14
## PP.Nat_1_PBPB -0.04 -0.72 -0.77 -0.68
## PP.Nat_4R_PBPB -0.65 0.06 0.03 0.27
## PP.Nat_2R_PBPB -0.74 0.03 0.03 0.31
## PP.Nat_3R_PBPB -0.54 0.19 0.32 0.40
## PP.Nat_1_PBFB 0.07 -0.80 -0.81 -0.78
## PP.Nat_4R_PBFB 0.53 0.19 0.23 -0.03
## PP.Nat_2R_PBFB 0.58 -0.05 -0.12 -0.28
## PP.Nat_3R_PBFB 0.54 -0.06 -0.17 -0.31
## PP.Nat_1_VB -0.15 -0.51 -0.57 -0.39
## PP.Nat_4R_VB -0.63 0.34 0.24 0.54
## PP.Nat_2R_VB -0.66 0.42 0.35 0.65
## PP.Nat_3R_VB -0.64 0.41 0.40 0.65
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Risk_Score_GFFB -0.49 -0.81 -0.82
## PP.Risk_Score_GFPRB -0.59 -0.93 -0.92
## PP.Risk_Score_CBB 0.02 -0.35 -0.33
## PP.Risk_Score_PBFB -0.12 -0.32 -0.20
## PP.Risk_Score_PBPB -0.27 -0.51 -0.39
## PP.Risk_Score_VB -0.36 -0.83 -0.74
## PP.Ben_Score_GFFB 0.23 -0.41 -0.37
## PP.Ben_Score_GFPRB 0.63 0.00 0.01
## PP.Ben_Score_CBB -0.20 -0.72 -0.70
## PP.Ben_Score_PBFB -0.14 -0.53 -0.59
## PP.Ben_Score_PBPB -0.06 -0.37 -0.43
## PP.Ben_Score_VB 0.11 -0.21 -0.31
## PP.BehavInt1_GFFB 0.36 -0.22 -0.16
## PP.BehavInt2_GFFB 0.45 -0.11 -0.06
## PP.BehavInt3_GFFB 0.33 -0.26 -0.20
## PP.BehavInt4_GFFB 0.37 -0.20 -0.15
## PP.BehavInt1_GFPRB -0.05 -0.31 -0.38
## PP.BehavInt2_GFPRB -0.14 -0.40 -0.46
## PP.BehavInt3_GFPRB -0.08 -0.37 -0.44
## PP.BehavInt4_GFPRB -0.06 -0.35 -0.41
## PP.BehavInt1_CBB -0.20 -0.66 -0.64
## PP.BehavInt2_CBB -0.21 -0.70 -0.67
## PP.BehavInt3_CBB -0.22 -0.69 -0.66
## PP.BehavInt4_CBB -0.21 -0.68 -0.65
## PP.BehavInt1_PBPB -0.05 -0.31 -0.38
## PP.BehavInt2_PBPB -0.14 -0.40 -0.46
## PP.BehavInt3_PBPB -0.08 -0.37 -0.44
## PP.BehavInt4_PBPB -0.06 -0.35 -0.41
## PP.BehavInt1_PBFB -0.23 -0.56 -0.62
## PP.BehavInt2_PBFB -0.23 -0.60 -0.66
## PP.BehavInt3_PBFB -0.17 -0.54 -0.61
## PP.BehavInt4_PBFB -0.16 -0.54 -0.61
## PP.BehavInt1_VB 0.03 -0.26 -0.35
## PP.BehavInt2_VB -0.08 -0.37 -0.47
## PP.BehavInt3_VB 0.07 -0.22 -0.32
## PP.BehavInt4_VB 0.03 -0.27 -0.36
## PP.Nat_1_GFFB 0.45 -0.18 -0.13
## PP.Nat_4R_GFFB 0.41 0.82 0.84
## PP.Nat_2R_GFFB 0.28 0.66 0.69
## PP.Nat_3R_GFFB 0.19 0.79 0.79
## PP.Nat_1_GFPRB 1.00 0.57 0.53
## PP.Nat_4R_GFPRB 0.57 1.00 0.95
## PP.Nat_2R_GFPRB 0.53 0.95 1.00
## PP.Nat_3R_GFPRB 0.38 0.89 0.90
## PP.Nat_1_CBB -0.28 -0.77 -0.74
## PP.Nat_4R_CBB -0.29 0.07 0.03
## PP.Nat_2R_CBB -0.39 -0.07 -0.06
## PP.Nat_3R_CBB -0.39 -0.05 -0.04
## PP.Nat_1_PBPB -0.19 -0.52 -0.60
## PP.Nat_4R_PBPB -0.08 0.31 0.18
## PP.Nat_2R_PBPB -0.19 0.26 0.15
## PP.Nat_3R_PBPB -0.45 0.12 0.06
## PP.Nat_1_PBFB -0.26 -0.65 -0.70
## PP.Nat_4R_PBFB 0.36 0.01 0.11
## PP.Nat_2R_PBFB 0.37 -0.16 -0.06
## PP.Nat_3R_PBFB 0.51 -0.05 0.00
## PP.Nat_1_VB 0.04 -0.25 -0.34
## PP.Nat_4R_VB 0.12 0.60 0.49
## PP.Nat_2R_VB 0.14 0.70 0.61
## PP.Nat_3R_VB 0.02 0.59 0.54
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Risk_Score_GFFB -0.79 0.64 -0.02 -0.10
## PP.Risk_Score_GFPRB -0.87 0.71 -0.07 -0.03
## PP.Risk_Score_CBB -0.19 0.01 -0.82 -0.60
## PP.Risk_Score_PBFB -0.09 0.15 -0.36 -0.01
## PP.Risk_Score_PBPB -0.29 0.38 -0.21 0.14
## PP.Risk_Score_VB -0.71 0.73 -0.08 0.18
## PP.Ben_Score_GFFB -0.46 0.64 -0.31 -0.13
## PP.Ben_Score_GFPRB -0.09 0.21 -0.41 -0.34
## PP.Ben_Score_CBB -0.84 0.94 0.15 0.10
## PP.Ben_Score_PBFB -0.69 0.66 0.08 -0.13
## PP.Ben_Score_PBPB -0.55 0.49 0.02 -0.27
## PP.Ben_Score_VB -0.36 0.22 -0.27 -0.55
## PP.BehavInt1_GFFB -0.26 0.46 -0.35 -0.16
## PP.BehavInt2_GFFB -0.18 0.37 -0.37 -0.21
## PP.BehavInt3_GFFB -0.30 0.50 -0.35 -0.15
## PP.BehavInt4_GFFB -0.23 0.44 -0.35 -0.15
## PP.BehavInt1_GFPRB -0.50 0.45 0.07 -0.23
## PP.BehavInt2_GFPRB -0.56 0.51 0.09 -0.19
## PP.BehavInt3_GFPRB -0.54 0.49 0.07 -0.22
## PP.BehavInt4_GFPRB -0.52 0.47 0.02 -0.27
## PP.BehavInt1_CBB -0.79 0.92 0.23 0.13
## PP.BehavInt2_CBB -0.81 0.95 0.22 0.16
## PP.BehavInt3_CBB -0.81 0.93 0.22 0.14
## PP.BehavInt4_CBB -0.80 0.93 0.23 0.14
## PP.BehavInt1_PBPB -0.50 0.45 0.07 -0.23
## PP.BehavInt2_PBPB -0.56 0.51 0.09 -0.19
## PP.BehavInt3_PBPB -0.54 0.49 0.07 -0.22
## PP.BehavInt4_PBPB -0.52 0.47 0.02 -0.27
## PP.BehavInt1_PBFB -0.70 0.64 0.08 -0.13
## PP.BehavInt2_PBFB -0.74 0.69 0.11 -0.07
## PP.BehavInt3_PBFB -0.70 0.65 0.07 -0.14
## PP.BehavInt4_PBFB -0.71 0.66 0.09 -0.12
## PP.BehavInt1_VB -0.42 0.27 -0.15 -0.44
## PP.BehavInt2_VB -0.51 0.32 -0.13 -0.38
## PP.BehavInt3_VB -0.38 0.25 -0.17 -0.47
## PP.BehavInt4_VB -0.42 0.27 -0.17 -0.45
## PP.Nat_1_GFFB -0.24 0.45 -0.24 -0.08
## PP.Nat_4R_GFFB 0.83 -0.72 0.07 0.11
## PP.Nat_2R_GFFB 0.71 -0.67 0.08 0.23
## PP.Nat_3R_GFFB 0.88 -0.85 0.10 0.09
## PP.Nat_1_GFPRB 0.38 -0.28 -0.29 -0.39
## PP.Nat_4R_GFPRB 0.89 -0.77 0.07 -0.07
## PP.Nat_2R_GFPRB 0.90 -0.74 0.03 -0.06
## PP.Nat_3R_GFPRB 1.00 -0.84 -0.03 -0.06
## PP.Nat_1_CBB -0.84 1.00 0.18 0.19
## PP.Nat_4R_CBB -0.03 0.18 1.00 0.85
## PP.Nat_2R_CBB -0.06 0.19 0.85 1.00
## PP.Nat_3R_CBB 0.04 0.07 0.73 0.91
## PP.Nat_1_PBPB -0.68 0.62 0.07 -0.15
## PP.Nat_4R_PBPB 0.14 -0.29 0.36 0.10
## PP.Nat_2R_PBPB 0.17 -0.33 0.47 0.33
## PP.Nat_3R_PBPB 0.25 -0.37 0.24 0.38
## PP.Nat_1_PBFB -0.78 0.78 0.16 0.00
## PP.Nat_4R_PBFB 0.12 -0.12 -0.56 -0.33
## PP.Nat_2R_PBFB -0.12 0.14 -0.65 -0.58
## PP.Nat_3R_PBFB -0.12 0.14 -0.49 -0.56
## PP.Nat_1_VB -0.41 0.28 -0.17 -0.42
## PP.Nat_4R_VB 0.50 -0.61 0.10 -0.15
## PP.Nat_2R_VB 0.63 -0.74 0.07 -0.10
## PP.Nat_3R_VB 0.63 -0.74 0.01 -0.06
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Risk_Score_GFFB -0.16 0.67 -0.06 -0.02
## PP.Risk_Score_GFPRB -0.08 0.66 -0.12 -0.10
## PP.Risk_Score_CBB -0.44 -0.06 -0.51 -0.54
## PP.Risk_Score_PBFB 0.12 -0.47 -0.76 -0.67
## PP.Risk_Score_PBPB 0.24 -0.33 -0.79 -0.68
## PP.Risk_Score_VB 0.18 0.12 -0.65 -0.58
## PP.Ben_Score_GFFB -0.16 0.18 -0.60 -0.73
## PP.Ben_Score_GFPRB -0.31 -0.06 -0.44 -0.60
## PP.Ben_Score_CBB -0.06 0.70 -0.23 -0.28
## PP.Ben_Score_PBFB -0.27 0.94 0.19 0.09
## PP.Ben_Score_PBPB -0.40 0.94 0.34 0.20
## PP.Ben_Score_VB -0.62 0.82 0.34 0.12
## PP.BehavInt1_GFFB -0.16 -0.03 -0.64 -0.78
## PP.BehavInt2_GFFB -0.23 -0.08 -0.60 -0.77
## PP.BehavInt3_GFFB -0.16 0.01 -0.63 -0.77
## PP.BehavInt4_GFFB -0.15 -0.05 -0.63 -0.77
## PP.BehavInt1_GFPRB -0.37 0.92 0.40 0.27
## PP.BehavInt2_GFPRB -0.32 0.93 0.37 0.25
## PP.BehavInt3_GFPRB -0.34 0.94 0.36 0.24
## PP.BehavInt4_GFPRB -0.39 0.93 0.35 0.22
## PP.BehavInt1_CBB -0.04 0.72 -0.15 -0.21
## PP.BehavInt2_CBB -0.01 0.70 -0.19 -0.25
## PP.BehavInt3_CBB -0.02 0.71 -0.17 -0.22
## PP.BehavInt4_CBB -0.02 0.72 -0.16 -0.22
## PP.BehavInt1_PBPB -0.37 0.92 0.40 0.27
## PP.BehavInt2_PBPB -0.32 0.93 0.37 0.25
## PP.BehavInt3_PBPB -0.34 0.94 0.36 0.24
## PP.BehavInt4_PBPB -0.39 0.93 0.35 0.22
## PP.BehavInt1_PBFB -0.26 0.93 0.21 0.12
## PP.BehavInt2_PBFB -0.20 0.93 0.19 0.09
## PP.BehavInt3_PBFB -0.28 0.93 0.17 0.08
## PP.BehavInt4_PBFB -0.25 0.93 0.19 0.10
## PP.BehavInt1_VB -0.51 0.86 0.40 0.21
## PP.BehavInt2_VB -0.46 0.88 0.43 0.26
## PP.BehavInt3_VB -0.55 0.85 0.40 0.22
## PP.BehavInt4_VB -0.52 0.86 0.39 0.22
## PP.Nat_1_GFFB -0.10 -0.04 -0.65 -0.74
## PP.Nat_4R_GFFB 0.16 -0.72 0.06 0.03
## PP.Nat_2R_GFFB 0.30 -0.77 0.03 0.03
## PP.Nat_3R_GFFB 0.14 -0.68 0.27 0.31
## PP.Nat_1_GFPRB -0.39 -0.19 -0.08 -0.19
## PP.Nat_4R_GFPRB -0.05 -0.52 0.31 0.26
## PP.Nat_2R_GFPRB -0.04 -0.60 0.18 0.15
## PP.Nat_3R_GFPRB 0.04 -0.68 0.14 0.17
## PP.Nat_1_CBB 0.07 0.62 -0.29 -0.33
## PP.Nat_4R_CBB 0.73 0.07 0.36 0.47
## PP.Nat_2R_CBB 0.91 -0.15 0.10 0.33
## PP.Nat_3R_CBB 1.00 -0.27 0.01 0.28
## PP.Nat_1_PBPB -0.27 1.00 0.33 0.19
## PP.Nat_4R_PBPB 0.01 0.33 1.00 0.85
## PP.Nat_2R_PBPB 0.28 0.19 0.85 1.00
## PP.Nat_3R_PBPB 0.44 -0.25 0.49 0.63
## PP.Nat_1_PBFB -0.12 0.93 0.13 0.06
## PP.Nat_4R_PBFB -0.22 -0.54 -0.71 -0.67
## PP.Nat_2R_PBFB -0.54 -0.09 -0.63 -0.78
## PP.Nat_3R_PBFB -0.54 0.09 -0.45 -0.62
## PP.Nat_1_VB -0.50 0.84 0.39 0.20
## PP.Nat_4R_VB -0.16 0.08 0.79 0.69
## PP.Nat_2R_VB -0.09 -0.18 0.63 0.63
## PP.Nat_3R_VB -0.01 -0.39 0.47 0.51
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Risk_Score_GFFB -0.21 0.73 -0.19 0.07
## PP.Risk_Score_GFPRB -0.14 0.75 -0.16 0.10
## PP.Risk_Score_CBB -0.18 -0.10 0.57 0.59
## PP.Risk_Score_PBFB -0.04 -0.38 0.73 0.51
## PP.Risk_Score_PBPB -0.06 -0.16 0.60 0.41
## PP.Risk_Score_VB -0.22 0.33 0.36 0.39
## PP.Ben_Score_GFFB -0.52 0.32 0.41 0.52
## PP.Ben_Score_GFPRB -0.45 -0.04 0.50 0.52
## PP.Ben_Score_CBB -0.48 0.85 -0.16 0.15
## PP.Ben_Score_PBFB -0.39 0.96 -0.48 -0.06
## PP.Ben_Score_PBPB -0.36 0.87 -0.50 -0.05
## PP.Ben_Score_VB -0.35 0.67 -0.40 0.06
## PP.BehavInt1_GFFB -0.51 0.09 0.53 0.57
## PP.BehavInt2_GFFB -0.54 0.03 0.54 0.59
## PP.BehavInt3_GFFB -0.50 0.12 0.52 0.56
## PP.BehavInt4_GFFB -0.48 0.07 0.52 0.55
## PP.BehavInt1_GFPRB -0.31 0.85 -0.57 -0.14
## PP.BehavInt2_GFPRB -0.29 0.87 -0.56 -0.12
## PP.BehavInt3_GFPRB -0.33 0.87 -0.54 -0.10
## PP.BehavInt4_GFPRB -0.34 0.85 -0.51 -0.08
## PP.BehavInt1_CBB -0.48 0.87 -0.24 0.07
## PP.BehavInt2_CBB -0.46 0.85 -0.21 0.10
## PP.BehavInt3_CBB -0.47 0.86 -0.22 0.09
## PP.BehavInt4_CBB -0.47 0.85 -0.23 0.08
## PP.BehavInt1_PBPB -0.31 0.85 -0.57 -0.14
## PP.BehavInt2_PBPB -0.29 0.87 -0.56 -0.12
## PP.BehavInt3_PBPB -0.33 0.87 -0.54 -0.10
## PP.BehavInt4_PBPB -0.34 0.85 -0.51 -0.08
## PP.BehavInt1_PBFB -0.34 0.95 -0.49 -0.09
## PP.BehavInt2_PBFB -0.32 0.96 -0.48 -0.08
## PP.BehavInt3_PBFB -0.38 0.95 -0.47 -0.06
## PP.BehavInt4_PBFB -0.37 0.96 -0.48 -0.08
## PP.BehavInt1_VB -0.30 0.73 -0.49 -0.04
## PP.BehavInt2_VB -0.18 0.76 -0.48 -0.09
## PP.BehavInt3_VB -0.29 0.72 -0.48 -0.05
## PP.BehavInt4_VB -0.28 0.72 -0.47 -0.03
## PP.Nat_1_GFFB -0.54 0.07 0.53 0.58
## PP.Nat_4R_GFFB 0.19 -0.80 0.19 -0.05
## PP.Nat_2R_GFFB 0.32 -0.81 0.23 -0.12
## PP.Nat_3R_GFFB 0.40 -0.78 -0.03 -0.28
## PP.Nat_1_GFPRB -0.45 -0.26 0.36 0.37
## PP.Nat_4R_GFPRB 0.12 -0.65 0.01 -0.16
## PP.Nat_2R_GFPRB 0.06 -0.70 0.11 -0.06
## PP.Nat_3R_GFPRB 0.25 -0.78 0.12 -0.12
## PP.Nat_1_CBB -0.37 0.78 -0.12 0.14
## PP.Nat_4R_CBB 0.24 0.16 -0.56 -0.65
## PP.Nat_2R_CBB 0.38 0.00 -0.33 -0.58
## PP.Nat_3R_CBB 0.44 -0.12 -0.22 -0.54
## PP.Nat_1_PBPB -0.25 0.93 -0.54 -0.09
## PP.Nat_4R_PBPB 0.49 0.13 -0.71 -0.63
## PP.Nat_2R_PBPB 0.63 0.06 -0.67 -0.78
## PP.Nat_3R_PBPB 1.00 -0.31 -0.33 -0.66
## PP.Nat_1_PBFB -0.31 1.00 -0.49 -0.10
## PP.Nat_4R_PBFB -0.33 -0.49 1.00 0.78
## PP.Nat_2R_PBFB -0.66 -0.10 0.78 1.00
## PP.Nat_3R_PBFB -0.80 0.06 0.62 0.84
## PP.Nat_1_VB -0.26 0.72 -0.49 -0.05
## PP.Nat_4R_VB 0.40 -0.14 -0.56 -0.54
## PP.Nat_2R_VB 0.43 -0.33 -0.40 -0.52
## PP.Nat_3R_VB 0.62 -0.49 -0.27 -0.49
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Risk_Score_GFFB 0.08 0.46 -0.32 -0.40
## PP.Risk_Score_GFPRB 0.06 0.41 -0.43 -0.53
## PP.Risk_Score_CBB 0.40 0.07 -0.32 -0.30
## PP.Risk_Score_PBFB 0.20 -0.56 -0.68 -0.56
## PP.Risk_Score_PBPB 0.12 -0.52 -0.81 -0.70
## PP.Risk_Score_VB 0.18 -0.19 -0.89 -0.88
## PP.Ben_Score_GFFB 0.48 0.04 -0.63 -0.71
## PP.Ben_Score_GFPRB 0.51 0.02 -0.35 -0.39
## PP.Ben_Score_CBB 0.21 0.38 -0.54 -0.67
## PP.Ben_Score_PBFB 0.15 0.78 -0.03 -0.23
## PP.Ben_Score_PBPB 0.18 0.88 0.17 -0.06
## PP.Ben_Score_VB 0.26 0.93 0.33 0.11
## PP.BehavInt1_GFFB 0.51 -0.10 -0.60 -0.63
## PP.BehavInt2_GFFB 0.54 -0.10 -0.54 -0.54
## PP.BehavInt3_GFFB 0.49 -0.08 -0.62 -0.66
## PP.BehavInt4_GFFB 0.49 -0.12 -0.59 -0.62
## PP.BehavInt1_GFPRB 0.10 0.85 0.23 0.01
## PP.BehavInt2_GFPRB 0.08 0.84 0.17 -0.05
## PP.BehavInt3_GFPRB 0.13 0.86 0.18 -0.06
## PP.BehavInt4_GFPRB 0.16 0.87 0.20 -0.04
## PP.BehavInt1_CBB 0.16 0.42 -0.46 -0.59
## PP.BehavInt2_CBB 0.16 0.38 -0.51 -0.65
## PP.BehavInt3_CBB 0.16 0.39 -0.50 -0.63
## PP.BehavInt4_CBB 0.16 0.39 -0.48 -0.63
## PP.BehavInt1_PBPB 0.10 0.85 0.23 0.01
## PP.BehavInt2_PBPB 0.08 0.84 0.17 -0.05
## PP.BehavInt3_PBPB 0.13 0.86 0.18 -0.06
## PP.BehavInt4_PBPB 0.16 0.87 0.20 -0.04
## PP.BehavInt1_PBFB 0.10 0.77 -0.02 -0.21
## PP.BehavInt2_PBFB 0.09 0.74 -0.06 -0.26
## PP.BehavInt3_PBFB 0.14 0.78 -0.02 -0.22
## PP.BehavInt4_PBFB 0.13 0.78 -0.02 -0.22
## PP.BehavInt1_VB 0.18 0.93 0.33 0.10
## PP.BehavInt2_VB 0.11 0.91 0.29 0.05
## PP.BehavInt3_VB 0.17 0.93 0.35 0.13
## PP.BehavInt4_VB 0.17 0.93 0.32 0.09
## PP.Nat_1_GFFB 0.54 -0.15 -0.63 -0.66
## PP.Nat_4R_GFFB -0.06 -0.51 0.34 0.42
## PP.Nat_2R_GFFB -0.17 -0.57 0.24 0.35
## PP.Nat_3R_GFFB -0.31 -0.39 0.54 0.65
## PP.Nat_1_GFPRB 0.51 0.04 0.12 0.14
## PP.Nat_4R_GFPRB -0.05 -0.25 0.60 0.70
## PP.Nat_2R_GFPRB 0.00 -0.34 0.49 0.61
## PP.Nat_3R_GFPRB -0.12 -0.41 0.50 0.63
## PP.Nat_1_CBB 0.14 0.28 -0.61 -0.74
## PP.Nat_4R_CBB -0.49 -0.17 0.10 0.07
## PP.Nat_2R_CBB -0.56 -0.42 -0.15 -0.10
## PP.Nat_3R_CBB -0.54 -0.50 -0.16 -0.09
## PP.Nat_1_PBPB 0.09 0.84 0.08 -0.18
## PP.Nat_4R_PBPB -0.45 0.39 0.79 0.63
## PP.Nat_2R_PBPB -0.62 0.20 0.69 0.63
## PP.Nat_3R_PBPB -0.80 -0.26 0.40 0.43
## PP.Nat_1_PBFB 0.06 0.72 -0.14 -0.33
## PP.Nat_4R_PBFB 0.62 -0.49 -0.56 -0.40
## PP.Nat_2R_PBFB 0.84 -0.05 -0.54 -0.52
## PP.Nat_3R_PBFB 1.00 0.13 -0.36 -0.40
## PP.Nat_1_VB 0.13 1.00 0.38 0.16
## PP.Nat_4R_VB -0.36 0.38 1.00 0.91
## PP.Nat_2R_VB -0.40 0.16 0.91 1.00
## PP.Nat_3R_VB -0.54 -0.08 0.73 0.85
## PP.Nat_3R_VB
## PP.Risk_Score_GFFB -0.39
## PP.Risk_Score_GFPRB -0.50
## PP.Risk_Score_CBB -0.16
## PP.Risk_Score_PBFB -0.27
## PP.Risk_Score_PBPB -0.41
## PP.Risk_Score_VB -0.67
## PP.Ben_Score_GFFB -0.71
## PP.Ben_Score_GFPRB -0.37
## PP.Ben_Score_CBB -0.74
## PP.Ben_Score_PBFB -0.42
## PP.Ben_Score_PBPB -0.30
## PP.Ben_Score_VB -0.13
## PP.BehavInt1_GFFB -0.61
## PP.BehavInt2_GFFB -0.55
## PP.BehavInt3_GFFB -0.63
## PP.BehavInt4_GFFB -0.59
## PP.BehavInt1_GFPRB -0.22
## PP.BehavInt2_GFPRB -0.26
## PP.BehavInt3_GFPRB -0.29
## PP.BehavInt4_GFPRB -0.26
## PP.BehavInt1_CBB -0.70
## PP.BehavInt2_CBB -0.73
## PP.BehavInt3_CBB -0.72
## PP.BehavInt4_CBB -0.72
## PP.BehavInt1_PBPB -0.22
## PP.BehavInt2_PBPB -0.26
## PP.BehavInt3_PBPB -0.29
## PP.BehavInt4_PBPB -0.26
## PP.BehavInt1_PBFB -0.37
## PP.BehavInt2_PBFB -0.42
## PP.BehavInt3_PBFB -0.40
## PP.BehavInt4_PBFB -0.40
## PP.BehavInt1_VB -0.13
## PP.BehavInt2_VB -0.15
## PP.BehavInt3_VB -0.10
## PP.BehavInt4_VB -0.13
## PP.Nat_1_GFFB -0.64
## PP.Nat_4R_GFFB 0.41
## PP.Nat_2R_GFFB 0.40
## PP.Nat_3R_GFFB 0.65
## PP.Nat_1_GFPRB 0.02
## PP.Nat_4R_GFPRB 0.59
## PP.Nat_2R_GFPRB 0.54
## PP.Nat_3R_GFPRB 0.63
## PP.Nat_1_CBB -0.74
## PP.Nat_4R_CBB 0.01
## PP.Nat_2R_CBB -0.06
## PP.Nat_3R_CBB -0.01
## PP.Nat_1_PBPB -0.39
## PP.Nat_4R_PBPB 0.47
## PP.Nat_2R_PBPB 0.51
## PP.Nat_3R_PBPB 0.62
## PP.Nat_1_PBFB -0.49
## PP.Nat_4R_PBFB -0.27
## PP.Nat_2R_PBFB -0.49
## PP.Nat_3R_PBFB -0.54
## PP.Nat_1_VB -0.08
## PP.Nat_4R_VB 0.73
## PP.Nat_2R_VB 0.85
## PP.Nat_3R_VB 1.00
##
## n= 60
##
##
## P
## PP.Risk_Score_GFFB PP.Risk_Score_GFPRB PP.Risk_Score_CBB
## PP.Risk_Score_GFFB 0.0000 0.0850
## PP.Risk_Score_GFPRB 0.0000 0.0182
## PP.Risk_Score_CBB 0.0850 0.0182
## PP.Risk_Score_PBFB 0.9821 0.5112 0.0000
## PP.Risk_Score_PBPB 0.2574 0.0351 0.0001
## PP.Risk_Score_VB 0.0000 0.0000 0.0020
## PP.Ben_Score_GFFB 0.7029 0.0810 0.0056
## PP.Ben_Score_GFPRB 0.0669 0.1499 0.0138
## PP.Ben_Score_CBB 0.0000 0.0000 0.6545
## PP.Ben_Score_PBFB 0.0000 0.0000 0.5142
## PP.Ben_Score_PBPB 0.0000 0.0000 0.4770
## PP.Ben_Score_VB 0.0003 0.0021 0.3022
## PP.BehavInt1_GFFB 0.2286 0.9218 0.0035
## PP.BehavInt2_GFFB 0.0769 0.5498 0.0059
## PP.BehavInt3_GFFB 0.3799 0.6576 0.0026
## PP.BehavInt4_GFFB 0.1559 0.9468 0.0038
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.2457
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.3409
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.3261
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.4484
## PP.BehavInt1_CBB 0.0000 0.0000 0.2472
## PP.BehavInt2_CBB 0.0000 0.0000 0.3981
## PP.BehavInt3_CBB 0.0000 0.0000 0.3185
## PP.BehavInt4_CBB 0.0000 0.0000 0.2825
## PP.BehavInt1_PBPB 0.0000 0.0000 0.2457
## PP.BehavInt2_PBPB 0.0000 0.0000 0.3409
## PP.BehavInt3_PBPB 0.0000 0.0000 0.3261
## PP.BehavInt4_PBPB 0.0000 0.0000 0.4484
## PP.BehavInt1_PBFB 0.0000 0.0000 0.6023
## PP.BehavInt2_PBFB 0.0000 0.0000 0.6184
## PP.BehavInt3_PBFB 0.0000 0.0000 0.6188
## PP.BehavInt4_PBFB 0.0000 0.0000 0.5135
## PP.BehavInt1_VB 0.0000 0.0004 0.7257
## PP.BehavInt2_VB 0.0000 0.0000 0.5083
## PP.BehavInt3_VB 0.0000 0.0011 0.7201
## PP.BehavInt4_VB 0.0000 0.0003 0.6442
## PP.Nat_1_GFFB 0.2088 0.8478 0.0353
## PP.Nat_4R_GFFB 0.0000 0.0000 0.1352
## PP.Nat_2R_GFFB 0.0000 0.0000 0.3218
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0484
## PP.Nat_1_GFPRB 0.0000 0.0000 0.8521
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0057
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0108
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.1453
## PP.Nat_1_CBB 0.0000 0.0000 0.9157
## PP.Nat_4R_CBB 0.8636 0.6037 0.0000
## PP.Nat_2R_CBB 0.4251 0.8339 0.0000
## PP.Nat_3R_CBB 0.2271 0.5230 0.0004
## PP.Nat_1_PBPB 0.0000 0.0000 0.6335
## PP.Nat_4R_PBPB 0.6265 0.3733 0.0000
## PP.Nat_2R_PBPB 0.8776 0.4392 0.0000
## PP.Nat_3R_PBPB 0.1019 0.2990 0.1800
## PP.Nat_1_PBFB 0.0000 0.0000 0.4682
## PP.Nat_4R_PBFB 0.1378 0.2253 0.0000
## PP.Nat_2R_PBFB 0.5756 0.4337 0.0000
## PP.Nat_3R_PBFB 0.5479 0.6713 0.0017
## PP.Nat_1_VB 0.0002 0.0013 0.5774
## PP.Nat_4R_VB 0.0125 0.0005 0.0137
## PP.Nat_2R_VB 0.0016 0.0000 0.0219
## PP.Nat_3R_VB 0.0022 0.0000 0.2097
## PP.Risk_Score_PBFB PP.Risk_Score_PBPB PP.Risk_Score_VB
## PP.Risk_Score_GFFB 0.9821 0.2574 0.0000
## PP.Risk_Score_GFPRB 0.5112 0.0351 0.0000
## PP.Risk_Score_CBB 0.0000 0.0001 0.0020
## PP.Risk_Score_PBFB 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0000 0.0000
## PP.Risk_Score_VB 0.0000 0.0000
## PP.Ben_Score_GFFB 0.0010 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.0046 0.0146 0.0557
## PP.Ben_Score_CBB 0.8295 0.0913 0.0000
## PP.Ben_Score_PBFB 0.0000 0.0161 0.2005
## PP.Ben_Score_PBPB 0.0000 0.0001 0.7114
## PP.Ben_Score_VB 0.0000 0.0000 0.0999
## PP.BehavInt1_GFFB 0.0001 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0011 0.0003 0.0007
## PP.BehavInt3_GFFB 0.0001 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0001 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.4229
## PP.BehavInt2_GFPRB 0.0000 0.0002 0.8407
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.7309
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.6202
## PP.BehavInt1_CBB 0.3667 0.3304 0.0000
## PP.BehavInt2_CBB 0.6533 0.1449 0.0000
## PP.BehavInt3_CBB 0.4927 0.2182 0.0000
## PP.BehavInt4_CBB 0.4877 0.2278 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.4229
## PP.BehavInt2_PBPB 0.0000 0.0002 0.8407
## PP.BehavInt3_PBPB 0.0000 0.0000 0.7309
## PP.BehavInt4_PBPB 0.0000 0.0000 0.6202
## PP.BehavInt1_PBFB 0.0000 0.0227 0.1635
## PP.BehavInt2_PBFB 0.0003 0.0593 0.0744
## PP.BehavInt3_PBFB 0.0001 0.0244 0.1804
## PP.BehavInt4_PBFB 0.0000 0.0200 0.1759
## PP.BehavInt1_VB 0.0000 0.0000 0.1356
## PP.BehavInt2_VB 0.0000 0.0000 0.4135
## PP.BehavInt3_VB 0.0000 0.0000 0.0771
## PP.BehavInt4_VB 0.0000 0.0000 0.1527
## PP.Nat_1_GFFB 0.0003 0.0000 0.0000
## PP.Nat_4R_GFFB 0.8430 0.2627 0.0000
## PP.Nat_2R_GFFB 0.2865 0.9675 0.0020
## PP.Nat_3R_GFFB 0.3020 0.0127 0.0000
## PP.Nat_1_GFPRB 0.3628 0.0403 0.0052
## PP.Nat_4R_GFPRB 0.0131 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.1221 0.0019 0.0000
## PP.Nat_3R_GFPRB 0.4855 0.0225 0.0000
## PP.Nat_1_CBB 0.2620 0.0025 0.0000
## PP.Nat_4R_CBB 0.0053 0.1092 0.5515
## PP.Nat_2R_CBB 0.9177 0.2782 0.1624
## PP.Nat_3R_CBB 0.3603 0.0706 0.1652
## PP.Nat_1_PBPB 0.0001 0.0107 0.3741
## PP.Nat_4R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_3R_PBPB 0.7587 0.6297 0.0845
## PP.Nat_1_PBFB 0.0025 0.2233 0.0113
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0049
## PP.Nat_2R_PBFB 0.0000 0.0011 0.0021
## PP.Nat_3R_PBFB 0.1244 0.3723 0.1618
## PP.Nat_1_VB 0.0000 0.0000 0.1547
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0347 0.0011 0.0000
## PP.Ben_Score_GFFB PP.Ben_Score_GFPRB PP.Ben_Score_CBB
## PP.Risk_Score_GFFB 0.7029 0.0669 0.0000
## PP.Risk_Score_GFPRB 0.0810 0.1499 0.0000
## PP.Risk_Score_CBB 0.0056 0.0138 0.6545
## PP.Risk_Score_PBFB 0.0010 0.0046 0.8295
## PP.Risk_Score_PBPB 0.0000 0.0146 0.0913
## PP.Risk_Score_VB 0.0000 0.0557 0.0000
## PP.Ben_Score_GFFB 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.0000 0.1485
## PP.Ben_Score_CBB 0.0000 0.1485
## PP.Ben_Score_PBFB 0.0596 0.7790 0.0000
## PP.Ben_Score_PBPB 0.5108 0.6169 0.0000
## PP.Ben_Score_VB 0.6509 0.5244 0.0045
## PP.BehavInt1_GFFB 0.0000 0.0000 0.0009
## PP.BehavInt2_GFFB 0.0000 0.0000 0.0057
## PP.BehavInt3_GFFB 0.0000 0.0000 0.0003
## PP.BehavInt4_GFFB 0.0000 0.0000 0.0020
## PP.BehavInt1_GFPRB 0.9020 0.3180 0.0000
## PP.BehavInt2_GFPRB 0.7788 0.2634 0.0000
## PP.BehavInt3_GFPRB 0.6865 0.3986 0.0000
## PP.BehavInt4_GFPRB 0.6972 0.4528 0.0000
## PP.BehavInt1_CBB 0.0000 0.3743 0.0000
## PP.BehavInt2_CBB 0.0000 0.2289 0.0000
## PP.BehavInt3_CBB 0.0000 0.3160 0.0000
## PP.BehavInt4_CBB 0.0000 0.2794 0.0000
## PP.BehavInt1_PBPB 0.9020 0.3180 0.0000
## PP.BehavInt2_PBPB 0.7788 0.2634 0.0000
## PP.BehavInt3_PBPB 0.6865 0.3986 0.0000
## PP.BehavInt4_PBPB 0.6972 0.4528 0.0000
## PP.BehavInt1_PBFB 0.1836 0.3730 0.0000
## PP.BehavInt2_PBFB 0.0733 0.4840 0.0000
## PP.BehavInt3_PBFB 0.0828 0.6822 0.0000
## PP.BehavInt4_PBFB 0.0810 0.6609 0.0000
## PP.BehavInt1_VB 0.9987 0.9208 0.0013
## PP.BehavInt2_VB 0.9566 0.6204 0.0006
## PP.BehavInt3_VB 0.9799 0.9790 0.0017
## PP.BehavInt4_VB 0.9818 0.9238 0.0011
## PP.Nat_1_GFFB 0.0000 0.0000 0.0011
## PP.Nat_4R_GFFB 0.1226 0.6213 0.0000
## PP.Nat_2R_GFFB 0.2595 0.7109 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0675 0.0000
## PP.Nat_1_GFPRB 0.0812 0.0000 0.1332
## PP.Nat_4R_GFPRB 0.0011 0.9838 0.0000
## PP.Nat_2R_GFPRB 0.0037 0.9652 0.0000
## PP.Nat_3R_GFPRB 0.0002 0.5055 0.0000
## PP.Nat_1_CBB 0.0000 0.1103 0.0000
## PP.Nat_4R_CBB 0.0170 0.0010 0.2598
## PP.Nat_2R_CBB 0.3101 0.0073 0.4670
## PP.Nat_3R_CBB 0.2343 0.0172 0.6493
## PP.Nat_1_PBPB 0.1599 0.6674 0.0000
## PP.Nat_4R_PBPB 0.0000 0.0005 0.0818
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0301
## PP.Nat_3R_PBPB 0.0000 0.0003 0.0000
## PP.Nat_1_PBFB 0.0139 0.7514 0.0000
## PP.Nat_4R_PBFB 0.0012 0.0000 0.2333
## PP.Nat_2R_PBFB 0.0000 0.0000 0.2638
## PP.Nat_3R_PBFB 0.0001 0.0000 0.1156
## PP.Nat_1_VB 0.7350 0.8506 0.0024
## PP.Nat_4R_VB 0.0000 0.0067 0.0000
## PP.Nat_2R_VB 0.0000 0.0021 0.0000
## PP.Nat_3R_VB 0.0000 0.0032 0.0000
## PP.Ben_Score_PBFB PP.Ben_Score_PBPB PP.Ben_Score_VB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0003
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0021
## PP.Risk_Score_CBB 0.5142 0.4770 0.3022
## PP.Risk_Score_PBFB 0.0000 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0161 0.0001 0.0000
## PP.Risk_Score_VB 0.2005 0.7114 0.0999
## PP.Ben_Score_GFFB 0.0596 0.5108 0.6509
## PP.Ben_Score_GFPRB 0.7790 0.6169 0.5244
## PP.Ben_Score_CBB 0.0000 0.0000 0.0045
## PP.Ben_Score_PBFB 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000
## PP.BehavInt1_GFFB 0.7878 0.4463 0.5565
## PP.BehavInt2_GFFB 0.9941 0.3899 0.6206
## PP.BehavInt3_GFFB 0.6174 0.6117 0.6441
## PP.BehavInt4_GFFB 0.9165 0.3658 0.4877
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0024
## PP.BehavInt2_CBB 0.0000 0.0000 0.0074
## PP.BehavInt3_CBB 0.0000 0.0000 0.0041
## PP.BehavInt4_CBB 0.0000 0.0000 0.0038
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.Nat_1_GFFB 0.8793 0.3782 0.3730
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0024
## PP.Nat_1_GFPRB 0.2850 0.6691 0.3935
## PP.Nat_4R_GFPRB 0.0000 0.0039 0.1014
## PP.Nat_2R_GFPRB 0.0000 0.0005 0.0163
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0046
## PP.Nat_1_CBB 0.0000 0.0000 0.0909
## PP.Nat_4R_CBB 0.5185 0.8768 0.0358
## PP.Nat_2R_CBB 0.3302 0.0368 0.0000
## PP.Nat_3R_CBB 0.0403 0.0017 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1504 0.0070 0.0087
## PP.Nat_2R_PBPB 0.5027 0.1216 0.3696
## PP.Nat_3R_PBPB 0.0023 0.0044 0.0054
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0001 0.0000 0.0017
## PP.Nat_2R_PBFB 0.6257 0.6953 0.6752
## PP.Nat_3R_PBFB 0.2406 0.1721 0.0429
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.8458 0.1904 0.0103
## PP.Nat_2R_VB 0.0745 0.6529 0.4038
## PP.Nat_3R_VB 0.0008 0.0191 0.3392
## PP.BehavInt1_GFFB PP.BehavInt2_GFFB PP.BehavInt3_GFFB
## PP.Risk_Score_GFFB 0.2286 0.0769 0.3799
## PP.Risk_Score_GFPRB 0.9218 0.5498 0.6576
## PP.Risk_Score_CBB 0.0035 0.0059 0.0026
## PP.Risk_Score_PBFB 0.0001 0.0011 0.0001
## PP.Risk_Score_PBPB 0.0000 0.0003 0.0000
## PP.Risk_Score_VB 0.0000 0.0007 0.0000
## PP.Ben_Score_GFFB 0.0000 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Ben_Score_CBB 0.0009 0.0057 0.0003
## PP.Ben_Score_PBFB 0.7878 0.9941 0.6174
## PP.Ben_Score_PBPB 0.4463 0.3899 0.6117
## PP.Ben_Score_VB 0.5565 0.6206 0.6441
## PP.BehavInt1_GFFB 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.2090 0.1871 0.3010
## PP.BehavInt2_GFPRB 0.2228 0.1568 0.3410
## PP.BehavInt3_GFPRB 0.3022 0.2398 0.4331
## PP.BehavInt4_GFPRB 0.3149 0.2603 0.4390
## PP.BehavInt1_CBB 0.0079 0.0304 0.0031
## PP.BehavInt2_CBB 0.0017 0.0100 0.0006
## PP.BehavInt3_CBB 0.0051 0.0215 0.0018
## PP.BehavInt4_CBB 0.0051 0.0233 0.0019
## PP.BehavInt1_PBPB 0.2090 0.1871 0.3010
## PP.BehavInt2_PBPB 0.2228 0.1568 0.3410
## PP.BehavInt3_PBPB 0.3022 0.2398 0.4331
## PP.BehavInt4_PBPB 0.3149 0.2603 0.4390
## PP.BehavInt1_PBFB 0.7510 0.5318 0.9436
## PP.BehavInt2_PBFB 0.9283 0.7740 0.7280
## PP.BehavInt3_PBFB 0.9262 0.8388 0.7525
## PP.BehavInt4_PBFB 0.9185 0.8476 0.7381
## PP.BehavInt1_VB 0.2341 0.2450 0.3031
## PP.BehavInt2_VB 0.1880 0.1495 0.2718
## PP.BehavInt3_VB 0.2598 0.2946 0.3244
## PP.BehavInt4_VB 0.2163 0.2161 0.2845
## PP.Nat_1_GFFB 0.0000 0.0000 0.0000
## PP.Nat_4R_GFFB 0.8948 0.4505 0.8385
## PP.Nat_2R_GFFB 0.6689 0.3811 0.9097
## PP.Nat_3R_GFFB 0.0029 0.0248 0.0010
## PP.Nat_1_GFPRB 0.0042 0.0003 0.0101
## PP.Nat_4R_GFPRB 0.0953 0.4118 0.0471
## PP.Nat_2R_GFPRB 0.2157 0.6737 0.1164
## PP.Nat_3R_GFPRB 0.0416 0.1654 0.0195
## PP.Nat_1_CBB 0.0002 0.0035 0.0000
## PP.Nat_4R_CBB 0.0066 0.0034 0.0069
## PP.Nat_2R_CBB 0.2326 0.1122 0.2550
## PP.Nat_3R_CBB 0.2136 0.0809 0.2281
## PP.Nat_1_PBPB 0.8175 0.5478 0.9598
## PP.Nat_4R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0000 0.0000
## PP.Nat_1_PBFB 0.4976 0.8139 0.3449
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_1_VB 0.4431 0.4383 0.5397
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFFB PP.BehavInt1_GFPRB PP.BehavInt2_GFPRB
## PP.Risk_Score_GFFB 0.1559 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.9468 0.0000 0.0000
## PP.Risk_Score_CBB 0.0038 0.2457 0.3409
## PP.Risk_Score_PBFB 0.0001 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0000 0.0000 0.0002
## PP.Risk_Score_VB 0.0000 0.4229 0.8407
## PP.Ben_Score_GFFB 0.0000 0.9020 0.7788
## PP.Ben_Score_GFPRB 0.0000 0.3180 0.2634
## PP.Ben_Score_CBB 0.0020 0.0000 0.0000
## PP.Ben_Score_PBFB 0.9165 0.0000 0.0000
## PP.Ben_Score_PBPB 0.3658 0.0000 0.0000
## PP.Ben_Score_VB 0.4877 0.0000 0.0000
## PP.BehavInt1_GFFB 0.0000 0.2090 0.2228
## PP.BehavInt2_GFFB 0.0000 0.1871 0.1568
## PP.BehavInt3_GFFB 0.0000 0.3010 0.3410
## PP.BehavInt4_GFFB 0.1617 0.1642
## PP.BehavInt1_GFPRB 0.1617 0.0000
## PP.BehavInt2_GFPRB 0.1642 0.0000
## PP.BehavInt3_GFPRB 0.2370 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.2391 0.0000 0.0000
## PP.BehavInt1_CBB 0.0154 0.0000 0.0000
## PP.BehavInt2_CBB 0.0036 0.0000 0.0000
## PP.BehavInt3_CBB 0.0098 0.0000 0.0000
## PP.BehavInt4_CBB 0.0097 0.0000 0.0000
## PP.BehavInt1_PBPB 0.1617 0.0000 0.0000
## PP.BehavInt2_PBPB 0.1642 0.0000 0.0000
## PP.BehavInt3_PBPB 0.2370 0.0000 0.0000
## PP.BehavInt4_PBPB 0.2391 0.0000 0.0000
## PP.BehavInt1_PBFB 0.6208 0.0000 0.0000
## PP.BehavInt2_PBFB 0.9204 0.0000 0.0000
## PP.BehavInt3_PBFB 0.9174 0.0000 0.0000
## PP.BehavInt4_PBFB 0.9253 0.0000 0.0000
## PP.BehavInt1_VB 0.1829 0.0000 0.0000
## PP.BehavInt2_VB 0.1472 0.0000 0.0000
## PP.BehavInt3_VB 0.2063 0.0000 0.0000
## PP.BehavInt4_VB 0.1664 0.0000 0.0000
## PP.Nat_1_GFFB 0.0000 0.2043 0.2038
## PP.Nat_4R_GFFB 0.7437 0.0000 0.0000
## PP.Nat_2R_GFFB 0.5602 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0059 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0041 0.7029 0.2913
## PP.Nat_4R_GFPRB 0.1290 0.0156 0.0016
## PP.Nat_2R_GFPRB 0.2664 0.0029 0.0002
## PP.Nat_3R_GFPRB 0.0820 0.0000 0.0000
## PP.Nat_1_CBB 0.0005 0.0003 0.0000
## PP.Nat_4R_CBB 0.0062 0.5735 0.4954
## PP.Nat_2R_CBB 0.2592 0.0832 0.1512
## PP.Nat_3R_CBB 0.2408 0.0041 0.0137
## PP.Nat_1_PBPB 0.7319 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0000 0.0014 0.0036
## PP.Nat_2R_PBPB 0.0000 0.0346 0.0536
## PP.Nat_3R_PBPB 0.0001 0.0164 0.0257
## PP.Nat_1_PBFB 0.6127 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.2879 0.3486
## PP.Nat_3R_PBFB 0.0000 0.4519 0.5515
## PP.Nat_1_VB 0.3530 0.0000 0.0000
## PP.Nat_4R_VB 0.0000 0.0725 0.1831
## PP.Nat_2R_VB 0.0000 0.9355 0.7131
## PP.Nat_3R_VB 0.0000 0.0951 0.0421
## PP.BehavInt3_GFPRB PP.BehavInt4_GFPRB PP.BehavInt1_CBB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.3261 0.4484 0.2472
## PP.Risk_Score_PBFB 0.0000 0.0000 0.3667
## PP.Risk_Score_PBPB 0.0000 0.0000 0.3304
## PP.Risk_Score_VB 0.7309 0.6202 0.0000
## PP.Ben_Score_GFFB 0.6865 0.6972 0.0000
## PP.Ben_Score_GFPRB 0.3986 0.4528 0.3743
## PP.Ben_Score_CBB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000 0.0024
## PP.BehavInt1_GFFB 0.3022 0.3149 0.0079
## PP.BehavInt2_GFFB 0.2398 0.2603 0.0304
## PP.BehavInt3_GFFB 0.4331 0.4390 0.0031
## PP.BehavInt4_GFFB 0.2370 0.2391 0.0154
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0003
## PP.BehavInt2_VB 0.0000 0.0000 0.0002
## PP.BehavInt3_VB 0.0000 0.0000 0.0006
## PP.BehavInt4_VB 0.0000 0.0000 0.0004
## PP.Nat_1_GFFB 0.2665 0.2605 0.0079
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.5228 0.6308 0.1183
## PP.Nat_4R_GFPRB 0.0035 0.0063 0.0000
## PP.Nat_2R_GFPRB 0.0005 0.0010 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0001 0.0000
## PP.Nat_4R_CBB 0.5976 0.8537 0.0770
## PP.Nat_2R_CBB 0.0938 0.0372 0.3182
## PP.Nat_3R_CBB 0.0070 0.0020 0.7783
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0044 0.0054 0.2491
## PP.Nat_2R_PBPB 0.0636 0.0870 0.1132
## PP.Nat_3R_PBPB 0.0095 0.0078 0.0001
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0604
## PP.Nat_2R_PBFB 0.4354 0.5432 0.5720
## PP.Nat_3R_PBFB 0.3199 0.2242 0.2249
## PP.Nat_1_VB 0.0000 0.0000 0.0009
## PP.Nat_4R_VB 0.1789 0.1348 0.0002
## PP.Nat_2R_VB 0.6584 0.7905 0.0000
## PP.Nat_3R_VB 0.0224 0.0419 0.0000
## PP.BehavInt2_CBB PP.BehavInt3_CBB PP.BehavInt4_CBB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.3981 0.3185 0.2825
## PP.Risk_Score_PBFB 0.6533 0.4927 0.4877
## PP.Risk_Score_PBPB 0.1449 0.2182 0.2278
## PP.Risk_Score_VB 0.0000 0.0000 0.0000
## PP.Ben_Score_GFFB 0.0000 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.2289 0.3160 0.2794
## PP.Ben_Score_CBB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0074 0.0041 0.0038
## PP.BehavInt1_GFFB 0.0017 0.0051 0.0051
## PP.BehavInt2_GFFB 0.0100 0.0215 0.0233
## PP.BehavInt3_GFFB 0.0006 0.0018 0.0019
## PP.BehavInt4_GFFB 0.0036 0.0098 0.0097
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0013 0.0007 0.0006
## PP.BehavInt2_VB 0.0005 0.0004 0.0004
## PP.BehavInt3_VB 0.0026 0.0011 0.0010
## PP.BehavInt4_VB 0.0015 0.0007 0.0006
## PP.Nat_1_GFFB 0.0016 0.0051 0.0044
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.1092 0.0969 0.1140
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0955 0.0973 0.0780
## PP.Nat_2R_CBB 0.2337 0.2849 0.2842
## PP.Nat_3R_CBB 0.9660 0.8506 0.8796
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1566 0.2007 0.2233
## PP.Nat_2R_PBPB 0.0572 0.0940 0.0972
## PP.Nat_3R_PBPB 0.0002 0.0002 0.0002
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.1103 0.0891 0.0727
## PP.Nat_2R_PBFB 0.4418 0.4966 0.5380
## PP.Nat_3R_PBFB 0.2120 0.2171 0.2291
## PP.Nat_1_VB 0.0026 0.0019 0.0022
## PP.Nat_4R_VB 0.0000 0.0000 0.0000
## PP.Nat_2R_VB 0.0000 0.0000 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB PP.BehavInt2_PBPB PP.BehavInt3_PBPB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.2457 0.3409 0.3261
## PP.Risk_Score_PBFB 0.0000 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0000 0.0002 0.0000
## PP.Risk_Score_VB 0.4229 0.8407 0.7309
## PP.Ben_Score_GFFB 0.9020 0.7788 0.6865
## PP.Ben_Score_GFPRB 0.3180 0.2634 0.3986
## PP.Ben_Score_CBB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.2090 0.2228 0.3022
## PP.BehavInt2_GFFB 0.1871 0.1568 0.2398
## PP.BehavInt3_GFFB 0.3010 0.3410 0.4331
## PP.BehavInt4_GFFB 0.1617 0.1642 0.2370
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.Nat_1_GFFB 0.2043 0.2038 0.2665
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.7029 0.2913 0.5228
## PP.Nat_4R_GFPRB 0.0156 0.0016 0.0035
## PP.Nat_2R_GFPRB 0.0029 0.0002 0.0005
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0003 0.0000 0.0000
## PP.Nat_4R_CBB 0.5735 0.4954 0.5976
## PP.Nat_2R_CBB 0.0832 0.1512 0.0938
## PP.Nat_3R_CBB 0.0041 0.0137 0.0070
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0014 0.0036 0.0044
## PP.Nat_2R_PBPB 0.0346 0.0536 0.0636
## PP.Nat_3R_PBPB 0.0164 0.0257 0.0095
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.2879 0.3486 0.4354
## PP.Nat_3R_PBFB 0.4519 0.5515 0.3199
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.0725 0.1831 0.1789
## PP.Nat_2R_VB 0.9355 0.7131 0.6584
## PP.Nat_3R_VB 0.0951 0.0421 0.0224
## PP.BehavInt4_PBPB PP.BehavInt1_PBFB PP.BehavInt2_PBFB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.4484 0.6023 0.6184
## PP.Risk_Score_PBFB 0.0000 0.0000 0.0003
## PP.Risk_Score_PBPB 0.0000 0.0227 0.0593
## PP.Risk_Score_VB 0.6202 0.1635 0.0744
## PP.Ben_Score_GFFB 0.6972 0.1836 0.0733
## PP.Ben_Score_GFPRB 0.4528 0.3730 0.4840
## PP.Ben_Score_CBB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.3149 0.7510 0.9283
## PP.BehavInt2_GFFB 0.2603 0.5318 0.7740
## PP.BehavInt3_GFFB 0.4390 0.9436 0.7280
## PP.BehavInt4_GFFB 0.2391 0.6208 0.9204
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.Nat_1_GFFB 0.2605 0.6248 0.9935
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.6308 0.0820 0.0736
## PP.Nat_4R_GFPRB 0.0063 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0010 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0001 0.0000 0.0000
## PP.Nat_4R_CBB 0.8537 0.5238 0.3935
## PP.Nat_2R_CBB 0.0372 0.3391 0.5701
## PP.Nat_3R_CBB 0.0020 0.0435 0.1175
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0054 0.1156 0.1529
## PP.Nat_2R_PBPB 0.0870 0.3742 0.4743
## PP.Nat_3R_PBPB 0.0078 0.0081 0.0122
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0001
## PP.Nat_2R_PBFB 0.5432 0.5157 0.5244
## PP.Nat_3R_PBFB 0.2242 0.4289 0.4733
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.1348 0.8756 0.6219
## PP.Nat_2R_VB 0.7905 0.1024 0.0431
## PP.Nat_3R_VB 0.0419 0.0032 0.0008
## PP.BehavInt3_PBFB PP.BehavInt4_PBFB PP.BehavInt1_VB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0004
## PP.Risk_Score_CBB 0.6188 0.5135 0.7257
## PP.Risk_Score_PBFB 0.0001 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0244 0.0200 0.0000
## PP.Risk_Score_VB 0.1804 0.1759 0.1356
## PP.Ben_Score_GFFB 0.0828 0.0810 0.9987
## PP.Ben_Score_GFPRB 0.6822 0.6609 0.9208
## PP.Ben_Score_CBB 0.0000 0.0000 0.0013
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.9262 0.9185 0.2341
## PP.BehavInt2_GFFB 0.8388 0.8476 0.2450
## PP.BehavInt3_GFFB 0.7525 0.7381 0.3031
## PP.BehavInt4_GFFB 0.9174 0.9253 0.1829
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0000 0.0000 0.0003
## PP.BehavInt2_CBB 0.0000 0.0000 0.0013
## PP.BehavInt3_CBB 0.0000 0.0000 0.0007
## PP.BehavInt4_CBB 0.0000 0.0000 0.0006
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000 0.0000
## PP.Nat_1_GFFB 0.9479 0.9800 0.1772
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0000 0.0000 0.0009
## PP.Nat_1_GFPRB 0.1965 0.2226 0.8204
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0472
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.0055
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.0009
## PP.Nat_1_CBB 0.0000 0.0000 0.0344
## PP.Nat_4R_CBB 0.6149 0.4770 0.2576
## PP.Nat_2R_CBB 0.2702 0.3768 0.0005
## PP.Nat_3R_CBB 0.0332 0.0531 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.1862 0.1454 0.0016
## PP.Nat_2R_PBPB 0.5406 0.4251 0.1046
## PP.Nat_3R_PBPB 0.0030 0.0040 0.0211
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0002 0.0001 0.0000
## PP.Nat_2R_PBFB 0.6281 0.5338 0.7690
## PP.Nat_3R_PBFB 0.2791 0.3244 0.1786
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.8524 0.8594 0.0097
## PP.Nat_2R_VB 0.0899 0.0928 0.4262
## PP.Nat_3R_VB 0.0015 0.0014 0.3345
## PP.BehavInt2_VB PP.BehavInt3_VB PP.BehavInt4_VB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0011 0.0003
## PP.Risk_Score_CBB 0.5083 0.7201 0.6442
## PP.Risk_Score_PBFB 0.0000 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0000 0.0000 0.0000
## PP.Risk_Score_VB 0.4135 0.0771 0.1527
## PP.Ben_Score_GFFB 0.9566 0.9799 0.9818
## PP.Ben_Score_GFPRB 0.6204 0.9790 0.9238
## PP.Ben_Score_CBB 0.0006 0.0017 0.0011
## PP.Ben_Score_PBFB 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.0000 0.0000 0.0000
## PP.BehavInt1_GFFB 0.1880 0.2598 0.2163
## PP.BehavInt2_GFFB 0.1495 0.2946 0.2161
## PP.BehavInt3_GFFB 0.2718 0.3244 0.2845
## PP.BehavInt4_GFFB 0.1472 0.2063 0.1664
## PP.BehavInt1_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0002 0.0006 0.0004
## PP.BehavInt2_CBB 0.0005 0.0026 0.0015
## PP.BehavInt3_CBB 0.0004 0.0011 0.0007
## PP.BehavInt4_CBB 0.0004 0.0010 0.0006
## PP.BehavInt1_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.0000 0.0000 0.0000
## PP.BehavInt2_VB 0.0000 0.0000
## PP.BehavInt3_VB 0.0000 0.0000
## PP.BehavInt4_VB 0.0000 0.0000
## PP.Nat_1_GFFB 0.1188 0.1809 0.1631
## PP.Nat_4R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0000 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0002 0.0014 0.0008
## PP.Nat_1_GFPRB 0.5376 0.5971 0.8229
## PP.Nat_4R_GFPRB 0.0041 0.0949 0.0375
## PP.Nat_2R_GFPRB 0.0001 0.0118 0.0044
## PP.Nat_3R_GFPRB 0.0000 0.0027 0.0009
## PP.Nat_1_CBB 0.0128 0.0586 0.0388
## PP.Nat_4R_CBB 0.3045 0.1891 0.2000
## PP.Nat_2R_CBB 0.0029 0.0001 0.0004
## PP.Nat_3R_CBB 0.0003 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0007 0.0018 0.0020
## PP.Nat_2R_PBPB 0.0423 0.0939 0.0866
## PP.Nat_3R_PBPB 0.1808 0.0229 0.0273
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.0001 0.0002
## PP.Nat_2R_PBFB 0.5170 0.7072 0.8037
## PP.Nat_3R_PBFB 0.4011 0.1855 0.1916
## PP.Nat_1_VB 0.0000 0.0000 0.0000
## PP.Nat_4R_VB 0.0242 0.0055 0.0129
## PP.Nat_2R_VB 0.6961 0.3164 0.4848
## PP.Nat_3R_VB 0.2688 0.4485 0.3164
## PP.Nat_1_GFFB PP.Nat_4R_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB
## PP.Risk_Score_GFFB 0.2088 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.8478 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.0353 0.1352 0.3218 0.0484
## PP.Risk_Score_PBFB 0.0003 0.8430 0.2865 0.3020
## PP.Risk_Score_PBPB 0.0000 0.2627 0.9675 0.0127
## PP.Risk_Score_VB 0.0000 0.0000 0.0020 0.0000
## PP.Ben_Score_GFFB 0.0000 0.1226 0.2595 0.0000
## PP.Ben_Score_GFPRB 0.0000 0.6213 0.7109 0.0675
## PP.Ben_Score_CBB 0.0011 0.0000 0.0000 0.0000
## PP.Ben_Score_PBFB 0.8793 0.0000 0.0000 0.0000
## PP.Ben_Score_PBPB 0.3782 0.0000 0.0000 0.0000
## PP.Ben_Score_VB 0.3730 0.0000 0.0000 0.0024
## PP.BehavInt1_GFFB 0.0000 0.8948 0.6689 0.0029
## PP.BehavInt2_GFFB 0.0000 0.4505 0.3811 0.0248
## PP.BehavInt3_GFFB 0.0000 0.8385 0.9097 0.0010
## PP.BehavInt4_GFFB 0.0000 0.7437 0.5602 0.0059
## PP.BehavInt1_GFPRB 0.2043 0.0000 0.0000 0.0000
## PP.BehavInt2_GFPRB 0.2038 0.0000 0.0000 0.0000
## PP.BehavInt3_GFPRB 0.2665 0.0000 0.0000 0.0000
## PP.BehavInt4_GFPRB 0.2605 0.0000 0.0000 0.0000
## PP.BehavInt1_CBB 0.0079 0.0000 0.0000 0.0000
## PP.BehavInt2_CBB 0.0016 0.0000 0.0000 0.0000
## PP.BehavInt3_CBB 0.0051 0.0000 0.0000 0.0000
## PP.BehavInt4_CBB 0.0044 0.0000 0.0000 0.0000
## PP.BehavInt1_PBPB 0.2043 0.0000 0.0000 0.0000
## PP.BehavInt2_PBPB 0.2038 0.0000 0.0000 0.0000
## PP.BehavInt3_PBPB 0.2665 0.0000 0.0000 0.0000
## PP.BehavInt4_PBPB 0.2605 0.0000 0.0000 0.0000
## PP.BehavInt1_PBFB 0.6248 0.0000 0.0000 0.0000
## PP.BehavInt2_PBFB 0.9935 0.0000 0.0000 0.0000
## PP.BehavInt3_PBFB 0.9479 0.0000 0.0000 0.0000
## PP.BehavInt4_PBFB 0.9800 0.0000 0.0000 0.0000
## PP.BehavInt1_VB 0.1772 0.0000 0.0000 0.0009
## PP.BehavInt2_VB 0.1188 0.0000 0.0000 0.0002
## PP.BehavInt3_VB 0.1809 0.0000 0.0000 0.0014
## PP.BehavInt4_VB 0.1631 0.0000 0.0000 0.0008
## PP.Nat_1_GFFB 0.7073 0.5783 0.0051
## PP.Nat_4R_GFFB 0.7073 0.0000 0.0000
## PP.Nat_2R_GFFB 0.5783 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0051 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0003 0.0012 0.0295 0.1362
## PP.Nat_4R_GFPRB 0.1716 0.0000 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.3107 0.0000 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0689 0.0000 0.0000 0.0000
## PP.Nat_1_CBB 0.0003 0.0000 0.0000 0.0000
## PP.Nat_4R_CBB 0.0678 0.5893 0.5512 0.4635
## PP.Nat_2R_CBB 0.5532 0.3854 0.0755 0.5070
## PP.Nat_3R_CBB 0.4670 0.2241 0.0183 0.2859
## PP.Nat_1_PBPB 0.7369 0.0000 0.0000 0.0000
## PP.Nat_4R_PBPB 0.0000 0.6316 0.8374 0.0346
## PP.Nat_2R_PBPB 0.0000 0.8182 0.8131 0.0153
## PP.Nat_3R_PBPB 0.0000 0.1523 0.0125 0.0015
## PP.Nat_1_PBFB 0.5796 0.0000 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0000 0.1543 0.0739 0.8364
## PP.Nat_2R_PBFB 0.0000 0.6849 0.3625 0.0307
## PP.Nat_3R_PBFB 0.0000 0.6284 0.1886 0.0157
## PP.Nat_1_VB 0.2686 0.0000 0.0000 0.0022
## PP.Nat_4R_VB 0.0000 0.0088 0.0701 0.0000
## PP.Nat_2R_VB 0.0000 0.0008 0.0065 0.0000
## PP.Nat_3R_VB 0.0000 0.0013 0.0016 0.0000
## PP.Nat_1_GFPRB PP.Nat_4R_GFPRB PP.Nat_2R_GFPRB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.0000
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.0000
## PP.Risk_Score_CBB 0.8521 0.0057 0.0108
## PP.Risk_Score_PBFB 0.3628 0.0131 0.1221
## PP.Risk_Score_PBPB 0.0403 0.0000 0.0019
## PP.Risk_Score_VB 0.0052 0.0000 0.0000
## PP.Ben_Score_GFFB 0.0812 0.0011 0.0037
## PP.Ben_Score_GFPRB 0.0000 0.9838 0.9652
## PP.Ben_Score_CBB 0.1332 0.0000 0.0000
## PP.Ben_Score_PBFB 0.2850 0.0000 0.0000
## PP.Ben_Score_PBPB 0.6691 0.0039 0.0005
## PP.Ben_Score_VB 0.3935 0.1014 0.0163
## PP.BehavInt1_GFFB 0.0042 0.0953 0.2157
## PP.BehavInt2_GFFB 0.0003 0.4118 0.6737
## PP.BehavInt3_GFFB 0.0101 0.0471 0.1164
## PP.BehavInt4_GFFB 0.0041 0.1290 0.2664
## PP.BehavInt1_GFPRB 0.7029 0.0156 0.0029
## PP.BehavInt2_GFPRB 0.2913 0.0016 0.0002
## PP.BehavInt3_GFPRB 0.5228 0.0035 0.0005
## PP.BehavInt4_GFPRB 0.6308 0.0063 0.0010
## PP.BehavInt1_CBB 0.1183 0.0000 0.0000
## PP.BehavInt2_CBB 0.1092 0.0000 0.0000
## PP.BehavInt3_CBB 0.0969 0.0000 0.0000
## PP.BehavInt4_CBB 0.1140 0.0000 0.0000
## PP.BehavInt1_PBPB 0.7029 0.0156 0.0029
## PP.BehavInt2_PBPB 0.2913 0.0016 0.0002
## PP.BehavInt3_PBPB 0.5228 0.0035 0.0005
## PP.BehavInt4_PBPB 0.6308 0.0063 0.0010
## PP.BehavInt1_PBFB 0.0820 0.0000 0.0000
## PP.BehavInt2_PBFB 0.0736 0.0000 0.0000
## PP.BehavInt3_PBFB 0.1965 0.0000 0.0000
## PP.BehavInt4_PBFB 0.2226 0.0000 0.0000
## PP.BehavInt1_VB 0.8204 0.0472 0.0055
## PP.BehavInt2_VB 0.5376 0.0041 0.0001
## PP.BehavInt3_VB 0.5971 0.0949 0.0118
## PP.BehavInt4_VB 0.8229 0.0375 0.0044
## PP.Nat_1_GFFB 0.0003 0.1716 0.3107
## PP.Nat_4R_GFFB 0.0012 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0295 0.0000 0.0000
## PP.Nat_3R_GFFB 0.1362 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0000 0.0000
## PP.Nat_4R_GFPRB 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0030 0.0000 0.0000
## PP.Nat_1_CBB 0.0311 0.0000 0.0000
## PP.Nat_4R_CBB 0.0261 0.6134 0.7953
## PP.Nat_2R_CBB 0.0019 0.5774 0.6340
## PP.Nat_3R_CBB 0.0020 0.7303 0.7766
## PP.Nat_1_PBPB 0.1476 0.0000 0.0000
## PP.Nat_4R_PBPB 0.5598 0.0157 0.1728
## PP.Nat_2R_PBPB 0.1414 0.0419 0.2410
## PP.Nat_3R_PBPB 0.0003 0.3522 0.6607
## PP.Nat_1_PBFB 0.0433 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0044 0.9247 0.3866
## PP.Nat_2R_PBFB 0.0034 0.2110 0.6604
## PP.Nat_3R_PBFB 0.0000 0.7018 0.9882
## PP.Nat_1_VB 0.7864 0.0531 0.0076
## PP.Nat_4R_VB 0.3453 0.0000 0.0000
## PP.Nat_2R_VB 0.2831 0.0000 0.0000
## PP.Nat_3R_VB 0.8787 0.0000 0.0000
## PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB PP.Nat_2R_CBB
## PP.Risk_Score_GFFB 0.0000 0.0000 0.8636 0.4251
## PP.Risk_Score_GFPRB 0.0000 0.0000 0.6037 0.8339
## PP.Risk_Score_CBB 0.1453 0.9157 0.0000 0.0000
## PP.Risk_Score_PBFB 0.4855 0.2620 0.0053 0.9177
## PP.Risk_Score_PBPB 0.0225 0.0025 0.1092 0.2782
## PP.Risk_Score_VB 0.0000 0.0000 0.5515 0.1624
## PP.Ben_Score_GFFB 0.0002 0.0000 0.0170 0.3101
## PP.Ben_Score_GFPRB 0.5055 0.1103 0.0010 0.0073
## PP.Ben_Score_CBB 0.0000 0.0000 0.2598 0.4670
## PP.Ben_Score_PBFB 0.0000 0.0000 0.5185 0.3302
## PP.Ben_Score_PBPB 0.0000 0.0000 0.8768 0.0368
## PP.Ben_Score_VB 0.0046 0.0909 0.0358 0.0000
## PP.BehavInt1_GFFB 0.0416 0.0002 0.0066 0.2326
## PP.BehavInt2_GFFB 0.1654 0.0035 0.0034 0.1122
## PP.BehavInt3_GFFB 0.0195 0.0000 0.0069 0.2550
## PP.BehavInt4_GFFB 0.0820 0.0005 0.0062 0.2592
## PP.BehavInt1_GFPRB 0.0000 0.0003 0.5735 0.0832
## PP.BehavInt2_GFPRB 0.0000 0.0000 0.4954 0.1512
## PP.BehavInt3_GFPRB 0.0000 0.0000 0.5976 0.0938
## PP.BehavInt4_GFPRB 0.0000 0.0001 0.8537 0.0372
## PP.BehavInt1_CBB 0.0000 0.0000 0.0770 0.3182
## PP.BehavInt2_CBB 0.0000 0.0000 0.0955 0.2337
## PP.BehavInt3_CBB 0.0000 0.0000 0.0973 0.2849
## PP.BehavInt4_CBB 0.0000 0.0000 0.0780 0.2842
## PP.BehavInt1_PBPB 0.0000 0.0003 0.5735 0.0832
## PP.BehavInt2_PBPB 0.0000 0.0000 0.4954 0.1512
## PP.BehavInt3_PBPB 0.0000 0.0000 0.5976 0.0938
## PP.BehavInt4_PBPB 0.0000 0.0001 0.8537 0.0372
## PP.BehavInt1_PBFB 0.0000 0.0000 0.5238 0.3391
## PP.BehavInt2_PBFB 0.0000 0.0000 0.3935 0.5701
## PP.BehavInt3_PBFB 0.0000 0.0000 0.6149 0.2702
## PP.BehavInt4_PBFB 0.0000 0.0000 0.4770 0.3768
## PP.BehavInt1_VB 0.0009 0.0344 0.2576 0.0005
## PP.BehavInt2_VB 0.0000 0.0128 0.3045 0.0029
## PP.BehavInt3_VB 0.0027 0.0586 0.1891 0.0001
## PP.BehavInt4_VB 0.0009 0.0388 0.2000 0.0004
## PP.Nat_1_GFFB 0.0689 0.0003 0.0678 0.5532
## PP.Nat_4R_GFFB 0.0000 0.0000 0.5893 0.3854
## PP.Nat_2R_GFFB 0.0000 0.0000 0.5512 0.0755
## PP.Nat_3R_GFFB 0.0000 0.0000 0.4635 0.5070
## PP.Nat_1_GFPRB 0.0030 0.0311 0.0261 0.0019
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.6134 0.5774
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.7953 0.6340
## PP.Nat_3R_GFPRB 0.0000 0.8011 0.6511
## PP.Nat_1_CBB 0.0000 0.1662 0.1368
## PP.Nat_4R_CBB 0.8011 0.1662 0.0000
## PP.Nat_2R_CBB 0.6511 0.1368 0.0000
## PP.Nat_3R_CBB 0.7621 0.5798 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.0000 0.6215 0.2573
## PP.Nat_4R_PBPB 0.2916 0.0263 0.0045 0.4459
## PP.Nat_2R_PBPB 0.1952 0.0101 0.0002 0.0108
## PP.Nat_3R_PBPB 0.0530 0.0035 0.0624 0.0026
## PP.Nat_1_PBFB 0.0000 0.0000 0.2234 0.9870
## PP.Nat_4R_PBFB 0.3631 0.3785 0.0000 0.0109
## PP.Nat_2R_PBFB 0.3716 0.2842 0.0000 0.0000
## PP.Nat_3R_PBFB 0.3471 0.2878 0.0000 0.0000
## PP.Nat_1_VB 0.0012 0.0329 0.1925 0.0008
## PP.Nat_4R_VB 0.0000 0.0000 0.4421 0.2440
## PP.Nat_2R_VB 0.0000 0.0000 0.5712 0.4326
## PP.Nat_3R_VB 0.0000 0.0000 0.9524 0.6426
## PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB PP.Nat_2R_PBPB
## PP.Risk_Score_GFFB 0.2271 0.0000 0.6265 0.8776
## PP.Risk_Score_GFPRB 0.5230 0.0000 0.3733 0.4392
## PP.Risk_Score_CBB 0.0004 0.6335 0.0000 0.0000
## PP.Risk_Score_PBFB 0.3603 0.0001 0.0000 0.0000
## PP.Risk_Score_PBPB 0.0706 0.0107 0.0000 0.0000
## PP.Risk_Score_VB 0.1652 0.3741 0.0000 0.0000
## PP.Ben_Score_GFFB 0.2343 0.1599 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.0172 0.6674 0.0005 0.0000
## PP.Ben_Score_CBB 0.6493 0.0000 0.0818 0.0301
## PP.Ben_Score_PBFB 0.0403 0.0000 0.1504 0.5027
## PP.Ben_Score_PBPB 0.0017 0.0000 0.0070 0.1216
## PP.Ben_Score_VB 0.0000 0.0000 0.0087 0.3696
## PP.BehavInt1_GFFB 0.2136 0.8175 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0809 0.5478 0.0000 0.0000
## PP.BehavInt3_GFFB 0.2281 0.9598 0.0000 0.0000
## PP.BehavInt4_GFFB 0.2408 0.7319 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0041 0.0000 0.0014 0.0346
## PP.BehavInt2_GFPRB 0.0137 0.0000 0.0036 0.0536
## PP.BehavInt3_GFPRB 0.0070 0.0000 0.0044 0.0636
## PP.BehavInt4_GFPRB 0.0020 0.0000 0.0054 0.0870
## PP.BehavInt1_CBB 0.7783 0.0000 0.2491 0.1132
## PP.BehavInt2_CBB 0.9660 0.0000 0.1566 0.0572
## PP.BehavInt3_CBB 0.8506 0.0000 0.2007 0.0940
## PP.BehavInt4_CBB 0.8796 0.0000 0.2233 0.0972
## PP.BehavInt1_PBPB 0.0041 0.0000 0.0014 0.0346
## PP.BehavInt2_PBPB 0.0137 0.0000 0.0036 0.0536
## PP.BehavInt3_PBPB 0.0070 0.0000 0.0044 0.0636
## PP.BehavInt4_PBPB 0.0020 0.0000 0.0054 0.0870
## PP.BehavInt1_PBFB 0.0435 0.0000 0.1156 0.3742
## PP.BehavInt2_PBFB 0.1175 0.0000 0.1529 0.4743
## PP.BehavInt3_PBFB 0.0332 0.0000 0.1862 0.5406
## PP.BehavInt4_PBFB 0.0531 0.0000 0.1454 0.4251
## PP.BehavInt1_VB 0.0000 0.0000 0.0016 0.1046
## PP.BehavInt2_VB 0.0003 0.0000 0.0007 0.0423
## PP.BehavInt3_VB 0.0000 0.0000 0.0018 0.0939
## PP.BehavInt4_VB 0.0000 0.0000 0.0020 0.0866
## PP.Nat_1_GFFB 0.4670 0.7369 0.0000 0.0000
## PP.Nat_4R_GFFB 0.2241 0.0000 0.6316 0.8182
## PP.Nat_2R_GFFB 0.0183 0.0000 0.8374 0.8131
## PP.Nat_3R_GFFB 0.2859 0.0000 0.0346 0.0153
## PP.Nat_1_GFPRB 0.0020 0.1476 0.5598 0.1414
## PP.Nat_4R_GFPRB 0.7303 0.0000 0.0157 0.0419
## PP.Nat_2R_GFPRB 0.7766 0.0000 0.1728 0.2410
## PP.Nat_3R_GFPRB 0.7621 0.0000 0.2916 0.1952
## PP.Nat_1_CBB 0.5798 0.0000 0.0263 0.0101
## PP.Nat_4R_CBB 0.0000 0.6215 0.0045 0.0002
## PP.Nat_2R_CBB 0.0000 0.2573 0.4459 0.0108
## PP.Nat_3R_CBB 0.0379 0.9202 0.0314
## PP.Nat_1_PBPB 0.0379 0.0108 0.1381
## PP.Nat_4R_PBPB 0.9202 0.0108 0.0000
## PP.Nat_2R_PBPB 0.0314 0.1381 0.0000
## PP.Nat_3R_PBPB 0.0004 0.0512 0.0000 0.0000
## PP.Nat_1_PBFB 0.3568 0.0000 0.3291 0.6422
## PP.Nat_4R_PBFB 0.0957 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.4927 0.0000 0.0000
## PP.Nat_3R_PBFB 0.0000 0.5114 0.0003 0.0000
## PP.Nat_1_VB 0.0000 0.0000 0.0021 0.1188
## PP.Nat_4R_VB 0.2332 0.5523 0.0000 0.0000
## PP.Nat_2R_VB 0.4941 0.1637 0.0000 0.0000
## PP.Nat_3R_VB 0.9169 0.0021 0.0002 0.0000
## PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB PP.Nat_2R_PBFB
## PP.Risk_Score_GFFB 0.1019 0.0000 0.1378 0.5756
## PP.Risk_Score_GFPRB 0.2990 0.0000 0.2253 0.4337
## PP.Risk_Score_CBB 0.1800 0.4682 0.0000 0.0000
## PP.Risk_Score_PBFB 0.7587 0.0025 0.0000 0.0000
## PP.Risk_Score_PBPB 0.6297 0.2233 0.0000 0.0011
## PP.Risk_Score_VB 0.0845 0.0113 0.0049 0.0021
## PP.Ben_Score_GFFB 0.0000 0.0139 0.0012 0.0000
## PP.Ben_Score_GFPRB 0.0003 0.7514 0.0000 0.0000
## PP.Ben_Score_CBB 0.0000 0.0000 0.2333 0.2638
## PP.Ben_Score_PBFB 0.0023 0.0000 0.0001 0.6257
## PP.Ben_Score_PBPB 0.0044 0.0000 0.0000 0.6953
## PP.Ben_Score_VB 0.0054 0.0000 0.0017 0.6752
## PP.BehavInt1_GFFB 0.0000 0.4976 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.8139 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.3449 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0001 0.6127 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.0164 0.0000 0.0000 0.2879
## PP.BehavInt2_GFPRB 0.0257 0.0000 0.0000 0.3486
## PP.BehavInt3_GFPRB 0.0095 0.0000 0.0000 0.4354
## PP.BehavInt4_GFPRB 0.0078 0.0000 0.0000 0.5432
## PP.BehavInt1_CBB 0.0001 0.0000 0.0604 0.5720
## PP.BehavInt2_CBB 0.0002 0.0000 0.1103 0.4418
## PP.BehavInt3_CBB 0.0002 0.0000 0.0891 0.4966
## PP.BehavInt4_CBB 0.0002 0.0000 0.0727 0.5380
## PP.BehavInt1_PBPB 0.0164 0.0000 0.0000 0.2879
## PP.BehavInt2_PBPB 0.0257 0.0000 0.0000 0.3486
## PP.BehavInt3_PBPB 0.0095 0.0000 0.0000 0.4354
## PP.BehavInt4_PBPB 0.0078 0.0000 0.0000 0.5432
## PP.BehavInt1_PBFB 0.0081 0.0000 0.0000 0.5157
## PP.BehavInt2_PBFB 0.0122 0.0000 0.0001 0.5244
## PP.BehavInt3_PBFB 0.0030 0.0000 0.0002 0.6281
## PP.BehavInt4_PBFB 0.0040 0.0000 0.0001 0.5338
## PP.BehavInt1_VB 0.0211 0.0000 0.0000 0.7690
## PP.BehavInt2_VB 0.1808 0.0000 0.0000 0.5170
## PP.BehavInt3_VB 0.0229 0.0000 0.0001 0.7072
## PP.BehavInt4_VB 0.0273 0.0000 0.0002 0.8037
## PP.Nat_1_GFFB 0.0000 0.5796 0.0000 0.0000
## PP.Nat_4R_GFFB 0.1523 0.0000 0.1543 0.6849
## PP.Nat_2R_GFFB 0.0125 0.0000 0.0739 0.3625
## PP.Nat_3R_GFFB 0.0015 0.0000 0.8364 0.0307
## PP.Nat_1_GFPRB 0.0003 0.0433 0.0044 0.0034
## PP.Nat_4R_GFPRB 0.3522 0.0000 0.9247 0.2110
## PP.Nat_2R_GFPRB 0.6607 0.0000 0.3866 0.6604
## PP.Nat_3R_GFPRB 0.0530 0.0000 0.3631 0.3716
## PP.Nat_1_CBB 0.0035 0.0000 0.3785 0.2842
## PP.Nat_4R_CBB 0.0624 0.2234 0.0000 0.0000
## PP.Nat_2R_CBB 0.0026 0.9870 0.0109 0.0000
## PP.Nat_3R_CBB 0.0004 0.3568 0.0957 0.0000
## PP.Nat_1_PBPB 0.0512 0.0000 0.0000 0.4927
## PP.Nat_4R_PBPB 0.0000 0.3291 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.6422 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0167 0.0093 0.0000
## PP.Nat_1_PBFB 0.0167 0.0000 0.4395
## PP.Nat_4R_PBFB 0.0093 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0000 0.4395 0.0000
## PP.Nat_3R_PBFB 0.0000 0.6461 0.0000 0.0000
## PP.Nat_1_VB 0.0477 0.0000 0.0000 0.6876
## PP.Nat_4R_VB 0.0014 0.2961 0.0000 0.0000
## PP.Nat_2R_VB 0.0006 0.0102 0.0016 0.0000
## PP.Nat_3R_VB 0.0000 0.0000 0.0366 0.0000
## PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB PP.Nat_2R_VB
## PP.Risk_Score_GFFB 0.5479 0.0002 0.0125 0.0016
## PP.Risk_Score_GFPRB 0.6713 0.0013 0.0005 0.0000
## PP.Risk_Score_CBB 0.0017 0.5774 0.0137 0.0219
## PP.Risk_Score_PBFB 0.1244 0.0000 0.0000 0.0000
## PP.Risk_Score_PBPB 0.3723 0.0000 0.0000 0.0000
## PP.Risk_Score_VB 0.1618 0.1547 0.0000 0.0000
## PP.Ben_Score_GFFB 0.0001 0.7350 0.0000 0.0000
## PP.Ben_Score_GFPRB 0.0000 0.8506 0.0067 0.0021
## PP.Ben_Score_CBB 0.1156 0.0024 0.0000 0.0000
## PP.Ben_Score_PBFB 0.2406 0.0000 0.8458 0.0745
## PP.Ben_Score_PBPB 0.1721 0.0000 0.1904 0.6529
## PP.Ben_Score_VB 0.0429 0.0000 0.0103 0.4038
## PP.BehavInt1_GFFB 0.0000 0.4431 0.0000 0.0000
## PP.BehavInt2_GFFB 0.0000 0.4383 0.0000 0.0000
## PP.BehavInt3_GFFB 0.0000 0.5397 0.0000 0.0000
## PP.BehavInt4_GFFB 0.0000 0.3530 0.0000 0.0000
## PP.BehavInt1_GFPRB 0.4519 0.0000 0.0725 0.9355
## PP.BehavInt2_GFPRB 0.5515 0.0000 0.1831 0.7131
## PP.BehavInt3_GFPRB 0.3199 0.0000 0.1789 0.6584
## PP.BehavInt4_GFPRB 0.2242 0.0000 0.1348 0.7905
## PP.BehavInt1_CBB 0.2249 0.0009 0.0002 0.0000
## PP.BehavInt2_CBB 0.2120 0.0026 0.0000 0.0000
## PP.BehavInt3_CBB 0.2171 0.0019 0.0000 0.0000
## PP.BehavInt4_CBB 0.2291 0.0022 0.0000 0.0000
## PP.BehavInt1_PBPB 0.4519 0.0000 0.0725 0.9355
## PP.BehavInt2_PBPB 0.5515 0.0000 0.1831 0.7131
## PP.BehavInt3_PBPB 0.3199 0.0000 0.1789 0.6584
## PP.BehavInt4_PBPB 0.2242 0.0000 0.1348 0.7905
## PP.BehavInt1_PBFB 0.4289 0.0000 0.8756 0.1024
## PP.BehavInt2_PBFB 0.4733 0.0000 0.6219 0.0431
## PP.BehavInt3_PBFB 0.2791 0.0000 0.8524 0.0899
## PP.BehavInt4_PBFB 0.3244 0.0000 0.8594 0.0928
## PP.BehavInt1_VB 0.1786 0.0000 0.0097 0.4262
## PP.BehavInt2_VB 0.4011 0.0000 0.0242 0.6961
## PP.BehavInt3_VB 0.1855 0.0000 0.0055 0.3164
## PP.BehavInt4_VB 0.1916 0.0000 0.0129 0.4848
## PP.Nat_1_GFFB 0.0000 0.2686 0.0000 0.0000
## PP.Nat_4R_GFFB 0.6284 0.0000 0.0088 0.0008
## PP.Nat_2R_GFFB 0.1886 0.0000 0.0701 0.0065
## PP.Nat_3R_GFFB 0.0157 0.0022 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0000 0.7864 0.3453 0.2831
## PP.Nat_4R_GFPRB 0.7018 0.0531 0.0000 0.0000
## PP.Nat_2R_GFPRB 0.9882 0.0076 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.3471 0.0012 0.0000 0.0000
## PP.Nat_1_CBB 0.2878 0.0329 0.0000 0.0000
## PP.Nat_4R_CBB 0.0000 0.1925 0.4421 0.5712
## PP.Nat_2R_CBB 0.0000 0.0008 0.2440 0.4326
## PP.Nat_3R_CBB 0.0000 0.0000 0.2332 0.4941
## PP.Nat_1_PBPB 0.5114 0.0000 0.5523 0.1637
## PP.Nat_4R_PBPB 0.0003 0.0021 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.1188 0.0000 0.0000
## PP.Nat_3R_PBPB 0.0000 0.0477 0.0014 0.0006
## PP.Nat_1_PBFB 0.6461 0.0000 0.2961 0.0102
## PP.Nat_4R_PBFB 0.0000 0.0000 0.0000 0.0016
## PP.Nat_2R_PBFB 0.0000 0.6876 0.0000 0.0000
## PP.Nat_3R_PBFB 0.3365 0.0044 0.0015
## PP.Nat_1_VB 0.3365 0.0031 0.2348
## PP.Nat_4R_VB 0.0044 0.0031 0.0000
## PP.Nat_2R_VB 0.0015 0.2348 0.0000
## PP.Nat_3R_VB 0.0000 0.5576 0.0000 0.0000
## PP.Nat_3R_VB
## PP.Risk_Score_GFFB 0.0022
## PP.Risk_Score_GFPRB 0.0000
## PP.Risk_Score_CBB 0.2097
## PP.Risk_Score_PBFB 0.0347
## PP.Risk_Score_PBPB 0.0011
## PP.Risk_Score_VB 0.0000
## PP.Ben_Score_GFFB 0.0000
## PP.Ben_Score_GFPRB 0.0032
## PP.Ben_Score_CBB 0.0000
## PP.Ben_Score_PBFB 0.0008
## PP.Ben_Score_PBPB 0.0191
## PP.Ben_Score_VB 0.3392
## PP.BehavInt1_GFFB 0.0000
## PP.BehavInt2_GFFB 0.0000
## PP.BehavInt3_GFFB 0.0000
## PP.BehavInt4_GFFB 0.0000
## PP.BehavInt1_GFPRB 0.0951
## PP.BehavInt2_GFPRB 0.0421
## PP.BehavInt3_GFPRB 0.0224
## PP.BehavInt4_GFPRB 0.0419
## PP.BehavInt1_CBB 0.0000
## PP.BehavInt2_CBB 0.0000
## PP.BehavInt3_CBB 0.0000
## PP.BehavInt4_CBB 0.0000
## PP.BehavInt1_PBPB 0.0951
## PP.BehavInt2_PBPB 0.0421
## PP.BehavInt3_PBPB 0.0224
## PP.BehavInt4_PBPB 0.0419
## PP.BehavInt1_PBFB 0.0032
## PP.BehavInt2_PBFB 0.0008
## PP.BehavInt3_PBFB 0.0015
## PP.BehavInt4_PBFB 0.0014
## PP.BehavInt1_VB 0.3345
## PP.BehavInt2_VB 0.2688
## PP.BehavInt3_VB 0.4485
## PP.BehavInt4_VB 0.3164
## PP.Nat_1_GFFB 0.0000
## PP.Nat_4R_GFFB 0.0013
## PP.Nat_2R_GFFB 0.0016
## PP.Nat_3R_GFFB 0.0000
## PP.Nat_1_GFPRB 0.8787
## PP.Nat_4R_GFPRB 0.0000
## PP.Nat_2R_GFPRB 0.0000
## PP.Nat_3R_GFPRB 0.0000
## PP.Nat_1_CBB 0.0000
## PP.Nat_4R_CBB 0.9524
## PP.Nat_2R_CBB 0.6426
## PP.Nat_3R_CBB 0.9169
## PP.Nat_1_PBPB 0.0021
## PP.Nat_4R_PBPB 0.0002
## PP.Nat_2R_PBPB 0.0000
## PP.Nat_3R_PBPB 0.0000
## PP.Nat_1_PBFB 0.0000
## PP.Nat_4R_PBFB 0.0366
## PP.Nat_2R_PBFB 0.0000
## PP.Nat_3R_PBFB 0.0000
## PP.Nat_1_VB 0.5576
## PP.Nat_4R_VB 0.0000
## PP.Nat_2R_VB 0.0000
## PP.Nat_3R_VBlibrary(corrplot)
corrplot(mydata.cor7, method="color")
corrplot(mydata.cor7, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Familiarity/Naturalness
PP$corFN <- data.frame(PP$Nat_1_GFFB , PP$Nat_2R_GFFB , PP$Nat_3R_GFFB , PP$Nat_4R_GFFB, PP$Nat_1_GFPRB , PP$Nat_2R_GFPRB , PP$Nat_3R_GFPRB , PP$Nat_4R_GFPRB, PP$Nat_1_CBB , PP$Nat_2R_CBB , PP$Nat_3R_CBB , PP$Nat_4R_CBB, PP$Nat_1_PBPB , PP$Nat_2R_PBPB , PP$Nat_3R_PBPB , PP$Nat_4R_PBPB, PP$Nat_1_PBFB , PP$Nat_2R_PBFB , PP$Nat_3R_PBFB , PP$Nat_4R_PBFB, PP$Nat_1_VB , PP$Nat_2R_VB , PP$Nat_3R_VB, PP$Nat_4R_VB, PP$FR.GFFB, PP$FR.GFPRB, PP$FR.CBB, PP$FR.PBPB, PP$FR.PBFB, PP$FR.VB)
mydata.corFN = cor(PP$corFN, use = "pairwise.complete.obs")
head(round(mydata.corFN,2))## PP.Nat_1_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB PP.Nat_4R_GFFB
## PP.Nat_1_GFFB 1.00 0.18 -0.15 0.18
## PP.Nat_2R_GFFB 0.18 1.00 0.44 0.61
## PP.Nat_3R_GFFB -0.15 0.44 1.00 0.50
## PP.Nat_4R_GFFB 0.18 0.61 0.50 1.00
## PP.Nat_1_GFPRB 0.42 0.07 0.01 0.15
## PP.Nat_2R_GFPRB -0.03 0.29 0.34 0.49
## PP.Nat_1_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_4R_GFPRB
## PP.Nat_1_GFFB 0.42 -0.03 -0.04 0.04
## PP.Nat_2R_GFFB 0.07 0.29 0.17 0.21
## PP.Nat_3R_GFFB 0.01 0.34 0.49 0.33
## PP.Nat_4R_GFFB 0.15 0.49 0.38 0.47
## PP.Nat_1_GFPRB 1.00 0.25 0.14 0.38
## PP.Nat_2R_GFPRB 0.25 1.00 0.51 0.68
## PP.Nat_1_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_4R_CBB
## PP.Nat_1_GFFB 0.35 0.04 0.00 -0.01
## PP.Nat_2R_GFFB -0.32 0.22 0.21 0.13
## PP.Nat_3R_GFFB -0.41 0.07 0.01 0.08
## PP.Nat_4R_GFFB -0.36 0.14 0.05 0.20
## PP.Nat_1_GFPRB -0.10 -0.13 -0.13 -0.05
## PP.Nat_2R_GFPRB -0.34 -0.07 -0.07 0.00
## PP.Nat_1_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_4R_PBPB
## PP.Nat_1_GFFB 0.14 -0.26 -0.17 -0.21
## PP.Nat_2R_GFFB -0.27 0.04 0.06 0.15
## PP.Nat_3R_GFFB -0.36 0.14 0.10 0.09
## PP.Nat_4R_GFFB -0.23 0.00 -0.04 0.08
## PP.Nat_1_GFPRB -0.04 0.05 -0.33 0.03
## PP.Nat_2R_GFPRB -0.27 0.02 -0.21 0.04
## PP.Nat_1_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_4R_PBFB
## PP.Nat_1_GFFB 0.17 0.28 0.29 0.24
## PP.Nat_2R_GFFB -0.33 -0.11 -0.07 0.05
## PP.Nat_3R_GFFB -0.37 -0.11 -0.15 -0.11
## PP.Nat_4R_GFFB -0.35 0.03 0.05 -0.07
## PP.Nat_1_GFPRB -0.06 0.20 0.28 0.23
## PP.Nat_2R_GFPRB -0.38 0.07 0.09 0.05
## PP.Nat_1_VB PP.Nat_2R_VB PP.Nat_3R_VB PP.Nat_4R_VB PP.FR.GFFB
## PP.Nat_1_GFFB 0.09 -0.22 -0.24 -0.21 0.42
## PP.Nat_2R_GFFB -0.09 0.12 0.12 0.15 0.01
## PP.Nat_3R_GFFB -0.04 0.22 0.20 0.27 -0.08
## PP.Nat_4R_GFFB -0.13 0.13 0.05 0.25 0.09
## PP.Nat_1_GFPRB 0.09 0.07 0.06 0.02 0.42
## PP.Nat_2R_GFPRB -0.16 0.35 0.28 0.24 0.08
## PP.FR.GFPRB PP.FR.CBB PP.FR.PBPB PP.FR.PBFB PP.FR.VB
## PP.Nat_1_GFFB 0.25 0.28 0.06 0.17 0.20
## PP.Nat_2R_GFFB -0.02 -0.33 -0.35 -0.35 -0.02
## PP.Nat_3R_GFFB 0.09 -0.39 -0.19 -0.40 -0.01
## PP.Nat_4R_GFFB 0.08 -0.33 -0.20 -0.36 -0.14
## PP.Nat_1_GFPRB 0.53 0.05 0.09 -0.08 0.29
## PP.Nat_2R_GFPRB 0.24 -0.39 -0.02 -0.30 -0.13library("Hmisc")
mydata.rcorrFN = rcorr(as.matrix(mydata.corFN))
mydata.rcorrFN## PP.Nat_1_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB PP.Nat_4R_GFFB
## PP.Nat_1_GFFB 1.00 -0.16 -0.46 -0.13
## PP.Nat_2R_GFFB -0.16 1.00 0.80 0.88
## PP.Nat_3R_GFFB -0.46 0.80 1.00 0.84
## PP.Nat_4R_GFFB -0.13 0.88 0.84 1.00
## PP.Nat_1_GFPRB 0.51 0.08 0.05 0.23
## PP.Nat_2R_GFPRB -0.24 0.63 0.75 0.82
## PP.Nat_3R_GFPRB -0.32 0.62 0.84 0.78
## PP.Nat_4R_GFPRB -0.24 0.60 0.75 0.79
## PP.Nat_1_CBB 0.48 -0.71 -0.84 -0.76
## PP.Nat_2R_CBB -0.23 0.26 0.09 0.10
## PP.Nat_3R_CBB -0.25 0.28 0.11 0.10
## PP.Nat_4R_CBB -0.27 0.21 0.10 0.14
## PP.Nat_1_PBPB 0.18 -0.74 -0.76 -0.75
## PP.Nat_2R_PBPB -0.72 0.17 0.28 0.06
## PP.Nat_3R_PBPB -0.65 0.26 0.31 0.08
## PP.Nat_4R_PBPB -0.65 0.17 0.25 0.10
## PP.Nat_1_PBFB 0.24 -0.76 -0.80 -0.82
## PP.Nat_2R_PBFB 0.63 -0.24 -0.30 -0.10
## PP.Nat_3R_PBFB 0.65 -0.24 -0.33 -0.09
## PP.Nat_4R_PBFB 0.52 0.02 -0.12 0.04
## PP.Nat_1_VB 0.09 -0.54 -0.43 -0.54
## PP.Nat_2R_VB -0.67 0.41 0.62 0.44
## PP.Nat_3R_VB -0.68 0.38 0.58 0.34
## PP.Nat_4R_VB -0.69 0.33 0.55 0.37
## PP.FR.GFFB 0.74 -0.31 -0.38 -0.16
## PP.FR.GFPRB 0.44 -0.21 -0.09 -0.04
## PP.FR.CBB 0.50 -0.78 -0.87 -0.80
## PP.FR.PBPB 0.26 -0.79 -0.69 -0.69
## PP.FR.PBFB 0.38 -0.81 -0.82 -0.81
## PP.FR.VB 0.38 -0.52 -0.46 -0.51
## PP.Nat_1_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_4R_GFPRB
## PP.Nat_1_GFFB 0.51 -0.24 -0.32 -0.24
## PP.Nat_2R_GFFB 0.08 0.63 0.62 0.60
## PP.Nat_3R_GFFB 0.05 0.75 0.84 0.75
## PP.Nat_4R_GFFB 0.23 0.82 0.78 0.79
## PP.Nat_1_GFPRB 1.00 0.40 0.26 0.47
## PP.Nat_2R_GFPRB 0.40 1.00 0.88 0.94
## PP.Nat_3R_GFPRB 0.26 0.88 1.00 0.87
## PP.Nat_4R_GFPRB 0.47 0.94 0.87 1.00
## PP.Nat_1_CBB -0.19 -0.80 -0.84 -0.79
## PP.Nat_2R_CBB -0.51 -0.20 -0.13 -0.17
## PP.Nat_3R_CBB -0.49 -0.15 -0.05 -0.14
## PP.Nat_4R_CBB -0.43 -0.11 -0.09 -0.05
## PP.Nat_1_PBPB -0.22 -0.75 -0.77 -0.69
## PP.Nat_2R_PBPB -0.37 0.01 0.07 0.11
## PP.Nat_3R_PBPB -0.61 -0.07 0.11 -0.01
## PP.Nat_4R_PBPB -0.30 0.04 0.04 0.17
## PP.Nat_1_PBFB -0.26 -0.83 -0.82 -0.79
## PP.Nat_2R_PBFB 0.52 0.01 -0.09 -0.08
## PP.Nat_3R_PBFB 0.59 0.02 -0.12 -0.04
## PP.Nat_4R_PBFB 0.52 0.15 0.07 0.06
## PP.Nat_1_VB 0.04 -0.45 -0.48 -0.39
## PP.Nat_2R_VB -0.01 0.59 0.61 0.64
## PP.Nat_3R_VB -0.12 0.49 0.54 0.51
## PP.Nat_4R_VB -0.12 0.43 0.46 0.51
## PP.FR.GFFB 0.64 -0.08 -0.20 -0.05
## PP.FR.GFPRB 0.78 0.22 0.10 0.26
## PP.FR.CBB -0.05 -0.80 -0.85 -0.78
## PP.FR.PBPB 0.02 -0.53 -0.61 -0.52
## PP.FR.PBFB -0.06 -0.71 -0.76 -0.71
## PP.FR.VB 0.34 -0.38 -0.40 -0.30
## PP.Nat_1_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_4R_CBB
## PP.Nat_1_GFFB 0.48 -0.23 -0.25 -0.27
## PP.Nat_2R_GFFB -0.71 0.26 0.28 0.21
## PP.Nat_3R_GFFB -0.84 0.09 0.11 0.10
## PP.Nat_4R_GFFB -0.76 0.10 0.10 0.14
## PP.Nat_1_GFPRB -0.19 -0.51 -0.49 -0.43
## PP.Nat_2R_GFPRB -0.80 -0.20 -0.15 -0.11
## PP.Nat_3R_GFPRB -0.84 -0.13 -0.05 -0.09
## PP.Nat_4R_GFPRB -0.79 -0.17 -0.14 -0.05
## PP.Nat_1_CBB 1.00 0.17 0.08 0.17
## PP.Nat_2R_CBB 0.17 1.00 0.92 0.90
## PP.Nat_3R_CBB 0.08 0.92 1.00 0.83
## PP.Nat_4R_CBB 0.17 0.90 0.83 1.00
## PP.Nat_1_PBPB 0.75 -0.04 -0.12 0.00
## PP.Nat_2R_PBPB -0.28 0.49 0.47 0.43
## PP.Nat_3R_PBPB -0.27 0.47 0.48 0.37
## PP.Nat_4R_PBPB -0.26 0.25 0.19 0.34
## PP.Nat_1_PBFB 0.85 0.07 0.03 0.10
## PP.Nat_2R_PBFB 0.14 -0.67 -0.65 -0.65
## PP.Nat_3R_PBFB 0.15 -0.59 -0.57 -0.53
## PP.Nat_4R_PBFB -0.11 -0.50 -0.44 -0.58
## PP.Nat_1_VB 0.40 -0.36 -0.41 -0.29
## PP.Nat_2R_VB -0.73 -0.05 -0.03 0.03
## PP.Nat_3R_VB -0.65 -0.03 0.00 0.00
## PP.Nat_4R_VB -0.59 -0.04 -0.03 0.09
## PP.FR.GFFB 0.34 -0.58 -0.64 -0.51
## PP.FR.GFPRB 0.06 -0.63 -0.61 -0.54
## PP.FR.CBB 0.93 -0.06 -0.13 -0.06
## PP.FR.PBPB 0.70 -0.19 -0.28 -0.12
## PP.FR.PBFB 0.80 -0.24 -0.30 -0.23
## PP.FR.VB 0.43 -0.48 -0.53 -0.41
## PP.Nat_1_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_4R_PBPB
## PP.Nat_1_GFFB 0.18 -0.72 -0.65 -0.65
## PP.Nat_2R_GFFB -0.74 0.17 0.26 0.17
## PP.Nat_3R_GFFB -0.76 0.28 0.31 0.25
## PP.Nat_4R_GFFB -0.75 0.06 0.08 0.10
## PP.Nat_1_GFPRB -0.22 -0.37 -0.61 -0.30
## PP.Nat_2R_GFPRB -0.75 0.01 -0.07 0.04
## PP.Nat_3R_GFPRB -0.77 0.07 0.11 0.04
## PP.Nat_4R_GFPRB -0.69 0.11 -0.01 0.17
## PP.Nat_1_CBB 0.75 -0.28 -0.27 -0.26
## PP.Nat_2R_CBB -0.04 0.49 0.47 0.25
## PP.Nat_3R_CBB -0.12 0.47 0.48 0.19
## PP.Nat_4R_CBB 0.00 0.43 0.37 0.34
## PP.Nat_1_PBPB 1.00 0.02 -0.13 0.14
## PP.Nat_2R_PBPB 0.02 1.00 0.78 0.79
## PP.Nat_3R_PBPB -0.13 0.78 1.00 0.67
## PP.Nat_4R_PBPB 0.14 0.79 0.67 1.00
## PP.Nat_1_PBFB 0.92 -0.05 -0.15 -0.03
## PP.Nat_2R_PBFB 0.02 -0.81 -0.76 -0.63
## PP.Nat_3R_PBFB 0.05 -0.75 -0.86 -0.60
## PP.Nat_4R_PBFB -0.35 -0.62 -0.55 -0.66
## PP.Nat_1_VB 0.75 -0.06 -0.23 0.13
## PP.Nat_2R_VB -0.39 0.53 0.45 0.52
## PP.Nat_3R_VB -0.45 0.48 0.60 0.47
## PP.Nat_4R_VB -0.14 0.60 0.49 0.72
## PP.FR.GFFB 0.27 -0.68 -0.75 -0.46
## PP.FR.GFPRB 0.06 -0.44 -0.71 -0.34
## PP.FR.CBB 0.79 -0.35 -0.39 -0.30
## PP.FR.PBPB 0.82 -0.21 -0.45 -0.13
## PP.FR.PBFB 0.83 -0.32 -0.41 -0.25
## PP.FR.VB 0.60 -0.36 -0.55 -0.15
## PP.Nat_1_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_4R_PBFB
## PP.Nat_1_GFFB 0.24 0.63 0.65 0.52
## PP.Nat_2R_GFFB -0.76 -0.24 -0.24 0.02
## PP.Nat_3R_GFFB -0.80 -0.30 -0.33 -0.12
## PP.Nat_4R_GFFB -0.82 -0.10 -0.09 0.04
## PP.Nat_1_GFPRB -0.26 0.52 0.59 0.52
## PP.Nat_2R_GFPRB -0.83 0.01 0.02 0.15
## PP.Nat_3R_GFPRB -0.82 -0.09 -0.12 0.07
## PP.Nat_4R_GFPRB -0.79 -0.08 -0.04 0.06
## PP.Nat_1_CBB 0.85 0.14 0.15 -0.11
## PP.Nat_2R_CBB 0.07 -0.67 -0.59 -0.50
## PP.Nat_3R_CBB 0.03 -0.65 -0.57 -0.44
## PP.Nat_4R_CBB 0.10 -0.65 -0.53 -0.58
## PP.Nat_1_PBPB 0.92 0.02 0.05 -0.35
## PP.Nat_2R_PBPB -0.05 -0.81 -0.75 -0.62
## PP.Nat_3R_PBPB -0.15 -0.76 -0.86 -0.55
## PP.Nat_4R_PBPB -0.03 -0.63 -0.60 -0.66
## PP.Nat_1_PBFB 1.00 -0.03 0.02 -0.33
## PP.Nat_2R_PBFB -0.03 1.00 0.89 0.80
## PP.Nat_3R_PBFB 0.02 0.89 1.00 0.76
## PP.Nat_4R_PBFB -0.33 0.80 0.76 1.00
## PP.Nat_1_VB 0.68 0.09 0.10 -0.27
## PP.Nat_2R_VB -0.45 -0.51 -0.48 -0.36
## PP.Nat_3R_VB -0.46 -0.53 -0.61 -0.35
## PP.Nat_4R_VB -0.28 -0.58 -0.53 -0.57
## PP.FR.GFFB 0.21 0.72 0.73 0.49
## PP.FR.GFPRB 0.02 0.51 0.59 0.35
## PP.FR.CBB 0.85 0.27 0.29 0.01
## PP.FR.PBPB 0.76 0.22 0.27 -0.13
## PP.FR.PBFB 0.84 0.27 0.29 -0.02
## PP.FR.VB 0.54 0.40 0.44 0.07
## PP.Nat_1_VB PP.Nat_2R_VB PP.Nat_3R_VB PP.Nat_4R_VB PP.FR.GFFB
## PP.Nat_1_GFFB 0.09 -0.67 -0.68 -0.69 0.74
## PP.Nat_2R_GFFB -0.54 0.41 0.38 0.33 -0.31
## PP.Nat_3R_GFFB -0.43 0.62 0.58 0.55 -0.38
## PP.Nat_4R_GFFB -0.54 0.44 0.34 0.37 -0.16
## PP.Nat_1_GFPRB 0.04 -0.01 -0.12 -0.12 0.64
## PP.Nat_2R_GFPRB -0.45 0.59 0.49 0.43 -0.08
## PP.Nat_3R_GFPRB -0.48 0.61 0.54 0.46 -0.20
## PP.Nat_4R_GFPRB -0.39 0.64 0.51 0.51 -0.05
## PP.Nat_1_CBB 0.40 -0.73 -0.65 -0.59 0.34
## PP.Nat_2R_CBB -0.36 -0.05 -0.03 -0.04 -0.58
## PP.Nat_3R_CBB -0.41 -0.03 0.00 -0.03 -0.64
## PP.Nat_4R_CBB -0.29 0.03 0.00 0.09 -0.51
## PP.Nat_1_PBPB 0.75 -0.39 -0.45 -0.14 0.27
## PP.Nat_2R_PBPB -0.06 0.53 0.48 0.60 -0.68
## PP.Nat_3R_PBPB -0.23 0.45 0.60 0.49 -0.75
## PP.Nat_4R_PBPB 0.13 0.52 0.47 0.72 -0.46
## PP.Nat_1_PBFB 0.68 -0.45 -0.46 -0.28 0.21
## PP.Nat_2R_PBFB 0.09 -0.51 -0.53 -0.58 0.72
## PP.Nat_3R_PBFB 0.10 -0.48 -0.61 -0.53 0.73
## PP.Nat_4R_PBFB -0.27 -0.36 -0.35 -0.57 0.49
## PP.Nat_1_VB 1.00 0.00 -0.12 0.17 0.34
## PP.Nat_2R_VB 0.00 1.00 0.83 0.88 -0.45
## PP.Nat_3R_VB -0.12 0.83 1.00 0.76 -0.59
## PP.Nat_4R_VB 0.17 0.88 0.76 1.00 -0.47
## PP.FR.GFFB 0.34 -0.45 -0.59 -0.47 1.00
## PP.FR.GFPRB 0.35 -0.09 -0.22 -0.11 0.70
## PP.FR.CBB 0.52 -0.72 -0.63 -0.56 0.46
## PP.FR.PBPB 0.66 -0.47 -0.52 -0.27 0.44
## PP.FR.PBFB 0.64 -0.56 -0.52 -0.42 0.45
## PP.FR.VB 0.75 -0.27 -0.37 -0.16 0.58
## PP.FR.GFPRB PP.FR.CBB PP.FR.PBPB PP.FR.PBFB PP.FR.VB
## PP.Nat_1_GFFB 0.44 0.50 0.26 0.38 0.38
## PP.Nat_2R_GFFB -0.21 -0.78 -0.79 -0.81 -0.52
## PP.Nat_3R_GFFB -0.09 -0.87 -0.69 -0.82 -0.46
## PP.Nat_4R_GFFB -0.04 -0.80 -0.69 -0.81 -0.51
## PP.Nat_1_GFPRB 0.78 -0.05 0.02 -0.06 0.34
## PP.Nat_2R_GFPRB 0.22 -0.80 -0.53 -0.71 -0.38
## PP.Nat_3R_GFPRB 0.10 -0.85 -0.61 -0.76 -0.40
## PP.Nat_4R_GFPRB 0.26 -0.78 -0.52 -0.71 -0.30
## PP.Nat_1_CBB 0.06 0.93 0.70 0.80 0.43
## PP.Nat_2R_CBB -0.63 -0.06 -0.19 -0.24 -0.48
## PP.Nat_3R_CBB -0.61 -0.13 -0.28 -0.30 -0.53
## PP.Nat_4R_CBB -0.54 -0.06 -0.12 -0.23 -0.41
## PP.Nat_1_PBPB 0.06 0.79 0.82 0.83 0.60
## PP.Nat_2R_PBPB -0.44 -0.35 -0.21 -0.32 -0.36
## PP.Nat_3R_PBPB -0.71 -0.39 -0.45 -0.41 -0.55
## PP.Nat_4R_PBPB -0.34 -0.30 -0.13 -0.25 -0.15
## PP.Nat_1_PBFB 0.02 0.85 0.76 0.84 0.54
## PP.Nat_2R_PBFB 0.51 0.27 0.22 0.27 0.40
## PP.Nat_3R_PBFB 0.59 0.29 0.27 0.29 0.44
## PP.Nat_4R_PBFB 0.35 0.01 -0.13 -0.02 0.07
## PP.Nat_1_VB 0.35 0.52 0.66 0.64 0.75
## PP.Nat_2R_VB -0.09 -0.72 -0.47 -0.56 -0.27
## PP.Nat_3R_VB -0.22 -0.63 -0.52 -0.52 -0.37
## PP.Nat_4R_VB -0.11 -0.56 -0.27 -0.42 -0.16
## PP.FR.GFFB 0.70 0.46 0.44 0.45 0.58
## PP.FR.GFPRB 1.00 0.21 0.39 0.30 0.64
## PP.FR.CBB 0.21 1.00 0.80 0.89 0.57
## PP.FR.PBPB 0.39 0.80 1.00 0.90 0.73
## PP.FR.PBFB 0.30 0.89 0.90 1.00 0.74
## PP.FR.VB 0.64 0.57 0.73 0.74 1.00
##
## n= 30
##
##
## P
## PP.Nat_1_GFFB PP.Nat_2R_GFFB PP.Nat_3R_GFFB PP.Nat_4R_GFFB
## PP.Nat_1_GFFB 0.4131 0.0110 0.4936
## PP.Nat_2R_GFFB 0.4131 0.0000 0.0000
## PP.Nat_3R_GFFB 0.0110 0.0000 0.0000
## PP.Nat_4R_GFFB 0.4936 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0037 0.6814 0.7791 0.2308
## PP.Nat_2R_GFPRB 0.2061 0.0002 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.0891 0.0002 0.0000 0.0000
## PP.Nat_4R_GFPRB 0.1981 0.0004 0.0000 0.0000
## PP.Nat_1_CBB 0.0066 0.0000 0.0000 0.0000
## PP.Nat_2R_CBB 0.2242 0.1703 0.6348 0.6156
## PP.Nat_3R_CBB 0.1826 0.1299 0.5534 0.5862
## PP.Nat_4R_CBB 0.1501 0.2684 0.5845 0.4487
## PP.Nat_1_PBPB 0.3376 0.0000 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0000 0.3720 0.1297 0.7568
## PP.Nat_3R_PBPB 0.0001 0.1637 0.0979 0.6597
## PP.Nat_4R_PBPB 0.0000 0.3701 0.1875 0.6092
## PP.Nat_1_PBFB 0.1963 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0002 0.1964 0.1066 0.5815
## PP.Nat_3R_PBFB 0.0000 0.2077 0.0743 0.6478
## PP.Nat_4R_PBFB 0.0030 0.9273 0.5187 0.8535
## PP.Nat_1_VB 0.6258 0.0023 0.0171 0.0020
## PP.Nat_2R_VB 0.0000 0.0245 0.0002 0.0158
## PP.Nat_3R_VB 0.0000 0.0379 0.0008 0.0636
## PP.Nat_4R_VB 0.0000 0.0762 0.0015 0.0422
## PP.FR.GFFB 0.0000 0.0985 0.0364 0.3944
## PP.FR.GFPRB 0.0148 0.2621 0.6310 0.8509
## PP.FR.CBB 0.0049 0.0000 0.0000 0.0000
## PP.FR.PBPB 0.1713 0.0000 0.0000 0.0000
## PP.FR.PBFB 0.0380 0.0000 0.0000 0.0000
## PP.FR.VB 0.0406 0.0033 0.0111 0.0044
## PP.Nat_1_GFPRB PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_4R_GFPRB
## PP.Nat_1_GFFB 0.0037 0.2061 0.0891 0.1981
## PP.Nat_2R_GFFB 0.6814 0.0002 0.0002 0.0004
## PP.Nat_3R_GFFB 0.7791 0.0000 0.0000 0.0000
## PP.Nat_4R_GFFB 0.2308 0.0000 0.0000 0.0000
## PP.Nat_1_GFPRB 0.0274 0.1590 0.0090
## PP.Nat_2R_GFPRB 0.0274 0.0000 0.0000
## PP.Nat_3R_GFPRB 0.1590 0.0000 0.0000
## PP.Nat_4R_GFPRB 0.0090 0.0000 0.0000
## PP.Nat_1_CBB 0.3105 0.0000 0.0000 0.0000
## PP.Nat_2R_CBB 0.0041 0.3014 0.4907 0.3621
## PP.Nat_3R_CBB 0.0056 0.4213 0.8037 0.4663
## PP.Nat_4R_CBB 0.0182 0.5765 0.6514 0.8097
## PP.Nat_1_PBPB 0.2446 0.0000 0.0000 0.0000
## PP.Nat_2R_PBPB 0.0449 0.9376 0.7175 0.5483
## PP.Nat_3R_PBPB 0.0003 0.7122 0.5753 0.9688
## PP.Nat_4R_PBPB 0.1087 0.8509 0.8354 0.3568
## PP.Nat_1_PBFB 0.1603 0.0000 0.0000 0.0000
## PP.Nat_2R_PBFB 0.0032 0.9478 0.6240 0.6832
## PP.Nat_3R_PBFB 0.0006 0.9051 0.5321 0.8529
## PP.Nat_4R_PBFB 0.0032 0.4238 0.6967 0.7454
## PP.Nat_1_VB 0.8487 0.0118 0.0068 0.0327
## PP.Nat_2R_VB 0.9488 0.0006 0.0004 0.0001
## PP.Nat_3R_VB 0.5124 0.0065 0.0023 0.0042
## PP.Nat_4R_VB 0.5111 0.0173 0.0113 0.0039
## PP.FR.GFFB 0.0001 0.6799 0.2939 0.8038
## PP.FR.GFPRB 0.0000 0.2519 0.5921 0.1694
## PP.FR.CBB 0.7877 0.0000 0.0000 0.0000
## PP.FR.PBPB 0.9216 0.0024 0.0003 0.0034
## PP.FR.PBFB 0.7664 0.0000 0.0000 0.0000
## PP.FR.VB 0.0659 0.0385 0.0303 0.1084
## PP.Nat_1_CBB PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_4R_CBB
## PP.Nat_1_GFFB 0.0066 0.2242 0.1826 0.1501
## PP.Nat_2R_GFFB 0.0000 0.1703 0.1299 0.2684
## PP.Nat_3R_GFFB 0.0000 0.6348 0.5534 0.5845
## PP.Nat_4R_GFFB 0.0000 0.6156 0.5862 0.4487
## PP.Nat_1_GFPRB 0.3105 0.0041 0.0056 0.0182
## PP.Nat_2R_GFPRB 0.0000 0.3014 0.4213 0.5765
## PP.Nat_3R_GFPRB 0.0000 0.4907 0.8037 0.6514
## PP.Nat_4R_GFPRB 0.0000 0.3621 0.4663 0.8097
## PP.Nat_1_CBB 0.3835 0.6689 0.3835
## PP.Nat_2R_CBB 0.3835 0.0000 0.0000
## PP.Nat_3R_CBB 0.6689 0.0000 0.0000
## PP.Nat_4R_CBB 0.3835 0.0000 0.0000
## PP.Nat_1_PBPB 0.0000 0.8172 0.5348 0.9969
## PP.Nat_2R_PBPB 0.1295 0.0065 0.0086 0.0166
## PP.Nat_3R_PBPB 0.1463 0.0087 0.0073 0.0470
## PP.Nat_4R_PBPB 0.1685 0.1917 0.3088 0.0678
## PP.Nat_1_PBFB 0.0000 0.7019 0.8645 0.6167
## PP.Nat_2R_PBFB 0.4730 0.0000 0.0000 0.0000
## PP.Nat_3R_PBFB 0.4265 0.0006 0.0010 0.0029
## PP.Nat_4R_PBFB 0.5511 0.0050 0.0143 0.0008
## PP.Nat_1_VB 0.0285 0.0487 0.0247 0.1143
## PP.Nat_2R_VB 0.0000 0.7992 0.8947 0.8869
## PP.Nat_3R_VB 0.0001 0.8935 0.9949 0.9868
## PP.Nat_4R_VB 0.0006 0.8508 0.8871 0.6247
## PP.FR.GFFB 0.0645 0.0008 0.0001 0.0041
## PP.FR.GFPRB 0.7672 0.0002 0.0003 0.0023
## PP.FR.CBB 0.0000 0.7534 0.4838 0.7334
## PP.FR.PBPB 0.0000 0.3034 0.1365 0.5349
## PP.FR.PBFB 0.0000 0.2092 0.1024 0.2276
## PP.FR.VB 0.0190 0.0076 0.0025 0.0254
## PP.Nat_1_PBPB PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_4R_PBPB
## PP.Nat_1_GFFB 0.3376 0.0000 0.0001 0.0000
## PP.Nat_2R_GFFB 0.0000 0.3720 0.1637 0.3701
## PP.Nat_3R_GFFB 0.0000 0.1297 0.0979 0.1875
## PP.Nat_4R_GFFB 0.0000 0.7568 0.6597 0.6092
## PP.Nat_1_GFPRB 0.2446 0.0449 0.0003 0.1087
## PP.Nat_2R_GFPRB 0.0000 0.9376 0.7122 0.8509
## PP.Nat_3R_GFPRB 0.0000 0.7175 0.5753 0.8354
## PP.Nat_4R_GFPRB 0.0000 0.5483 0.9688 0.3568
## PP.Nat_1_CBB 0.0000 0.1295 0.1463 0.1685
## PP.Nat_2R_CBB 0.8172 0.0065 0.0087 0.1917
## PP.Nat_3R_CBB 0.5348 0.0086 0.0073 0.3088
## PP.Nat_4R_CBB 0.9969 0.0166 0.0470 0.0678
## PP.Nat_1_PBPB 0.8961 0.4822 0.4477
## PP.Nat_2R_PBPB 0.8961 0.0000 0.0000
## PP.Nat_3R_PBPB 0.4822 0.0000 0.0000
## PP.Nat_4R_PBPB 0.4477 0.0000 0.0000
## PP.Nat_1_PBFB 0.0000 0.7902 0.4343 0.8715
## PP.Nat_2R_PBFB 0.9282 0.0000 0.0000 0.0002
## PP.Nat_3R_PBFB 0.7898 0.0000 0.0000 0.0005
## PP.Nat_4R_PBFB 0.0606 0.0003 0.0016 0.0000
## PP.Nat_1_VB 0.0000 0.7408 0.2193 0.4959
## PP.Nat_2R_VB 0.0331 0.0025 0.0136 0.0033
## PP.Nat_3R_VB 0.0120 0.0073 0.0005 0.0092
## PP.Nat_4R_VB 0.4566 0.0005 0.0059 0.0000
## PP.FR.GFFB 0.1508 0.0000 0.0000 0.0106
## PP.FR.GFPRB 0.7510 0.0145 0.0000 0.0703
## PP.FR.CBB 0.0000 0.0586 0.0343 0.1120
## PP.FR.PBPB 0.0000 0.2545 0.0130 0.5032
## PP.FR.PBFB 0.0000 0.0874 0.0252 0.1847
## PP.FR.VB 0.0005 0.0512 0.0016 0.4409
## PP.Nat_1_PBFB PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_4R_PBFB
## PP.Nat_1_GFFB 0.1963 0.0002 0.0000 0.0030
## PP.Nat_2R_GFFB 0.0000 0.1964 0.2077 0.9273
## PP.Nat_3R_GFFB 0.0000 0.1066 0.0743 0.5187
## PP.Nat_4R_GFFB 0.0000 0.5815 0.6478 0.8535
## PP.Nat_1_GFPRB 0.1603 0.0032 0.0006 0.0032
## PP.Nat_2R_GFPRB 0.0000 0.9478 0.9051 0.4238
## PP.Nat_3R_GFPRB 0.0000 0.6240 0.5321 0.6967
## PP.Nat_4R_GFPRB 0.0000 0.6832 0.8529 0.7454
## PP.Nat_1_CBB 0.0000 0.4730 0.4265 0.5511
## PP.Nat_2R_CBB 0.7019 0.0000 0.0006 0.0050
## PP.Nat_3R_CBB 0.8645 0.0000 0.0010 0.0143
## PP.Nat_4R_CBB 0.6167 0.0000 0.0029 0.0008
## PP.Nat_1_PBPB 0.0000 0.9282 0.7898 0.0606
## PP.Nat_2R_PBPB 0.7902 0.0000 0.0000 0.0003
## PP.Nat_3R_PBPB 0.4343 0.0000 0.0000 0.0016
## PP.Nat_4R_PBPB 0.8715 0.0002 0.0005 0.0000
## PP.Nat_1_PBFB 0.8543 0.9165 0.0752
## PP.Nat_2R_PBFB 0.8543 0.0000 0.0000
## PP.Nat_3R_PBFB 0.9165 0.0000 0.0000
## PP.Nat_4R_PBFB 0.0752 0.0000 0.0000
## PP.Nat_1_VB 0.0000 0.6184 0.5906 0.1497
## PP.Nat_2R_VB 0.0132 0.0042 0.0070 0.0516
## PP.Nat_3R_VB 0.0099 0.0029 0.0003 0.0564
## PP.Nat_4R_VB 0.1349 0.0008 0.0024 0.0011
## PP.FR.GFFB 0.2705 0.0000 0.0000 0.0058
## PP.FR.GFPRB 0.9025 0.0038 0.0006 0.0574
## PP.FR.CBB 0.0000 0.1512 0.1144 0.9669
## PP.FR.PBPB 0.0000 0.2467 0.1506 0.4894
## PP.FR.PBFB 0.0000 0.1519 0.1140 0.9038
## PP.FR.VB 0.0023 0.0287 0.0158 0.7094
## PP.Nat_1_VB PP.Nat_2R_VB PP.Nat_3R_VB PP.Nat_4R_VB PP.FR.GFFB
## PP.Nat_1_GFFB 0.6258 0.0000 0.0000 0.0000 0.0000
## PP.Nat_2R_GFFB 0.0023 0.0245 0.0379 0.0762 0.0985
## PP.Nat_3R_GFFB 0.0171 0.0002 0.0008 0.0015 0.0364
## PP.Nat_4R_GFFB 0.0020 0.0158 0.0636 0.0422 0.3944
## PP.Nat_1_GFPRB 0.8487 0.9488 0.5124 0.5111 0.0001
## PP.Nat_2R_GFPRB 0.0118 0.0006 0.0065 0.0173 0.6799
## PP.Nat_3R_GFPRB 0.0068 0.0004 0.0023 0.0113 0.2939
## PP.Nat_4R_GFPRB 0.0327 0.0001 0.0042 0.0039 0.8038
## PP.Nat_1_CBB 0.0285 0.0000 0.0001 0.0006 0.0645
## PP.Nat_2R_CBB 0.0487 0.7992 0.8935 0.8508 0.0008
## PP.Nat_3R_CBB 0.0247 0.8947 0.9949 0.8871 0.0001
## PP.Nat_4R_CBB 0.1143 0.8869 0.9868 0.6247 0.0041
## PP.Nat_1_PBPB 0.0000 0.0331 0.0120 0.4566 0.1508
## PP.Nat_2R_PBPB 0.7408 0.0025 0.0073 0.0005 0.0000
## PP.Nat_3R_PBPB 0.2193 0.0136 0.0005 0.0059 0.0000
## PP.Nat_4R_PBPB 0.4959 0.0033 0.0092 0.0000 0.0106
## PP.Nat_1_PBFB 0.0000 0.0132 0.0099 0.1349 0.2705
## PP.Nat_2R_PBFB 0.6184 0.0042 0.0029 0.0008 0.0000
## PP.Nat_3R_PBFB 0.5906 0.0070 0.0003 0.0024 0.0000
## PP.Nat_4R_PBFB 0.1497 0.0516 0.0564 0.0011 0.0058
## PP.Nat_1_VB 0.9812 0.5148 0.3695 0.0700
## PP.Nat_2R_VB 0.9812 0.0000 0.0000 0.0125
## PP.Nat_3R_VB 0.5148 0.0000 0.0000 0.0006
## PP.Nat_4R_VB 0.3695 0.0000 0.0000 0.0091
## PP.FR.GFFB 0.0700 0.0125 0.0006 0.0091
## PP.FR.GFPRB 0.0558 0.6318 0.2444 0.5565 0.0000
## PP.FR.CBB 0.0030 0.0000 0.0002 0.0012 0.0101
## PP.FR.PBPB 0.0000 0.0091 0.0029 0.1435 0.0156
## PP.FR.PBFB 0.0002 0.0013 0.0033 0.0211 0.0124
## PP.FR.VB 0.0000 0.1416 0.0470 0.3893 0.0008
## PP.FR.GFPRB PP.FR.CBB PP.FR.PBPB PP.FR.PBFB PP.FR.VB
## PP.Nat_1_GFFB 0.0148 0.0049 0.1713 0.0380 0.0406
## PP.Nat_2R_GFFB 0.2621 0.0000 0.0000 0.0000 0.0033
## PP.Nat_3R_GFFB 0.6310 0.0000 0.0000 0.0000 0.0111
## PP.Nat_4R_GFFB 0.8509 0.0000 0.0000 0.0000 0.0044
## PP.Nat_1_GFPRB 0.0000 0.7877 0.9216 0.7664 0.0659
## PP.Nat_2R_GFPRB 0.2519 0.0000 0.0024 0.0000 0.0385
## PP.Nat_3R_GFPRB 0.5921 0.0000 0.0003 0.0000 0.0303
## PP.Nat_4R_GFPRB 0.1694 0.0000 0.0034 0.0000 0.1084
## PP.Nat_1_CBB 0.7672 0.0000 0.0000 0.0000 0.0190
## PP.Nat_2R_CBB 0.0002 0.7534 0.3034 0.2092 0.0076
## PP.Nat_3R_CBB 0.0003 0.4838 0.1365 0.1024 0.0025
## PP.Nat_4R_CBB 0.0023 0.7334 0.5349 0.2276 0.0254
## PP.Nat_1_PBPB 0.7510 0.0000 0.0000 0.0000 0.0005
## PP.Nat_2R_PBPB 0.0145 0.0586 0.2545 0.0874 0.0512
## PP.Nat_3R_PBPB 0.0000 0.0343 0.0130 0.0252 0.0016
## PP.Nat_4R_PBPB 0.0703 0.1120 0.5032 0.1847 0.4409
## PP.Nat_1_PBFB 0.9025 0.0000 0.0000 0.0000 0.0023
## PP.Nat_2R_PBFB 0.0038 0.1512 0.2467 0.1519 0.0287
## PP.Nat_3R_PBFB 0.0006 0.1144 0.1506 0.1140 0.0158
## PP.Nat_4R_PBFB 0.0574 0.9669 0.4894 0.9038 0.7094
## PP.Nat_1_VB 0.0558 0.0030 0.0000 0.0002 0.0000
## PP.Nat_2R_VB 0.6318 0.0000 0.0091 0.0013 0.1416
## PP.Nat_3R_VB 0.2444 0.0002 0.0029 0.0033 0.0470
## PP.Nat_4R_VB 0.5565 0.0012 0.1435 0.0211 0.3893
## PP.FR.GFFB 0.0000 0.0101 0.0156 0.0124 0.0008
## PP.FR.GFPRB 0.2685 0.0349 0.1020 0.0002
## PP.FR.CBB 0.2685 0.0000 0.0000 0.0011
## PP.FR.PBPB 0.0349 0.0000 0.0000 0.0000
## PP.FR.PBFB 0.1020 0.0000 0.0000 0.0000
## PP.FR.VB 0.0002 0.0011 0.0000 0.0000library(corrplot)
corrplot(mydata.corFN, method="color")
corrplot(mydata.corFN, addCoef.col = 1, number.cex = 0.3, method = 'number')
Variable Checks & Balances
##Naturalness Scales and Scores
PP$Naturalness_Score_GFFB_AN ## NULLPP$Naturalness_Score_GFFB_HI ## NULLPP$Naturalness_Score_GFPRB_AN## NULLPP$Naturalness_Score_GFPRB_HI ## NULLPP$Naturalness_Score_CBB_AN ## NULLPP$Naturalness_Score_CBB_HI ## NULLPP$Naturalness_Score_PBPB_AN## NULLPP$Naturalness_Score_PBPB_HI## NULLPP$Naturalness_Score_PBFB_AN## NULLPP$Naturalness_Score_PBFB_HI## NULLPP$Naturalness_Score_VB_AN ## NULLPP$Naturalness_Score_VB_HI ## NULLPP$Naturalness_Score_GFFB_Tot ## [1] NaN NaN NaN 45.50000 23.00000 NaN 100.00000
## [8] 62.50000 100.00000 50.00000 88.00000 99.50000 100.00000 88.00000
## [15] 100.00000 99.50000 51.50000 24.25000 42.25000 80.75000 67.50000
## [22] 76.00000 41.50000 25.00000 62.00000 33.00000 32.25000 NaN
## [29] NaN NaN NaN NaN NaN NaN NaN
## [36] NaN NaN NaN NaN NaN NaN NaN
## [43] NaN NaN NaN NaN NaN NaN NaN
## [50] NaN NaN NaN NaN NaN NaN NaN
## [57] NaN 51.25000 NaN NaN NaN 84.75000 NaN
## [64] NaN NaN NaN NaN NaN NaN NaN
## [71] NaN 74.50000 79.75000 75.00000 NaN 25.00000 61.75000
## [78] NaN NaN 78.75000 NaN NaN NaN NaN
## [85] 99.25000 NaN NaN NaN NaN 30.25000 93.25000
## [92] NaN NaN NaN NaN 23.50000 NaN NaN
## [99] 41.00000 91.50000 NaN NaN NaN NaN 99.25000
## [106] 71.25000 NaN NaN 77.75000 25.75000 9.75000 NaN
## [113] 89.00000 NaN NaN NaN NaN NaN NaN
## [120] NaN NaN NaN 68.50000 NaN 62.75000 NaN
## [127] NaN 52.00000 NaN 70.25000 38.75000 NaN NaN
## [134] NaN 95.75000 NaN NaN 90.00000 44.25000 NaN
## [141] NaN NaN 50.00000 NaN 64.50000 46.50000 26.25000
## [148] NaN 85.00000 NaN 75.75000 NaN 35.00000 NaN
## [155] NaN NaN NaN 72.50000 57.00000 NaN NaN
## [162] NaN NaN NaN 49.00000 NaN NaN NaN
## [169] 60.75000 NaN NaN 40.00000 NaN NaN 39.00000
## [176] NaN 75.00000 98.25000 0.00000 NaN NaN NaN
## [183] NaN NaN NaN 64.75000 NaN NaN 63.75000
## [190] 40.50000 NaN NaN NaN 84.75000 34.50000 33.25000
## [197] 28.50000 NaN 69.50000 NaN NaN 34.75000 57.25000
## [204] 60.00000 NaN NaN 57.25000 40.75000 NaN NaN
## [211] NaN NaN NaN NaN NaN NaN 84.50000
## [218] NaN NaN NaN 59.25000 NaN NaN NaN
## [225] NaN NaN NaN NaN 49.50000 NaN NaN
## [232] 58.00000 NaN NaN NaN NaN 63.25000 NaN
## [239] 44.50000 NaN 39.00000 53.25000 NaN NaN NaN
## [246] NaN NaN NaN NaN NaN 75.00000 NaN
## [253] NaN NaN 54.00000 49.00000 64.00000 57.00000 NaN
## [260] NaN NaN 57.50000 NaN 0.00000 NaN 41.25000
## [267] 50.00000 NaN 64.50000 NaN 44.25000 81.75000 NaN
## [274] NaN NaN NaN 26.50000 53.00000 NaN NaN
## [281] NaN NaN NaN 32.25000 47.00000 23.75000 NaN
## [288] NaN NaN NaN 36.75000 NaN NaN 73.75000
## [295] NaN NaN NaN 57.00000 69.50000 65.00000 NaN
## [302] 46.50000 NaN NaN 42.25000 NaN NaN NaN
## [309] NaN 44.75000 48.50000 54.50000 NaN NaN 46.25000
## [316] 18.75000 NaN NaN NaN NaN 49.75000 49.00000
## [323] NaN NaN NaN NaN NaN NaN NaN
## [330] 45.50000 25.00000 NaN NaN NaN 38.00000 NaN
## [337] NaN NaN NaN 45.25000 47.75000 41.75000 47.50000
## [344] NaN 49.00000 NaN NaN NaN NaN NaN
## [351] NaN 57.00000 NaN NaN NaN NaN NaN
## [358] 47.25000 NaN NaN 34.75000 47.75000 NaN NaN
## [365] 41.50000 NaN NaN NaN NaN NaN 41.75000
## [372] 45.00000 NaN 40.00000 NaN NaN NaN NaN
## [379] NaN NaN 68.00000 NaN NaN NaN 51.00000
## [386] 33.00000 NaN NaN 38.75000 NaN NaN NaN
## [393] 55.00000 46.75000 33.75000 NaN 48.75000 40.75000 65.00000
## [400] 41.50000 38.25000 NaN 44.75000 57.50000 NaN 48.00000
## [407] 56.25000 NaN NaN 30.25000 NaN 63.50000 NaN
## [414] NaN 49.50000 NaN NaN NaN NaN 38.00000
## [421] NaN NaN NaN 51.50000 NaN NaN NaN
## [428] NaN 46.50000 37.00000 39.00000 NaN NaN NaN
## [435] 52.25000 38.50000 NaN 66.25000 41.50000 NaN NaN
## [442] NaN 27.25000 NaN 32.00000 NaN NaN NaN
## [449] 26.50000 43.75000 NaN 40.75000 NaN NaN 33.50000
## [456] 29.25000 NaN NaN NaN 37.75000 NaN 49.25000
## [463] NaN NaN NaN NaN NaN 50.50000 57.25000
## [470] 33.00000 NaN NaN 36.50000 32.00000 41.75000 NaN
## [477] NaN 32.00000 32.25000 NaN NaN NaN NaN
## [484] NaN NaN NaN 34.25000 NaN 37.50000 31.75000
## [491] 28.75000 NaN NaN 32.25000 NaN NaN NaN
## [498] 22.50000 NaN 37.75000 NaN NaN NaN NaN
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## [973] 27 52 100 70 74 85 NA NA 21 NA 21 53 88 54 35 56 43 18
## [991] 52 28 22 14 NA 93 52 24 43 57 NA 11 78 100 NAPP$Familiarity_PBPB## [1] NA 95 90 NA 100 NA NA NA NA NA NA NA NA NA NA NA NA NA
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## [595] 0 85 NA 72 NA 48 NA NA NA NA NA NA 58 NA 36 NA 17 28
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## [667] 51 52 NA NA NA 52 52 NA NA NA NA NA NA 54 57 32 NA NA
## [685] NA NA 70 NA NA NA NA 98 66 38 60 NA 54 NA 78 69 53 NA
## [703] 70 52 47 32 NA 28 NA 76 NA NA 71 64 83 25 62 67 26 NA
## [721] NA 100 75 27 NA 37 85 NA 20 NA 70 53 7 42 NA 69 NA 100
## [739] 63 0 NA 83 NA NA NA 0 NA 84 62 57 24 NA 76 100 74 72
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## [775] 86 98 88 75 NA 73 NA 55 NA NA NA NA 85 NA NA NA 100 84
## [793] NA 86 NA NA 52 100 NA NA 100 99 NA NA NA NA NA NA NA 99
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## [847] 71 NA 56 91 100 NA NA 56 NA 45 66 3 74 0 71 25 0 63
## [865] 93 67 75 60 31 NA 100 31 15 NA 94 NA 25 96 65 3 76 82
## [883] 19 79 52 NA 39 72 NA NA 57 80 30 0 NA 56 57 93 0 NA
## [901] 29 63 0 21 78 49 33 43 95 65 52 34 15 0 30 57 63 NA
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## [955] NA 23 65 NA NA NA 92 NA NA 26 3 41 0 71 96 79 97 89
## [973] 98 21 NA NA 23 72 37 80 19 80 27 30 88 50 44 60 NA 64
## [991] 52 NA 64 18 3 69 NA NA 37 NA 0 74 NA 88 38PP$Familiarity_PBFB## [1] NA 55 NA NA NA 100 53 100 0 100 0 NA NA NA NA NA 0 NA
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## [721] NA 51 NA NA 0 84 NA 29 NA 63 NA 71 NA NA 51 NA NA NA
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## [757] 72 38 NA NA NA NA NA NA 70 83 24 NA 14 0 90 19 NA NA
## [775] NA 72 NA NA NA NA 53 55 83 86 NA 44 NA NA 91 10 NA NA
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## [829] 69 81 62 NA 52 74 10 32 74 NA 86 NA 47 87 75 NA NA 20
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## [865] 88 58 NA NA NA 39 100 8 0 0 84 17 35 NA NA NA 30 79
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## [901] 77 85 14 36 73 64 0 37 81 NA NA NA 23 73 32 60 67 72
## [919] 31 27 17 NA 0 75 NA 0 NA 69 58 78 NA 100 33 9 85 NA
## [937] 79 21 NA 11 16 69 NA NA 68 80 21 18 97 55 75 23 42 NA
## [955] 25 95 NA 0 21 53 91 60 89 76 2 NA 17 62 NA 50 14 30
## [973] NA 23 80 64 NA NA 3 31 NA 87 NA NA NA NA NA NA 94 72
## [991] 21 30 29 4 3 86 52 6 NA 44 0 21 79 79 52PP$Familiarity_VB ## [1] NA NA NA NA NA NA NA NA NA NA NA 8 100 100 NA NA NA NA
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## [163] NA 81 NA NA NA 25 NA 26 17 NA 73 73 61 NA NA NA 50 9
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## [217] NA 56 66 NA NA 83 NA NA NA 11 NA NA 12 59 NA NA NA 12
## [235] 45 97 NA NA NA NA NA NA 80 42 57 56 NA NA NA 21 0 NA
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## [307] NA NA NA 66 44 56 NA 86 NA 90 16 91 57 61 NA 58 NA 85
## [325] 71 NA NA NA NA NA NA 27 NA NA NA 51 41 NA 99 75 NA NA
## [343] NA 31 NA 52 63 52 NA NA 57 NA NA 52 81 26 NA NA 94 NA
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## [415] NA 71 84 NA NA 67 65 69 NA 61 81 NA 63 NA NA NA NA 21
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## [469] NA 84 NA 81 NA NA NA 79 95 94 NA NA 97 NA 87 74 NA NA
## [487] 81 NA NA NA NA 100 NA NA NA 99 NA NA NA NA 52 NA 97 NA
## [505] 92 NA NA NA NA NA 100 100 NA 0 NA 100 100 NA 100 NA NA NA
## [523] 37 NA 52 NA 78 100 NA 30 98 NA NA NA NA NA NA NA 0 NA
## [541] 3 11 NA 83 87 22 100 82 NA 96 NA NA NA NA 89 7 94 NA
## [559] NA NA 69 NA NA NA NA 10 NA NA 0 NA NA NA NA NA 100 NA
## [577] 40 23 100 NA 24 NA NA 11 87 NA NA 45 NA 72 0 NA 39 NA
## [595] NA 94 100 NA 100 NA 58 NA 50 74 63 NA 29 88 0 100 NA 57
## [613] NA NA NA NA 39 47 NA NA NA NA 75 NA 84 NA 34 75 71 76
## [631] 67 47 85 NA NA 97 43 53 NA NA 4 NA NA 77 NA NA 0 63
## [649] NA NA 67 NA 69 NA 40 NA 35 NA 51 NA NA 56 94 NA NA NA
## [667] NA 52 53 52 52 NA NA 53 2 79 50 NA 55 NA NA NA 18 50
## [685] 98 93 76 NA 42 82 74 NA NA 53 NA 15 NA 45 NA 94 NA 86
## [703] NA NA NA NA 50 NA NA NA 52 69 NA 70 NA 52 NA NA NA NA
## [721] 36 NA 96 100 NA NA NA NA NA NA NA NA 66 45 NA NA 55 42
## [739] NA 0 NA NA 71 80 NA NA 73 NA NA NA 87 92 NA NA NA NA
## [757] NA 84 100 NA NA 65 NA NA NA NA NA 77 NA NA NA NA NA 94
## [775] 78 NA 84 NA 83 78 NA NA NA 80 81 NA 71 70 97 NA NA NA
## [793] 71 NA NA 87 NA NA 93 67 NA NA 60 100 96 5 29 NA NA NA
## [811] NA 58 NA NA NA 0 99 97 NA NA NA NA 31 49 79 60 85 NA
## [829] NA 79 NA 89 68 NA 20 70 34 53 45 86 54 93 NA 84 100 NA
## [847] 80 52 87 86 96 60 100 66 100 43 89 NA NA NA NA NA NA NA
## [865] NA NA 100 51 75 40 NA 21 NA 6 NA 95 NA 100 70 1 NA NA
## [883] NA 73 53 100 83 NA 68 43 NA NA 100 1 70 NA 0 NA 62 77
## [901] 100 NA NA 62 19 NA NA NA 79 45 72 59 NA NA NA NA 64 86
## [919] NA NA 85 76 NA 91 34 NA 36 81 NA 77 53 100 83 83 77 100
## [937] 74 NA 100 NA 36 NA 96 56 NA 68 69 50 NA 64 NA 68 NA 77
## [955] 70 71 50 0 33 55 NA 38 96 86 100 28 NA 68 95 NA NA NA
## [973] 98 NA 100 NA 99 61 42 98 42 83 69 38 86 50 47 64 83 NA
## [991] NA 48 NA NA 62 NA 0 100 44 42 0 NA 81 NA 68PP$Understanding_GFFB## [1] NA NA NA 65 93 NA 100 100 100 100 100 100 100 0 100 100 12 97
## [19] 100 100 100 80 69 NA 52 94 87 NA NA NA NA NA NA NA NA NA
## [37] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [55] NA NA NA 100 NA NA NA 100 NA NA NA NA NA NA NA NA NA 100
## [73] 100 100 NA 0 59 NA NA 100 NA NA NA NA 100 NA NA NA NA 18
## [91] 100 NA NA NA NA 90 NA NA 91 92 NA NA NA NA 89 95 NA NA
## [109] 91 100 93 NA 82 NA NA NA NA NA NA NA NA NA 78 NA 73 NA
## [127] NA 80 NA 85 69 NA NA NA 100 NA NA 100 49 NA NA NA 100 NA
## [145] 67 91 69 NA 69 NA 11 NA 18 NA NA NA NA 88 36 NA NA NA
## [163] NA NA 53 NA NA NA 100 NA NA 22 NA NA 75 NA 100 98 100 NA
## [181] NA NA NA NA NA 66 NA NA 75 21 NA NA NA 88 29 90 72 NA
## [199] 74 NA NA 98 50 68 NA NA 59 73 NA NA NA NA NA NA NA NA
## [217] 89 NA NA NA 44 NA NA NA NA NA NA NA 81 NA NA 30 NA NA
## [235] NA NA 77 NA 54 NA 93 67 NA NA NA NA NA NA NA NA 0 NA
## [253] NA NA 56 29 94 82 NA NA NA 31 NA 73 NA 31 100 NA 22 NA
## [271] 33 78 NA NA NA NA 0 11 NA NA NA NA NA 67 69 16 NA NA
## [289] NA NA 70 NA NA 100 NA NA NA 52 9 38 NA 82 NA NA 40 NA
## [307] NA NA NA 45 59 22 NA NA 66 96 NA NA NA NA 50 59 NA NA
## [325] NA NA NA NA NA 75 100 NA NA NA 69 NA NA NA NA 73 86 82
## [343] 50 NA 52 NA NA NA NA NA NA 79 NA NA NA NA NA 53 NA NA
## [361] 25 54 NA NA 53 NA NA NA NA NA 59 43 NA 20 NA NA NA NA
## [379] NA NA 100 NA NA NA 62 88 NA NA 84 NA NA NA 37 72 81 NA
## [397] 40 64 79 69 2 NA 55 79 NA 68 73 NA NA 71 NA 84 NA NA
## [415] 56 NA NA NA NA 78 NA NA NA 53 NA NA NA NA 69 42 64 NA
## [433] NA NA 38 38 NA 71 NA NA NA NA 74 NA 32 NA NA NA 87 64
## [451] NA 68 NA NA 96 76 NA NA NA 68 NA 53 NA NA NA NA NA 81
## [469] 92 78 NA NA 63 93 100 NA NA 86 81 NA NA NA NA NA NA NA
## [487] 85 NA 50 82 100 NA NA 90 NA NA NA 4 NA 98 NA NA NA NA
## [505] NA 0 100 98 0 100 100 NA NA NA NA NA 100 100 100 51 90 100
## [523] 100 100 100 0 100 100 100 100 78 100 100 90 100 0 0 99 61 94
## [541] 52 93 70 93 55 93 100 64 98 72 100 91 82 100 89 15 98 26
## [559] 85 53 17 53 100 94 82 90 17 91 81 87 66 80 22 83 100 62
## [577] 87 35 100 71 74 93 23 87 73 91 26 100 82 38 100 80 29 70
## [595] 100 86 100 100 100 53 87 86 21 59 60 73 62 100 19 80 67 87
## [613] 63 84 100 14 42 74 52 62 35 27 76 100 65 73 38 67 63 73
## [631] 37 31 74 85 71 93 56 47 75 78 25 61 100 45 67 38 57 80
## [649] 79 41 59 67 65 100 51 100 100 33 51 51 51 54 92 47 63 52
## [667] 25 52 53 52 52 52 52 53 54 90 50 82 53 51 57 13 40 80
## [685] 7 73 31 50 17 58 73 41 63 36 65 61 52 39 77 6 46 78
## [703] 58 52 62 100 65 59 75 85 52 79 55 58 89 81 62 62 63 76
## [721] 76 36 73 100 69 29 70 76 64 28 73 70 87 63 79 66 74 69
## [739] 44 80 59 100 86 79 69 0 51 64 67 70 84 96 68 100 70 78
## [757] 67 86 1 0 100 68 79 99 81 65 74 85 29 100 87 100 100 99
## [775] 75 81 81 85 85 82 53 53 85 71 28 94 83 71 100 100 91 85
## [793] 87 82 95 79 95 100 16 100 100 94 100 95 98 88 100 100 31 84
## [811] 100 99 100 100 99 56 98 85 100 0 76 100 NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Understanding_GFPRB## [1] NA NA NA 73 100 100 89 100 100 100 100 100 100 100 0 100 0 100
## [19] 98 100 100 70 100 51 100 0 77 85 100 82 100 0 100 100 100 100
## [37] 100 100 100 100 100 100 100 0 100 0 100 100 0 100 100 100 100 100
## [55] 100 100 100 100 100 100 71 100 99 90 100 100 100 52 100 94 100 100
## [73] 1 97 100 100 94 100 14 100 100 100 100 83 100 100 100 85 100 100
## [91] 100 94 92 88 100 80 75 95 93 100 93 9 90 91 90 97 98 93
## [109] 79 13 88 87 96 78 100 98 82 80 93 100 66 26 100 9 75 78
## [127] 66 81 92 85 82 100 93 100 100 62 100 100 89 28 100 53 100 82
## [145] NA 66 100 74 20 81 89 86 88 91 50 97 83 86 71 64 66 76
## [163] 70 91 53 100 52 76 98 19 76 69 87 87 24 93 100 96 100 0
## [181] 0 100 100 72 100 81 100 84 73 89 17 77 59 95 91 85 90 5
## [199] 84 81 98 81 50 69 90 32 20 82 100 76 34 89 73 67 87 93
## [217] 78 77 56 69 68 92 77 83 33 100 79 99 82 68 81 32 57 69
## [235] 30 89 76 88 89 80 100 70 72 37 39 56 55 100 87 64 97 75
## [253] 80 100 41 19 56 87 100 100 100 31 61 69 100 29 88 81 50 51
## [271] 75 71 100 62 99 69 59 29 100 50 100 100 54 64 37 69 77 52
## [289] 52 33 84 45 44 70 44 60 100 40 100 64 49 66 48 86 80 72
## [307] 26 34 37 30 56 39 58 34 71 94 20 96 56 26 50 42 75 18
## [325] 100 92 50 100 50 45 100 20 51 54 91 51 66 51 84 58 85 76
## [343] 62 100 52 52 80 52 86 0 56 65 55 62 81 30 81 52 53 45
## [361] 29 65 74 34 71 36 69 42 95 16 61 37 68 34 39 60 43 38
## [379] 67 85 100 71 66 90 59 100 52 39 88 68 36 43 40 71 73 95
## [397] 70 31 41 63 45 99 65 38 32 34 71 34 64 32 78 72 100 90
## [415] 51 64 84 66 67 63 64 75 70 53 100 56 62 66 67 70 66 85
## [433] 76 8 77 65 79 35 81 100 29 70 95 71 0 36 78 15 74 69
## [451] 84 73 100 77 94 73 80 71 2 85 75 87 75 82 32 85 60 86
## [469] 21 86 74 87 77 98 100 92 73 72 83 93 85 97 79 42 79 92
## [487] 79 79 0 89 100 96 89 78 81 94 90 11 84 95 100 91 100 100
## [505] 68 100 96 100 100 98 100 100 100 75 20 100 NA NA NA NA NA NA
## [523] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [541] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [559] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [577] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [595] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [613] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [631] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [649] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [667] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [685] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [703] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [721] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [739] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [757] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [775] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [793] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [811] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Understanding_CBB## [1] NA NA 92 54 NA 95 NA NA NA NA NA NA NA NA 30 0 NA 100
## [19] NA NA NA NA 72 0 NA NA NA NA NA NA 0 51 92 NA 82 50
## [37] NA NA 100 NA 100 NA NA NA 6 NA 0 91 90 NA 0 NA NA 100
## [55] NA 99 NA NA NA NA 71 NA 100 NA NA NA NA NA 100 NA NA NA
## [73] NA NA 3 NA NA 65 NA NA 30 NA 77 NA NA 100 100 NA 24 NA
## [91] NA NA 12 NA 96 NA NA 75 84 90 NA NA 65 NA NA NA 0 NA
## [109] NA NA NA 68 NA 76 NA 93 NA NA 0 NA 51 NA NA 87 NA NA
## [127] 5 NA NA NA 35 100 NA 38 100 NA NA NA 69 93 NA NA NA 86
## [145] NA NA NA 37 NA NA 18 NA 99 80 11 NA 16 95 40 18 62 NA
## [163] 1 NA 52 NA 100 50 NA NA NA 28 12 NA NA 86 NA NA NA 0
## [181] 0 NA NA NA NA 70 77 NA 69 NA NA 75 54 NA 75 NA 73 NA
## [199] NA NA NA NA NA NA 0 NA NA NA 100 23 73 NA NA NA NA NA
## [217] 28 NA NA NA NA NA NA 36 30 NA NA 86 NA 60 43 13 26 NA
## [235] NA NA NA 93 NA 53 69 57 100 NA NA NA NA 10 51 69 NA 50
## [253] 78 NA NA 51 NA 69 25 67 72 NA NA NA 100 NA NA 73 NA 56
## [271] NA NA NA NA 66 NA NA 54 NA NA NA 90 NA NA NA NA 33 NA
## [289] 3 33 NA 43 45 NA 63 NA NA NA NA NA NA NA 86 0 6 NA
## [307] NA 37 84 NA NA NA NA 53 NA NA 52 NA 39 NA NA NA 15 NA
## [325] NA NA 50 0 NA NA NA NA 58 93 19 NA 25 51 NA NA 85 87
## [343] NA 100 52 NA NA NA 62 0 NA NA 46 70 64 77 NA NA 53 52
## [361] NA 62 23 NA NA 29 NA NA NA 93 NA NA 53 NA NA NA 100 58
## [379] 71 96 NA 70 35 NA NA NA 70 NA NA NA NA NA 39 NA NA NA
## [397] NA NA 100 59 NA NA 75 70 NA NA NA NA NA 75 35 NA NA 70
## [415] 51 NA NA 69 NA NA NA NA 70 NA NA NA NA 64 NA NA NA 86
## [433] NA NA NA NA 72 NA NA NA 73 69 NA 38 NA NA 75 100 NA 69
## [451] NA NA NA NA NA NA 93 NA NA NA 83 NA 29 NA NA 100 NA NA
## [469] NA NA 79 NA NA 97 NA 86 NA NA 95 NA NA NA NA 86 83 88
## [487] NA NA 50 75 92 100 14 NA 97 NA NA NA 36 72 NA 85 NA 82
## [505] NA NA NA 99 100 100 NA 0 100 NA 10 100 100 0 NA 0 NA NA
## [523] NA 51 NA NA NA NA NA NA 18 100 100 34 0 NA NA 2 NA NA
## [541] 75 NA 21 20 34 NA NA NA NA 100 100 31 83 41 63 NA 38 NA
## [559] 78 NA 100 4 NA 95 NA NA 7 62 61 0 0 73 0 NA NA 15
## [577] NA NA 100 NA NA NA 66 NA NA NA 0 NA 93 6 100 75 30 62
## [595] 14 NA NA 100 100 NA 5 100 38 NA 57 13 NA NA NA 30 NA NA
## [613] 28 3 83 2 55 NA 41 0 32 95 NA NA 97 69 NA NA 75 NA
## [631] NA 10 21 20 NA NA 53 88 NA 79 NA 39 0 33 37 61 54 NA
## [649] NA NA NA NA 43 100 45 62 NA NA NA NA NA 29 NA 15 67 NA
## [667] NA NA 0 100 52 NA NA 53 50 NA NA 53 NA NA NA NA NA 85
## [685] 81 42 NA 52 NA NA 29 NA NA NA 56 NA NA NA 82 NA 52 59
## [703] NA NA 33 100 35 63 65 NA NA 66 NA NA 68 NA 66 54 NA 73
## [721] 62 NA NA NA 84 NA 59 87 22 26 76 NA NA NA 100 50 68 NA
## [739] NA NA 33 NA NA 81 73 NA 76 63 NA 68 NA NA 84 77 NA NA
## [757] NA NA 33 70 96 64 86 100 69 NA 27 81 NA NA 89 NA 100 100
## [775] NA NA NA 65 87 NA 22 NA 83 NA 85 74 NA 38 NA 28 94 83
## [793] 89 NA 84 NA 20 100 8 35 NA NA NA NA NA NA 72 100 16 100
## [811] 100 93 100 100 57 NA NA 49 78 0 NA 100 NA 70 NA NA 92 23
## [829] 66 75 38 67 NA 100 NA 80 NA 65 46 12 34 100 59 50 72 2
## [847] NA 52 65 NA NA 61 100 NA 0 47 90 53 36 100 27 50 25 27
## [865] 93 68 90 7 35 43 95 NA 99 25 100 100 51 4 51 3 26 83
## [883] 63 NA NA 3 100 29 28 31 56 64 NA NA 74 46 NA 99 NA 100
## [901] NA 88 0 NA NA 64 69 48 NA 59 87 63 74 15 83 60 NA 85
## [919] 30 76 19 83 20 100 32 94 96 NA 76 65 54 100 0 NA NA 68
## [937] 72 NA 80 26 NA 62 100 56 62 NA NA NA 68 62 53 10 64 50
## [955] 0 NA 44 0 30 52 96 43 100 NA NA 66 21 NA 98 52 20 23
## [973] 78 52 100 64 56 71 NA NA 22 NA 78 52 91 50 44 63 24 33
## [991] 52 24 60 41 NA 25 38 46 42 41 NA 13 79 100 NAPP$Understanding_PBPB## [1] NA 56 90 NA 100 NA NA NA NA NA NA NA NA NA NA NA NA NA
## [19] NA 71 NA NA NA NA NA 3 NA NA NA NA 0 NA NA 100 77 NA
## [37] 0 100 NA 100 NA 91 100 NA NA 34 100 NA NA 52 NA 30 100 NA
## [55] 62 99 NA NA NA NA 31 NA NA NA 1 NA NA 77 100 32 100 100
## [73] NA 100 13 NA NA NA NA NA NA NA NA 24 100 NA NA 57 NA 98
## [91] 88 NA 0 NA 63 28 52 NA NA NA 13 75 NA 0 NA NA 40 NA
## [109] 86 NA 56 77 86 84 NA NA NA NA 0 81 NA 52 NA NA NA 51
## [127] 28 NA 91 NA NA NA 90 NA NA NA 63 NA NA NA 100 NA 100 72
## [145] NA NA NA 29 NA NA NA 73 NA NA NA 88 NA NA NA NA NA NA
## [163] 0 92 NA 78 24 NA 100 NA 67 NA NA NA NA NA 0 72 NA NA
## [181] 0 100 NA NA 97 NA NA 28 NA 90 16 78 NA NA NA 29 NA 0
## [199] 42 78 3 83 NA NA NA 30 64 NA 100 19 NA NA 74 59 70 NA
## [217] NA NA 71 68 67 NA 73 82 33 NA 92 NA NA NA NA NA 29 NA
## [235] NA NA NA NA NA 62 NA NA NA NA NA 35 55 70 NA NA NA NA
## [253] 78 9 46 NA NA NA NA NA NA 62 23 10 100 NA 100 NA NA 50
## [271] NA 52 100 87 NA 41 NA NA 99 50 82 91 NA NA NA NA 50 NA
## [289] 80 NA 26 NA 17 100 21 21 100 52 NA 29 47 58 NA NA NA 77
## [307] 72 69 53 NA NA NA 74 NA NA NA NA NA NA 74 50 NA NA NA
## [325] 100 87 50 100 50 41 100 NA NA 57 NA NA NA 51 NA NA NA NA
## [343] 70 NA NA NA 95 59 NA 0 59 NA 41 NA NA NA 29 52 NA NA
## [361] 100 NA 30 NA 42 31 NA 25 61 93 NA NA NA 17 76 58 64 NA
## [379] 69 NA NA 68 60 NA NA 74 69 39 79 NA 64 60 NA NA 100 48
## [397] NA 86 NA NA NA 74 NA NA NA 85 62 58 58 NA 58 86 100 NA
## [415] NA NA NA NA 73 NA NA NA 84 NA NA 33 NA NA NA NA 66 NA
## [433] 83 NA NA NA NA 52 NA 100 NA NA NA NA 65 59 72 NA 100 NA
## [451] NA 67 NA 72 52 NA NA 71 NA NA 77 NA NA 80 NA NA 24 NA
## [469] 93 NA 66 71 86 NA NA NA NA NA NA 90 NA 86 89 NA 100 NA
## [487] NA 28 NA NA NA NA NA NA NA NA 20 14 NA NA NA 29 NA NA
## [505] NA NA NA NA NA NA NA NA NA NA NA NA NA 5 NA 51 100 50
## [523] NA NA 57 0 81 100 100 100 NA 100 80 NA 65 NA 100 NA 7 85
## [541] NA NA 17 NA NA NA NA NA 20 NA 93 63 80 NA NA 29 NA 19
## [559] 85 16 NA NA 75 93 87 76 28 59 NA NA 100 78 38 27 NA 70
## [577] 86 NA NA 77 NA 82 74 100 91 100 NA 45 100 NA NA NA NA NA
## [595] 13 84 NA 100 NA 48 NA NA NA NA NA NA 0 NA 100 NA 23 25
## [613] 64 51 NA 35 NA 60 52 52 58 20 72 0 NA 72 34 NA NA NA
## [631] 63 NA NA 33 68 NA NA NA 52 NA 27 NA NA NA NA 74 NA NA
## [649] 43 42 64 81 NA 100 NA 64 100 75 NA 52 51 NA NA 90 68 61
## [667] 89 52 NA NA NA 51 51 NA NA NA NA NA NA 53 55 27 NA NA
## [685] NA NA 68 NA NA NA NA 87 32 52 68 NA 52 NA 79 46 52 NA
## [703] 67 50 65 100 NA 62 NA 85 NA NA 64 42 76 98 34 61 4 NA
## [721] NA 100 72 28 NA 62 77 NA 26 NA 79 66 79 69 NA 68 NA 30
## [739] 42 79 NA 100 NA NA NA 42 NA 64 62 81 87 NA 63 100 65 86
## [757] 60 NA NA 100 100 NA 67 100 NA 88 NA NA 66 83 NA 100 100 NA
## [775] 62 86 77 73 NA 81 NA 58 NA NA NA NA 84 NA NA NA 82 90
## [793] NA 81 NA NA 61 100 NA NA 100 97 NA NA NA NA NA NA NA 100
## [811] NA NA 100 100 100 100 100 NA NA NA 76 NA 71 NA 22 100 NA 72
## [829] 64 NA 38 70 36 75 28 NA 62 85 NA 62 NA NA 55 50 38 36
## [847] 28 NA 56 100 100 NA NA 73 NA 35 91 43 70 0 66 58 60 60
## [865] 87 71 94 91 71 NA 87 65 100 NA 91 NA 31 100 68 69 28 59
## [883] 23 77 52 NA 100 87 NA NA 63 75 100 100 NA 52 61 100 100 NA
## [901] 76 59 34 22 81 76 100 47 97 42 52 24 82 28 29 63 57 NA
## [919] 31 71 NA 78 87 NA 28 0 75 3 91 NA 82 NA NA 79 74 23
## [937] NA 31 85 77 41 40 75 44 73 100 60 84 100 NA 53 NA 34 52
## [955] NA 66 53 NA NA NA 93 NA NA 76 100 69 63 72 95 44 93 92
## [973] 98 25 NA NA 22 52 1 100 27 77 0 45 80 47 45 62 NA 65
## [991] 52 NA 83 33 7 84 NA NA 36 NA 55 10 NA 88 43PP$Understanding_PBFB ## [1] NA 60 NA NA NA 100 52 100 0 100 100 NA NA NA NA NA 0 NA
## [19] 81 NA 0 4 NA NA NA NA 87 100 0 6 NA 51 26 100 NA NA
## [37] NA 100 0 100 0 100 0 0 80 NA NA 68 NA 57 54 NA 100 2
## [55] 0 NA 70 NA 1 0 NA 0 NA 8 0 52 100 11 NA NA NA NA
## [73] 1 NA NA NA NA NA 1 NA 75 100 100 33 NA NA 100 31 66 NA
## [91] NA 12 NA 6 NA NA 28 NA NA NA 14 82 42 16 NA NA NA 3
## [109] NA NA NA NA NA NA 15 NA 87 59 NA NA NA NA NA NA NA 0
## [127] NA 77 NA 67 NA 100 NA 100 NA 38 NA NA NA NA 80 53 NA NA
## [145] 89 57 NA NA 88 22 NA NA NA NA NA NA NA NA NA NA NA 40
## [163] NA NA NA 52 NA NA NA 20 NA NA NA 70 NA 32 NA NA NA NA
## [181] NA NA 100 55 100 NA NA NA NA NA NA NA NA 58 NA NA NA NA
## [199] NA NA NA NA NA NA NA NA NA NA NA NA 40 74 NA 5 NA 32
## [217] NA 57 NA 69 NA 89 75 NA NA 85 78 97 NA NA 68 NA NA 13
## [235] 28 83 74 85 61 NA NA NA NA 35 2 NA 54 NA 52 NA NA 90
## [253] NA NA NA NA NA NA 27 NA NA NA 28 NA NA NA NA NA 41 NA
## [271] 28 NA NA NA 74 24 NA NA 82 21 NA NA NA 28 NA 68 NA 52
## [289] NA NA NA NA NA NA NA NA 84 NA 34 NA NA NA NA 80 NA NA
## [307] 89 NA NA NA NA NA 64 NA 72 NA NA 69 NA NA NA NA 71 85
## [325] NA 43 NA NA 50 NA NA 0 52 NA NA 51 NA NA 93 NA NA NA
## [343] NA NA NA 52 NA NA 71 NA NA 68 NA NA NA NA 22 NA NA 100
## [361] NA NA NA 26 NA NA 80 NA 26 NA NA NA 52 NA 66 NA NA 65
## [379] NA 98 100 NA NA 66 NA NA NA 39 NA 66 NA NA NA NA NA NA
## [397] NA NA NA NA NA NA NA NA 28 NA NA NA NA NA NA NA 100 NA
## [415] NA 67 75 62 78 NA 63 76 NA NA 65 29 30 61 100 64 NA NA
## [433] NA 50 75 NA NA NA 80 100 NA NA 94 100 NA NA NA NA NA NA
## [451] 82 NA 100 74 NA NA 86 NA 0 91 NA 65 25 83 34 73 NA 36
## [469] NA NA NA NA NA NA 79 NA 100 NA NA 88 90 96 NA NA NA 82
## [487] NA 75 NA NA NA NA 56 98 83 79 70 NA 71 NA 95 NA 83 92
## [505] 100 98 100 NA NA NA NA NA 100 100 23 NA NA NA 85 NA 78 51
## [523] 100 0 NA 0 NA NA 100 NA NA NA NA 73 NA 1 100 2 NA 78
## [541] NA 5 NA NA NA 90 87 20 1 NA NA NA NA 77 NA NA NA 16
## [559] NA 52 NA 0 66 NA 93 NA NA NA NA 11 NA NA NA 35 100 NA
## [577] NA 15 NA 70 25 95 NA NA NA 50 0 NA NA NA NA 75 NA 77
## [595] NA NA 0 NA NA 52 NA 77 NA 18 NA 0 NA 83 NA NA 28 NA
## [613] NA NA 83 NA NA NA NA NA NA NA NA 0 NA NA NA 21 NA 84
## [631] NA NA NA NA 66 88 NA NA 70 33 NA 53 100 NA 33 NA NA 69
## [649] 73 41 NA 62 NA NA NA NA NA 67 51 53 51 NA 100 NA NA 68
## [667] 88 NA NA NA NA 52 51 NA NA 77 50 53 42 52 36 98 43 NA
## [685] NA NA NA 59 28 42 NA 41 62 NA NA NA 52 37 NA NA NA NA
## [703] 60 47 NA NA NA NA 53 94 69 NA 45 NA NA NA NA NA 64 27
## [721] NA 82 NA NA 3 70 NA 82 NA 30 NA 78 NA NA 88 NA NA NA
## [739] 67 NA 77 100 87 NA 78 57 NA NA 61 NA NA 34 NA NA 72 79
## [757] 62 63 NA NA NA NA NA NA 62 82 21 NA 75 80 86 100 NA NA
## [775] NA 78 NA NA NA NA 52 98 72 75 NA 55 NA NA 77 75 NA NA
## [793] NA 77 61 12 NA NA NA NA 100 92 100 98 100 10 NA 100 100 NA
## [811] 100 NA NA NA NA NA NA NA 77 53 28 100 65 47 81 80 95 82
## [829] 65 89 66 NA 15 73 35 75 89 NA 82 NA 53 90 68 NA NA 2
## [847] 73 19 NA 66 100 34 0 67 50 NA NA 21 64 0 36 34 28 76
## [865] 89 65 NA NA NA 24 89 64 100 70 11 13 43 NA NA NA 10 82
## [883] 28 37 47 0 NA 84 70 39 54 69 100 78 79 52 0 93 100 100
## [901] 87 37 36 29 100 81 44 47 81 NA NA NA 87 31 74 59 64 61
## [919] 21 72 13 NA 83 97 NA 12 NA 28 61 76 NA 100 41 73 62 NA
## [937] 88 NA NA 67 18 70 NA NA 64 80 28 21 96 58 74 43 35 NA
## [955] 10 81 NA 0 39 52 87 59 99 24 0 NA 33 68 NA 47 74 27
## [973] NA 26 100 37 NA NA 0 71 NA 89 NA NA NA NA NA NA 21 30
## [991] 52 30 43 40 9 93 72 34 NA 52 46 14 72 71 52PP$Understanding_VB## [1] NA NA NA NA NA NA NA NA NA NA NA 100 100 100 NA NA NA NA
## [19] NA NA NA NA NA NA 100 NA NA 100 100 100 NA NA NA NA NA 50
## [37] 100 NA NA NA NA NA NA 0 NA 26 NA NA 100 NA NA 100 NA NA
## [55] NA NA 92 100 12 100 NA NA 98 100 NA 52 100 NA NA 56 79 NA
## [73] NA NA NA 5 82 83 9 100 NA 100 NA NA NA 100 NA NA NA NA
## [91] NA 5 NA 0 NA NA NA 100 NA NA NA NA NA NA 41 68 NA 91
## [109] NA 0 NA NA NA NA 76 82 81 37 NA 78 61 52 92 18 51 NA
## [127] NA NA 88 NA NA NA 79 NA NA 81 100 90 NA 75 NA 53 NA NA
## [145] NA NA 74 NA NA 82 NA 76 NA 74 66 86 82 NA NA 32 22 77
## [163] NA 89 NA NA NA 63 NA 31 34 NA 81 67 100 NA NA NA 100 3
## [181] NA 100 100 60 NA NA 88 50 NA NA 22 NA 89 NA NA NA NA 56
## [199] NA 75 77 NA 83 23 51 17 NA 79 NA NA NA 85 75 NA 34 77
## [217] NA 70 75 NA NA 100 NA NA NA 85 NA NA 27 59 NA NA NA 15
## [235] 69 90 NA NA NA NA NA NA 79 39 48 68 NA NA NA 28 0 NA
## [253] NA 100 NA NA 52 NA NA 100 64 NA NA NA NA 18 NA 84 NA NA
## [271] NA NA 100 78 NA NA 79 NA NA NA 100 NA 17 NA 76 NA NA 52
## [289] NA 15 NA 44 NA NA NA 40 NA NA NA NA 49 NA 75 NA NA 69
## [307] NA NA NA 39 44 70 NA 42 NA 91 11 100 55 69 NA 63 NA 92
## [325] 83 NA NA NA NA NA NA 61 NA NA NA 51 69 NA 100 89 NA NA
## [343] NA 100 NA 52 28 52 NA NA 45 NA NA 52 79 31 NA NA 3 NA
## [361] NA NA NA 30 NA NA 87 84 NA NA 41 68 NA NA NA 61 NA NA
## [379] NA NA NA NA NA 88 62 NA NA NA NA 61 64 62 NA 60 NA 52
## [397] 74 NA NA NA NA 91 NA NA 85 NA NA 31 64 NA NA NA NA 85
## [415] NA 71 81 NA NA 45 66 71 NA 53 63 NA 68 NA NA NA NA 81
## [433] 60 35 NA 68 28 NA NA NA 26 79 NA NA NA 52 NA 100 NA NA
## [451] 87 NA 89 NA NA 80 NA 74 100 NA NA NA NA NA 21 NA 88 NA
## [469] NA 82 NA 92 NA NA NA 79 82 88 NA NA 100 NA 91 74 NA NA
## [487] 97 NA NA NA NA 13 NA NA NA 84 NA NA NA NA 84 NA 77 NA
## [505] 100 NA NA NA NA NA 100 NA NA 83 NA 100 100 NA 100 NA NA NA
## [523] 68 NA 52 NA 100 100 NA 100 100 NA NA NA NA NA NA NA 100 NA
## [541] 29 1 NA 86 68 65 100 58 NA 92 NA NA NA NA 95 24 85 NA
## [559] NA NA 40 NA NA NA NA 99 NA NA 75 NA NA NA NA NA 100 NA
## [577] 87 90 100 NA 53 NA NA 89 88 NA NA 0 NA 79 100 NA 35 NA
## [595] NA 87 100 NA 0 NA 83 NA 25 61 77 NA 31 100 0 100 NA 26
## [613] NA NA NA NA 72 20 NA NA NA NA 75 NA 93 NA 65 40 75 79
## [631] 67 32 82 NA NA 81 43 48 NA NA 64 NA NA 65 NA NA 0 67
## [649] NA NA 60 NA 40 NA 24 NA 38 NA 51 NA NA 50 74 NA NA NA
## [667] NA 52 0 52 52 NA NA 52 50 56 50 NA 34 NA NA NA 82 70
## [685] 90 82 23 NA 34 92 26 NA NA 52 NA 61 NA 100 NA 77 NA 85
## [703] NA NA NA NA 38 NA NA NA 52 82 NA 90 NA 68 NA NA NA NA
## [721] 35 NA 100 100 NA NA NA NA NA NA NA NA 90 88 NA NA 63 69
## [739] NA 87 NA NA 86 93 NA NA 65 NA NA NA 90 83 NA NA NA NA
## [757] NA 96 100 NA NA 70 NA NA NA NA NA 82 NA NA NA NA NA 97
## [775] 82 NA 18 NA 96 68 NA NA NA 88 17 NA 78 52 100 NA NA NA
## [793] 21 NA NA 80 NA NA 100 100 NA NA 80 100 97 76 69 NA NA NA
## [811] NA 60 NA NA NA 100 65 99 NA NA NA NA 93 63 79 100 88 NA
## [829] NA 100 NA 85 46 NA 19 67 81 69 100 61 79 99 NA 100 100 NA
## [847] 75 52 59 24 100 62 100 99 100 60 89 NA NA NA NA NA NA NA
## [865] NA NA 100 70 88 40 NA 63 NA 81 NA 15 NA 100 66 18 NA NA
## [883] NA 19 52 100 82 NA 73 64 NA NA 100 79 64 NA 98 NA 100 71
## [901] 100 NA NA 60 100 NA NA NA 100 33 84 63 NA NA NA NA 88 88
## [919] NA NA 8 73 NA 100 36 NA 100 93 NA 81 71 100 80 93 77 100
## [937] 75 NA 100 NA 36 NA 96 53 NA 100 67 49 NA 53 NA 72 NA 67
## [955] 50 73 73 0 37 31 NA 44 88 93 100 66 NA 73 99 NA NA NA
## [973] 90 NA 100 NA 100 52 31 100 71 88 83 52 51 50 48 52 70 NA
## [991] NA 46 NA NA 59 NA 72 100 39 58 51 NA 79 NA 33PP$Disgust_GFFB## [1] NA NA NA 95 59 NA 0 0 0 0 0 0 0 0 0 0 17 12
## [19] 15 0 0 0 68 51 100 72 91 NA NA NA NA NA NA NA NA NA
## [37] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [55] NA NA NA 62 NA NA NA 3 NA NA NA NA NA NA NA NA NA 0
## [73] 0 100 NA 0 72 NA NA 0 NA NA NA NA 0 NA NA NA NA 67
## [91] 50 NA NA NA NA 100 NA NA 10 3 NA NA NA NA 0 3 NA NA
## [109] 0 0 86 NA 12 NA NA NA NA NA NA NA NA NA 1 NA 76 NA
## [127] NA 13 NA 10 16 NA NA NA 87 NA NA 0 53 NA NA NA 0 NA
## [145] 0 51 84 NA 22 NA 28 NA 87 NA NA NA NA 22 33 NA NA NA
## [163] NA NA 30 NA NA NA 32 NA NA 31 NA NA 82 NA 0 2 100 NA
## [181] NA NA NA NA NA 40 NA NA 35 36 NA NA NA 11 71 25 46 NA
## [199] 69 NA NA 27 99 55 NA NA 21 66 NA NA NA NA NA NA NA NA
## [217] 100 NA NA NA 58 NA NA NA NA NA NA NA 88 NA NA 38 NA NA
## [235] NA NA 22 NA 33 NA 49 37 NA NA NA NA NA NA NA NA 0 NA
## [253] NA NA 48 100 52 33 NA NA NA 26 NA 89 NA 28 1 NA 27 NA
## [271] 32 100 NA NA NA NA 100 37 NA NA NA NA NA 69 77 32 NA NA
## [289] NA NA 73 NA NA 0 NA NA NA 41 16 36 NA 56 NA NA 56 NA
## [307] NA NA NA 37 61 32 NA NA 37 97 NA NA NA NA 50 64 NA NA
## [325] NA NA NA NA NA 43 100 NA NA NA 23 NA NA NA NA 0 85 20
## [343] 53 NA 52 NA NA NA NA NA NA 69 NA NA NA NA NA 53 NA NA
## [361] 28 57 NA NA 63 NA NA NA NA NA 48 64 NA 20 NA NA NA NA
## [379] NA NA 100 NA NA NA 43 100 NA NA 82 NA NA NA 36 68 28 NA
## [397] 79 34 30 70 34 NA 66 26 NA 74 36 NA NA 100 NA 65 NA NA
## [415] 100 NA NA NA NA 71 NA NA NA 62 NA NA NA NA 60 77 61 NA
## [433] NA NA 34 67 NA 7 NA NA NA NA 90 NA 32 NA NA NA 87 64
## [451] NA 75 NA NA 51 78 NA NA NA 95 NA 79 NA NA NA NA NA 100
## [469] 20 95 NA NA 77 86 100 NA NA 5 76 NA NA NA NA NA NA NA
## [487] 75 NA 50 77 88 NA NA 95 NA NA NA 99 NA 7 NA NA NA NA
## [505] NA NA 100 100 0 100 100 NA NA NA NA NA NA 0 0 1 0 0
## [523] 0 0 0 0 0 0 0 0 16 0 0 4 0 0 3 1 0 3
## [541] 1 4 2 3 6 0 0 9 2 0 10 1 22 17 15 18 16 18
## [559] 10 11 19 4 31 18 9 72 18 11 25 81 51 19 100 17 66 28
## [577] 75 22 0 7 10 3 75 85 10 32 20 34 33 59 0 3 24 38
## [595] 0 81 100 100 0 53 48 92 29 33 23 53 16 99 46 74 79 26
## [613] 25 100 91 7 44 30 52 43 39 30 25 32 66 28 26 52 40 39
## [631] 40 49 30 40 39 5 77 53 40 32 50 55 48 24 39 53 50 6
## [649] 60 33 58 38 38 100 45 50 100 28 51 51 52 54 6 51 66 52
## [667] 49 52 53 52 52 53 52 53 57 84 50 53 53 0 56 86 12 13
## [685] 54 89 38 49 86 81 35 41 82 77 57 60 52 64 72 73 46 75
## [703] 62 52 43 53 74 78 100 22 52 32 63 66 75 36 71 48 64 28
## [721] 39 92 62 52 53 30 67 100 66 42 77 77 15 100 100 68 65 81
## [739] 82 81 74 100 14 26 69 45 69 72 65 78 93 94 64 0 71 11
## [757] 80 42 0 100 72 60 68 1 84 87 72 87 89 91 94 82 65 100
## [775] 19 81 85 91 81 90 100 70 83 79 24 100 86 85 82 100 85 86
## [793] 90 86 88 84 95 100 100 100 100 98 100 100 97 81 100 5 100 100
## [811] 100 99 100 100 100 100 100 100 100 100 100 100 NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Disgust_GFPRB## [1] 13 NA NA 83 0 0 0 0 4 0 0 0 0 0 0 0 0 1
## [19] 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [55] 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2
## [73] 2 0 0 0 7 1 1 0 1 4 2 0 4 4 0 2 0 5
## [91] 0 3 0 3 0 0 0 0 5 0 3 11 12 13 0 8 0 90
## [109] 3 13 0 6 7 0 0 5 3 0 0 81 0 23 90 11 0 51
## [127] 21 5 12 6 7 0 17 14 0 73 23 0 23 10 0 53 18 0
## [145] 9 12 20 7 22 15 30 10 18 19 0 18 17 21 5 0 20 17
## [163] 0 20 24 0 11 15 40 26 19 30 21 18 24 25 0 0 0 100
## [181] 100 50 0 0 0 71 0 24 19 13 17 14 12 30 11 27 5 86
## [199] 0 13 3 6 31 18 51 37 26 23 0 13 27 36 27 66 39 40
## [217] 19 15 0 32 40 25 22 33 100 76 62 19 59 22 67 32 17 22
## [235] 33 3 28 0 38 35 33 31 39 46 97 NA 34 0 65 18 32 18
## [253] 29 0 38 80 35 12 0 52 0 30 39 23 35 27 15 10 31 40
## [271] 79 70 100 80 15 34 77 50 9 35 12 9 20 3 25 81 85 52
## [289] 0 27 60 45 NA 4 66 33 2 52 74 58 45 80 43 10 4 69
## [307] 4 57 50 49 59 66 34 38 36 88 8 57 52 14 46 58 72 20
## [325] 0 30 50 100 50 77 59 56 50 50 7 51 86 51 50 53 18 24
## [343] 43 17 52 52 25 52 51 100 47 67 66 52 35 87 44 52 53 0
## [361] 25 44 24 93 35 29 57 66 6 94 57 40 14 50 38 74 57 67
## [379] 65 2 0 65 28 44 63 74 39 41 37 44 68 64 30 10 94 94
## [397] 27 25 37 60 75 63 36 76 92 74 36 49 56 100 3 71 100 0
## [415] 100 61 22 58 0 62 64 28 26 55 42 84 72 66 60 39 66 74
## [433] 71 100 73 68 83 20 86 50 33 38 12 64 59 52 17 94 81 65
## [451] 24 71 0 77 21 77 30 74 0 85 76 67 40 81 81 80 73 100
## [469] 95 91 68 75 59 87 100 35 95 87 85 12 97 96 92 38 92 91
## [487] 85 89 100 88 91 84 54 72 0 18 82 89 85 91 98 100 100 97
## [505] 100 100 100 100 100 100 100 100 100 100 100 100 NA NA NA NA NA NA
## [523] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [541] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [559] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [577] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [595] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [613] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [631] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [649] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [667] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [685] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [703] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [721] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [739] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [757] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [775] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [793] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [811] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Disgust_CBB## [1] NA NA 100 NA NA 0 NA NA NA NA NA NA NA NA 14 100 NA 0
## [19] NA NA NA NA 100 0 NA NA NA NA NA NA 100 100 21 NA 82 0
## [37] NA NA 0 NA 0 NA NA NA 100 NA 0 0 100 NA 100 NA NA 89
## [55] NA 100 NA NA NA NA 0 NA 1 NA NA NA NA NA 0 NA NA NA
## [73] NA NA 0 NA NA 65 NA NA 100 NA 90 NA NA 13 0 NA 32 NA
## [91] NA NA 100 NA 52 NA NA 6 58 94 NA NA 28 NA NA NA 100 NA
## [109] NA NA NA 19 NA 1 NA 86 NA NA 0 NA 0 NA NA 9 NA NA
## [127] 56 NA NA NA 64 49 NA 100 0 NA NA NA 75 100 NA NA NA 15
## [145] NA NA NA 86 NA NA 10 NA 100 76 47 NA 81 18 60 100 37 NA
## [163] 95 NA 27 NA 100 82 NA NA NA 76 10 NA NA 6 NA NA NA 100
## [181] 100 NA NA NA NA 79 79 NA 78 NA NA 21 11 NA 90 NA 82 NA
## [199] NA NA NA NA NA NA 100 NA NA NA 0 44 18 NA NA NA NA NA
## [217] 28 NA NA NA NA NA NA 9 4 NA NA 8 NA 22 28 38 66 NA
## [235] NA NA NA 4 NA 86 91 40 30 NA NA NA NA 40 81 13 NA 18
## [253] 82 NA NA 51 NA 54 0 0 31 NA NA NA 60 NA NA 9 NA 39
## [271] NA NA NA NA 19 NA NA 61 NA NA NA 96 NA NA NA NA 61 NA
## [289] 52 52 NA 39 44 NA 31 NA NA NA NA NA NA NA 16 100 66 NA
## [307] NA 71 67 NA NA NA NA 55 NA NA 52 NA 32 NA NA NA 7 NA
## [325] NA NA 50 100 NA NA NA NA 53 67 63 NA 65 51 NA NA 87 36
## [343] NA 100 52 NA NA NA 0 100 NA NA 39 3 7 83 NA NA 0 0
## [361] NA 72 30 NA NA 37 NA NA NA 94 NA NA 53 NA NA NA 90 66
## [379] 75 10 NA 17 50 NA NA NA 70 NA NA NA NA NA 38 NA NA NA
## [397] NA NA 0 66 NA NA 7 75 NA NA NA NA NA 100 74 NA NA 10
## [415] 0 NA NA 82 NA NA NA NA 26 NA NA NA NA 64 NA NA NA 96
## [433] NA NA NA NA 100 NA NA NA 78 83 NA 69 NA NA 87 0 NA 65
## [451] NA NA NA NA NA NA 51 NA NA NA 83 NA 76 NA NA 100 NA NA
## [469] NA NA 64 NA NA 86 NA 85 NA NA 100 NA NA NA NA 85 100 86
## [487] NA NA 100 97 72 100 24 NA 8 NA NA NA 98 13 NA 100 NA 100
## [505] NA NA NA 99 100 100 NA 0 100 NA 100 100 NA 0 NA 100 NA NA
## [523] NA 51 NA NA NA NA NA NA 100 100 0 93 100 NA NA 100 NA NA
## [541] 100 NA 92 22 22 NA NA NA NA 100 10 61 85 89 4 NA 86 NA
## [559] 27 NA 5 21 NA 74 NA NA 9 0 26 100 0 21 100 NA NA 22
## [577] NA NA 1 NA NA NA 61 NA NA NA 100 NA 75 81 82 5 26 87
## [595] 100 NA NA 99 0 NA 85 100 23 NA 54 100 NA NA NA 67 NA NA
## [613] 100 52 9 82 19 NA 52 41 66 14 NA NA 94 36 NA NA 42 NA
## [631] NA 49 37 62 NA NA 53 37 NA 60 NA 51 0 74 34 61 45 NA
## [649] NA NA NA NA 66 0 63 50 NA NA NA NA NA 75 NA 18 29 NA
## [667] NA NA 0 0 52 NA NA 53 100 NA NA 75 NA NA NA NA NA 79
## [685] 22 59 NA 64 NA NA 54 NA NA NA 58 NA NA NA 80 NA 47 80
## [703] NA NA 31 100 69 59 0 NA NA 32 NA NA 50 NA 77 58 NA 28
## [721] 72 NA NA NA 1 NA 48 30 70 51 18 NA NA NA 42 58 67 NA
## [739] NA NA 85 NA NA 78 84 NA 18 67 NA 89 NA NA 70 13 NA NA
## [757] NA NA 97 100 5 60 68 100 35 NA 67 79 NA NA 89 NA 90 100
## [775] NA NA NA 84 89 NA 53 NA 86 NA 52 10 NA 80 NA 100 95 92
## [793] 96 NA 87 NA 100 0 88 65 NA NA NA NA NA NA 9 97 100 100
## [811] 100 91 100 100 100 NA NA 8 25 100 NA 100 NA 22 NA NA 87 15
## [829] 65 0 77 73 NA 0 NA 66 NA 0 34 59 88 9 35 0 82 100
## [847] NA 52 68 NA NA 56 0 NA 100 58 76 55 72 0 2 41 100 0
## [865] 8 60 100 98 50 43 25 NA 90 100 0 4 17 35 69 100 31 73
## [883] 43 NA NA 0 66 64 66 97 54 84 NA NA 16 47 NA 90 NA 0
## [901] NA 65 96 NA NA 42 0 72 NA 70 58 19 72 100 32 51 NA 73
## [919] 32 32 51 88 100 80 57 8 78 NA 22 19 66 0 51 NA NA 90
## [937] 80 NA 5 36 NA 42 100 41 38 NA NA NA 24 61 54 43 75 59
## [955] 60 NA 50 50 28 52 99 52 72 NA NA 58 43 NA 10 38 77 30
## [973] 16 94 0 NA 59 76 NA NA 44 NA 74 52 3 50 61 73 68 36
## [991] 52 0 90 59 NA 54 2 69 41 44 NA 73 80 1 NAPP$Disgust_PBPB## [1] NA 44 100 NA 0 NA NA NA NA NA NA NA NA NA NA NA NA NA
## [19] NA 100 NA NA NA NA NA 0 NA NA NA NA 100 NA NA 50 100 NA
## [37] 100 0 NA 1 NA 47 28 NA NA 86 0 NA NA 64 NA 30 36 NA
## [55] 80 100 NA NA NA NA 93 NA NA NA 1 NA NA 7 100 31 63 0
## [73] NA 80 0 NA NA NA NA NA NA NA NA 8 100 NA NA 27 NA 100
## [91] 13 NA 100 NA 36 0 94 NA NA NA 88 37 NA 95 NA NA 97 NA
## [109] 15 NA 0 88 15 2 NA NA NA NA 100 79 NA 56 NA NA NA 100
## [127] 77 NA 14 NA NA NA 31 NA NA NA 57 NA NA NA 0 NA 0 71
## [145] NA NA NA 77 NA NA NA 4 NA NA NA 41 NA NA NA NA NA NA
## [163] 25 21 NA 100 100 NA 32 NA 28 NA NA NA NA NA 100 100 NA NA
## [181] 0 0 NA NA 1 NA NA 22 NA 32 19 27 NA NA NA 37 NA 100
## [199] 43 35 5 19 NA NA NA 23 23 NA 0 0 NA NA 53 12 70 NA
## [217] NA NA 100 69 24 NA 24 10 100 NA 100 NA NA NA NA NA 31 NA
## [235] NA NA NA NA NA 91 NA NA NA NA NA 7 16 25 NA NA NA NA
## [253] 21 27 66 NA NA NA NA NA NA 76 67 39 23 NA 0 NA NA 30
## [271] NA 36 100 23 NA 37 NA NA 32 68 0 8 NA NA NA NA 37 NA
## [289] 2 NA 75 NA 19 6 80 0 3 52 NA 64 63 48 NA NA NA 28
## [307] 0 30 54 NA NA NA 76 NA NA NA NA NA NA 29 50 NA NA NA
## [325] 100 0 50 100 50 72 0 NA NA 60 NA NA NA 49 NA NA NA NA
## [343] 27 NA NA NA 34 52 NA 50 46 NA 50 NA NA NA 67 53 NA NA
## [361] 64 NA 89 NA 66 87 NA 80 18 81 NA NA NA 35 24 58 54 NA
## [379] 74 NA NA 68 61 NA NA 0 44 54 78 NA 67 58 NA NA 100 7
## [397] NA 68 NA NA NA 79 NA NA NA 25 25 70 61 NA 59 14 0 NA
## [415] NA NA NA NA 29 NA NA NA 13 NA NA 64 NA NA NA NA 72 NA
## [433] 78 NA NA NA NA 83 NA 0 NA NA NA NA 73 64 25 NA 79 NA
## [451] NA 66 NA 94 12 NA NA 76 NA NA 96 NA NA 81 NA NA 65 NA
## [469] 89 NA 76 66 77 NA NA NA NA NA NA 82 NA 87 94 NA 0 NA
## [487] NA 36 NA NA NA NA NA NA NA NA 2 88 NA NA NA 79 NA NA
## [505] NA NA NA NA NA NA NA NA NA NA NA NA NA 70 NA 0 0 100
## [523] NA NA 54 0 100 0 0 0 NA 100 4 NA 0 NA 0 NA 100 9
## [541] NA NA 43 NA NA NA NA NA 46 NA 16 71 78 NA NA 35 NA 23
## [559] 24 98 NA NA 37 91 9 100 19 5 NA NA 78 23 72 74 NA 17
## [577] 8 NA NA 6 NA 2 79 7 3 11 NA 100 36 NA NA NA NA NA
## [595] 100 12 NA 5 NA 86 NA NA NA NA NA NA 43 NA 50 NA 84 29
## [613] 86 0 NA 88 NA 100 3 33 67 4 0 0 NA 36 32 NA NA NA
## [631] 63 NA NA 69 58 NA NA NA 29 NA 36 NA NA NA NA 30 NA NA
## [649] 10 46 63 35 NA 100 NA 50 100 79 NA 53 51 NA NA 15 63 14
## [667] 79 52 NA NA NA 52 53 NA NA NA NA NA NA 53 55 78 NA NA
## [685] NA NA 61 NA NA NA NA 27 76 53 74 74 51 NA 48 40 61 NA
## [703] 66 52 46 0 NA 26 NA 31 NA NA 100 74 0 64 40 53 78 NA
## [721] NA 7 0 100 NA 37 98 NA 52 NA 71 68 8 37 NA 70 NA 58
## [739] 23 100 NA 3 NA NA NA 66 NA 68 67 93 0 NA 71 25 69 19
## [757] 33 NA NA 0 5 NA 52 100 NA 88 NA NA 17 34 NA 0 71 NA
## [775] 61 84 95 78 NA 80 NA 54 NA NA NA NA 84 NA NA NA 100 79
## [793] NA 85 NA NA 37 36 NA NA 0 97 NA NA NA NA NA NA NA 100
## [811] NA NA 100 100 0 0 3 NA NA NA 0 NA 100 NA 80 0 NA 77
## [829] 75 NA 71 9 89 63 26 NA 40 8 NA 20 NA NA 42 50 24 50
## [847] 37 NA 68 1 10 NA NA 44 NA 73 80 36 32 0 69 78 79 0
## [865] 7 79 62 98 26 NA 23 64 0 NA 3 NA 27 0 34 75 77 86
## [883] 75 23 53 NA 41 19 NA NA 42 81 100 83 NA 0 100 98 100 NA
## [901] 0 64 100 48 93 34 66 77 82 42 100 19 22 77 69 67 58 NA
## [919] 29 71 NA 72 80 NA 27 100 40 79 30 NA 28 NA NA 88 71 73
## [937] NA 63 0 16 35 62 79 69 72 0 24 91 0 NA 53 NA 29 50
## [955] NA 69 48 NA NA NA 92 NA NA 12 2 64 100 20 2 27 0 7
## [973] 9 16 NA NA 0 83 100 17 84 26 77 44 14 50 25 64 NA 66
## [991] 52 NA 99 74 100 65 NA NA 37 NA 100 89 NA 3 65PP$Disgust_PBFB## [1] NA 40 NA NA NA 0 50 0 100 0 100 NA NA NA NA NA 21 NA
## [19] 100 NA 100 22 NA NA NA NA 0 53 100 83 NA 100 55 50 NA NA
## [37] NA 100 0 5 0 68 71 0 22 NA NA 100 NA 54 59 NA 0 100
## [55] 100 NA 19 NA 100 100 NA 100 NA 79 0 100 10 70 NA NA NA NA
## [73] 100 NA NA NA NA NA 98 NA 23 3 0 8 NA NA 0 31 100 NA
## [91] NA 100 NA 0 NA NA 82 NA NA NA 87 96 45 98 NA NA NA 99
## [109] NA NA NA NA NA NA 100 NA 14 100 NA NA NA NA NA NA NA 100
## [127] NA 19 NA 81 NA 0 NA 52 NA 64 NA NA NA NA 100 53 NA NA
## [145] 14 83 NA NA 15 69 NA NA NA NA NA NA NA NA NA NA NA 50
## [163] NA NA NA 100 NA NA NA 80 NA NA NA 23 NA 31 NA NA NA NA
## [181] NA NA 100 0 0 NA NA NA NA NA NA NA NA 87 NA NA NA NA
## [199] NA NA NA NA NA NA NA NA NA NA NA NA 24 12 NA 68 NA 0
## [217] NA 51 NA 63 NA 9 23 NA NA 0 35 38 NA NA 71 NA NA 17
## [235] 21 31 30 8 56 NA NA NA NA 78 99 NA 23 NA 84 NA NA 100
## [253] NA NA NA NA NA NA 0 NA NA NA 70 NA NA NA NA NA 30 NA
## [271] 16 NA NA NA 19 69 NA NA 42 63 NA NA NA 66 NA 17 NA 70
## [289] NA NA NA NA NA NA NA NA 8 NA 0 NA NA NA NA 24 NA NA
## [307] 3 NA NA NA NA NA 41 NA 65 NA NA 40 NA NA NA NA 78 66
## [325] NA 25 NA NA 50 NA NA 98 51 NA NA 51 NA NA 19 NA NA NA
## [343] NA NA NA 55 NA NA 51 NA NA 48 NA NA NA NA 17 NA NA 1
## [361] NA NA NA 2 NA NA 64 NA 37 NA NA NA 69 NA 22 NA NA 61
## [379] NA 0 87 NA NA 20 NA NA NA 35 NA 40 NA NA NA NA NA NA
## [397] NA NA NA NA NA NA NA NA 90 NA NA NA NA NA NA NA 0 NA
## [415] NA 69 15 69 66 NA 62 18 NA NA 66 77 75 62 69 38 NA NA
## [433] NA 100 11 NA NA NA 35 10 NA NA 95 68 NA NA NA NA NA NA
## [451] 86 NA 0 81 NA NA 19 NA 0 80 NA 23 74 85 79 83 NA 84
## [469] NA NA NA NA NA NA 59 NA 95 NA NA 83 99 89 NA NA NA 81
## [487] NA 77 NA NA NA NA 48 97 17 27 2 NA 78 NA 62 NA 6 100
## [505] 0 100 100 NA NA NA NA NA 100 100 100 NA NA NA 0 NA 3 100
## [523] 69 0 NA 0 NA NA 0 NA NA NA NA 26 NA 100 16 100 NA 26
## [541] NA 95 NA NA NA 14 100 16 16 NA NA NA NA 21 NA NA NA 5
## [559] NA 88 NA 0 100 NA 32 NA NA NA NA 91 NA NA NA 87 74 NA
## [577] NA 27 NA 15 48 6 NA NA NA 11 100 NA NA NA NA 80 NA 61
## [595] NA NA 100 NA NA 100 NA 83 NA 51 NA 96 NA 27 NA NA 97 NA
## [613] NA NA 9 NA NA NA NA NA NA NA NA 100 NA NA NA 77 NA 73
## [631] NA NA NA NA 60 5 NA NA 0 35 NA 64 100 NA 64 NA NA 17
## [649] 94 44 NA 31 NA NA NA NA NA 35 51 54 51 NA 34 NA NA 1
## [667] 95 NA NA NA NA 52 52 NA NA 28 50 100 53 57 38 100 90 NA
## [685] NA NA NA 56 93 92 NA 52 30 NA NA NA 55 32 NA NA NA NA
## [703] 61 70 NA NA NA NA 16 10 36 NA 62 NA NA NA NA NA 67 26
## [721] NA 65 NA NA 29 74 NA 5 NA 21 NA 36 NA NA 32 NA NA NA
## [739] 59 NA 78 2 13 NA 80 27 NA NA 67 NA NA 81 NA NA 74 12
## [757] 75 71 NA NA NA NA NA NA 73 32 22 NA 0 0 91 0 NA NA
## [775] NA 80 NA NA NA NA 15 3 84 81 NA 59 NA NA 88 100 NA NA
## [793] NA 75 53 11 NA NA NA NA 25 96 3 3 100 90 NA 56 100 NA
## [811] 100 NA NA NA NA NA NA NA 78 53 100 100 100 19 83 20 91 39
## [829] 65 0 65 NA 91 71 70 68 83 NA 12 NA 91 6 70 NA NA 50
## [847] 18 57 NA 33 16 66 100 89 50 NA NA 8 66 0 75 25 100 15
## [865] 3 39 NA NA NA 37 29 68 31 100 2 12 100 NA NA NA 34 79
## [883] 100 22 46 100 NA 66 67 97 58 64 100 100 36 47 100 87 100 21
## [901] 0 71 100 79 100 41 38 53 82 NA NA NA 19 34 67 52 63 75
## [919] 31 30 94 NA 76 77 NA 99 NA 84 58 22 NA 0 41 89 71 NA
## [937] 78 43 NA 34 22 38 NA NA 22 0 72 87 17 60 52 89 44 NA
## [955] 100 76 NA 80 17 52 89 31 30 28 100 NA 100 31 NA 38 18 29
## [973] NA 24 95 58 NA NA 99 13 NA 85 NA NA NA NA NA NA 67 20
## [991] 100 39 67 92 99 26 83 0 NA 46 100 19 74 0 66PP$Disgust_VB## [1] NA NA NA NA NA NA NA NA NA NA NA 100 0 0 NA NA NA NA
## [19] NA NA NA NA NA NA 48 NA NA 100 0 88 NA NA NA NA NA 50
## [37] 100 NA NA NA NA NA NA 0 NA 69 NA NA 40 NA NA 18 NA NA
## [55] NA NA 67 47 66 0 NA NA 99 3 NA 100 4 NA NA 33 30 NA
## [73] NA NA NA 100 58 20 100 100 NA 5 NA NA NA 0 NA NA NA NA
## [91] NA 72 NA 0 NA NA NA 22 NA NA NA NA NA NA 54 95 NA 74
## [109] NA 0 NA NA NA NA 100 7 19 100 NA 86 1 52 100 24 0 NA
## [127] NA NA 16 NA NA NA 19 NA NA 20 100 90 NA 10 NA 53 NA NA
## [145] NA NA 35 NA NA 61 NA 52 NA 36 51 62 18 NA NA 33 25 51
## [163] NA 18 NA NA NA 84 NA 78 16 NA 8 10 80 NA NA NA 0 35
## [181] NA 50 0 0 NA NA 80 94 NA NA 20 NA 0 NA NA NA NA 79
## [199] NA 48 7 NA 53 82 51 42 NA 0 NA NA NA 24 28 NA 32 0
## [217] NA 46 100 NA NA 38 NA NA NA 0 NA NA 17 26 NA NA NA 19
## [235] 27 52 NA NA NA NA NA NA 25 55 9 19 NA NA NA 22 0 NA
## [253] NA 92 NA NA 37 NA NA 0 28 NA NA NA NA 27 NA 10 NA NA
## [271] NA NA 98 35 NA NA 0 NA NA NA 60 NA 46 NA 16 NA NA 29
## [289] NA 100 NA 42 NA NA NA 0 NA NA NA NA 11 NA 46 NA NA 33
## [307] NA NA NA 33 58 33 NA 53 NA 8 10 84 65 48 NA 65 NA 16
## [325] 0 NA NA NA NA NA NA 43 NA NA NA 51 63 NA 9 0 NA NA
## [343] NA 70 NA 52 25 52 NA NA 60 NA NA 52 60 80 NA NA 3 NA
## [361] NA NA NA 4 NA NA 62 86 NA NA 61 42 NA NA NA 59 NA NA
## [379] NA NA NA NA NA 0 57 NA NA NA NA 38 71 58 NA 67 NA 11
## [397] 74 NA NA NA NA 70 NA NA 28 NA NA 50 63 NA NA NA NA 0
## [415] NA 76 28 NA NA 73 62 37 NA 53 29 NA 31 NA NA NA NA 82
## [433] 65 100 NA 72 16 NA NA NA 70 55 NA NA NA 53 NA 100 NA NA
## [451] 18 NA 92 NA NA 75 NA 76 0 NA NA NA NA NA 89 NA 0 NA
## [469] NA 91 NA 82 NA NA NA 27 81 86 NA NA 93 NA 84 29 NA NA
## [487] 82 NA NA NA NA 100 NA NA NA 23 NA NA NA NA 91 NA 50 NA
## [505] 0 NA NA NA NA NA 100 0 NA 100 NA 100 100 NA 0 NA NA NA
## [523] 67 NA 52 NA 100 0 NA 0 0 NA NA NA NA NA NA NA 100 NA
## [541] 5 0 NA 5 14 72 0 15 NA 5 NA NA NA NA 37 20 1 NA
## [559] NA NA 4 NA NA NA NA 86 NA NA 100 NA NA NA NA NA 3 NA
## [577] 10 24 21 NA 33 NA NA 13 3 NA NA 100 NA 68 76 NA 9 NA
## [595] NA 0 1 NA 100 NA 92 NA 29 43 70 NA 76 5 22 88 NA 26
## [613] NA NA NA NA 38 100 NA NA NA NA 0 NA 78 NA 61 51 35 76
## [631] 22 16 31 NA NA 0 44 98 NA NA 4 NA NA 39 NA NA 0 76
## [649] NA NA 27 NA 67 NA 51 NA NA NA 51 NA NA 34 24 NA NA NA
## [667] NA 52 27 54 52 NA NA 52 0 28 50 NA 52 NA NA NA 67 21
## [685] 21 30 74 NA 68 9 76 NA NA 26 NA 65 NA 40 NA 32 NA 27
## [703] NA NA NA NA 26 NA NA NA 53 31 NA 81 NA 41 NA NA NA NA
## [721] 65 NA 66 0 NA NA NA NA NA NA NA NA 13 23 NA NA 70 79
## [739] NA 100 NA NA 16 81 NA NA 12 NA NA NA 4 68 NA NA NA NA
## [757] NA 100 0 NA NA 77 NA NA NA NA NA 79 NA NA NA NA NA 0
## [775] 53 NA 84 NA 75 73 NA NA NA 90 21 NA 50 61 92 NA NA NA
## [793] 58 NA NA 1 NA NA 0 0 NA NA 2 1 100 92 61 NA NA NA
## [811] NA 63 NA NA NA 0 65 2 NA NA NA NA 89 49 80 0 85 NA
## [829] NA 0 NA NA 85 NA 91 32 24 19 1 24 57 36 NA 0 100 NA
## [847] 10 53 46 27 19 43 0 46 0 60 89 NA NA NA NA NA NA NA
## [865] NA NA 1 75 13 37 NA 65 NA 0 NA 32 NA 0 51 67 NA NA
## [883] NA 21 52 100 20 NA 74 96 NA NA 30 90 31 NA 0 NA 0 64
## [901] 0 NA NA 50 100 NA NA NA 23 54 42 80 NA NA NA NA 64 77
## [919] NA NA 77 81 NA 46 65 NA 85 25 NA 22 52 0 37 37 64 1
## [937] 83 NA 0 NA 32 NA 100 73 NA 15 78 51 NA 73 NA 52 NA 20
## [955] 80 82 74 100 33 52 NA 44 76 0 1 19 NA 42 6 NA NA NA
## [973] 1 NA 0 NA 0 50 99 0 31 25 23 52 25 50 23 58 49 NA
## [991] NA 45 NA NA 29 NA 99 0 34 40 100 NA 80 NA 74PP$Control_GFFB ## [1] NA NA NA 40 35 NA 100 100 100 100 100 100 100 100 100 0 0 74
## [19] 85 92 100 0 68 51 71 82 96 NA NA NA NA NA NA NA NA NA
## [37] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [55] NA NA NA 3 NA NA NA 93 NA NA NA NA NA NA NA NA NA 100
## [73] 100 100 NA 0 36 NA NA 80 NA NA NA NA 100 NA NA NA NA 39
## [91] 100 NA NA NA NA 100 NA NA 80 81 NA NA NA NA 97 94 NA NA
## [109] 100 100 73 NA 11 NA NA NA NA NA NA NA NA NA 87 NA 77 NA
## [127] NA 82 NA 90 59 NA NA NA 100 NA NA 100 52 NA NA NA 100 NA
## [145] 73 63 85 NA 24 NA 8 NA 85 NA NA NA NA 77 66 NA NA NA
## [163] NA NA 53 NA NA NA 68 NA NA 71 NA NA 71 NA 0 0 0 NA
## [181] NA NA NA NA NA 23 NA NA 75 61 NA NA NA 85 66 76 45 NA
## [199] 72 NA NA 82 50 78 NA NA 56 44 NA NA NA NA NA NA NA NA
## [217] 87 NA NA NA 64 NA NA NA NA NA NA NA 81 NA NA 35 NA NA
## [235] NA NA 71 NA 54 NA 83 64 NA NA NA NA NA NA NA NA 0 NA
## [253] NA NA 61 56 40 78 NA NA NA 26 NA 96 NA 22 100 NA 33 NA
## [271] 31 78 NA NA NA NA 28 29 NA NA NA NA NA 72 77 76 NA NA
## [289] NA NA 77 NA NA 75 NA NA NA 52 100 61 NA 57 NA NA 51 NA
## [307] NA NA NA 42 40 5 NA NA 74 95 NA NA NA NA 51 61 NA NA
## [325] NA NA NA NA NA 55 0 NA NA NA 76 NA NA NA NA 95 88 68
## [343] 57 NA 46 NA NA NA NA NA NA 64 NA NA NA NA NA 56 NA NA
## [361] 33 56 NA NA 62 NA NA NA NA NA 55 49 NA 15 NA NA NA NA
## [379] NA NA 82 NA NA NA 61 100 NA NA 85 NA NA NA 74 83 89 NA
## [397] 81 39 73 66 82 NA 49 32 NA 33 59 NA NA 35 NA 85 NA NA
## [415] 100 NA NA NA NA 73 NA NA NA 57 NA NA NA NA 68 12 65 NA
## [433] NA NA 26 63 NA 77 NA NA NA NA 90 NA 37 NA NA NA 89 64
## [451] NA 72 NA NA 92 75 NA NA NA 87 NA 53 NA NA NA NA NA 69
## [469] 69 96 NA NA 81 81 82 NA NA 92 75 NA NA NA NA NA NA NA
## [487] 81 NA 50 82 87 NA NA 95 NA NA NA 10 NA 86 NA NA NA NA
## [505] NA NA 100 99 0 100 100 NA NA NA NA NA 100 100 100 51 67 100
## [523] 87 100 100 0 80 100 100 100 2 100 78 100 51 1 0 100 100 92
## [541] 31 97 100 93 79 81 63 84 88 0 100 71 50 69 76 9 76 19
## [559] 53 76 18 53 59 79 73 79 6 56 81 6 100 80 99 65 100 100
## [577] 84 28 100 25 82 94 71 95 94 81 11 100 90 56 100 50 21 64
## [595] 100 65 0 62 100 22 63 75 48 72 61 69 62 85 14 80 58 65
## [613] 80 87 84 12 41 29 23 65 67 62 84 86 100 75 60 68 60 25
## [631] 62 58 70 100 41 89 52 45 50 80 61 57 52 25 36 37 56 64
## [649] 66 62 50 64 74 0 49 50 0 37 51 51 51 53 69 52 35 52
## [667] 49 52 0 52 52 53 52 41 54 89 53 53 51 51 47 16 30 63
## [685] 54 78 76 55 34 69 75 41 58 40 60 57 59 32 71 55 52 57
## [703] 56 64 58 4 39 66 69 26 53 72 57 60 85 73 69 62 65 68
## [721] 67 76 73 52 51 87 78 32 63 40 53 69 85 60 90 66 71 61
## [739] 41 51 65 83 79 86 75 0 67 70 65 79 63 84 76 0 72 98
## [757] 65 84 100 0 100 76 89 100 66 87 86 86 53 68 95 100 84 100
## [775] 68 78 79 75 84 94 53 55 91 78 81 26 100 83 87 50 86 83
## [793] 78 80 85 64 NA 100 17 100 100 95 100 86 97 72 91 95 100 92
## [811] 100 98 100 100 99 0 4 88 100 0 50 100 NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Control_GFPRB## [1] NA NA NA 32 89 61 100 100 100 100 100 100 100 100 0 54 76 100
## [19] 21 95 100 17 100 100 100 0 80 50 80 65 100 100 100 100 100 100
## [37] 100 100 0 81 100 31 76 0 100 72 0 88 95 75 100 75 100 100
## [55] 100 100 100 1 100 100 64 100 100 81 0 52 100 95 0 88 0 100
## [73] 97 94 0 100 90 100 50 100 92 100 92 67 98 100 100 38 85 100
## [91] 100 25 65 79 91 86 53 78 74 100 97 13 81 91 81 96 96 91
## [109] 96 9 100 40 11 22 100 100 20 86 74 74 64 52 100 25 100 83
## [127] 75 74 84 84 87 100 80 80 33 60 57 100 80 69 50 53 71 92
## [145] 66 59 96 70 92 81 83 84 22 80 40 88 87 77 24 64 11 77
## [163] 95 90 26 100 100 81 48 24 27 74 88 80 19 75 100 1 100 100
## [181] 0 100 0 55 99 33 50 54 70 73 71 8 62 85 88 89 82 12
## [199] 29 68 43 83 50 70 51 28 66 65 56 78 44 68 54 64 60 25
## [217] 79 76 38 70 63 77 74 76 100 100 73 83 66 70 73 9 36 46
## [235] 35 87 75 71 59 83 68 48 88 72 36 NA 42 75 71 73 35 50
## [253] 79 100 55 62 43 68 100 71 26 65 12 40 100 20 88 81 35 58
## [271] 27 68 88 21 94 37 71 59 100 60 70 100 32 64 38 34 80 53
## [289] 52 52 71 47 44 100 40 26 99 52 100 29 55 67 40 68 51 66
## [307] 73 31 55 69 59 35 36 53 36 90 76 92 56 74 50 54 74 79
## [325] 96 85 50 100 50 41 0 31 45 50 61 51 32 51 73 55 89 26
## [343] 74 87 52 53 78 52 76 0 46 66 53 48 83 76 88 53 53 44
## [361] 82 64 72 31 37 74 56 84 94 23 59 78 74 50 76 84 56 62
## [379] 53 13 85 64 72 63 42 88 65 85 78 62 62 42 88 61 76 51
## [397] 82 68 64 62 70 62 50 82 85 85 65 62 63 79 66 40 100 100
## [415] 51 63 84 68 30 57 60 75 76 53 38 41 56 66 63 45 65 90
## [433] 65 41 30 65 25 66 73 100 69 71 94 100 61 66 89 91 84 66
## [451] 91 68 93 91 16 71 74 83 0 37 52 76 28 82 27 70 25 14
## [469] 25 85 67 77 80 64 21 91 88 76 83 84 82 78 88 70 92 89
## [487] 88 95 0 78 85 82 25 89 89 87 80 19 81 94 95 82 81 99
## [505] 34 85 99 100 74 100 100 100 100 0 100 100 NA NA NA NA NA NA
## [523] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [541] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [559] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [577] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [595] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [613] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [631] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [649] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [667] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [685] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [703] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [721] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [739] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [757] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [775] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [793] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [811] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [829] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [847] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [865] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [883] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [901] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [919] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [937] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [955] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [973] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [991] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NAPP$Control_CBB## [1] NA NA 98 33 NA 100 NA NA NA NA NA NA NA NA 24 0 NA 99
## [19] NA NA NA NA 73 100 NA NA NA NA NA NA 0 100 89 NA 0 100
## [37] NA NA 0 NA 100 NA NA NA 20 NA 100 89 60 NA 56 NA NA 72
## [55] NA 100 NA NA NA NA 72 NA 31 NA NA NA NA NA 100 NA NA NA
## [73] NA NA 100 NA NA 60 NA NA 32 NA 32 NA NA 94 100 NA 71 NA
## [91] NA NA 47 NA 22 NA NA 82 80 91 NA NA 89 NA NA NA 0 NA
## [109] NA NA NA 81 NA 34 NA 90 NA NA 0 NA 31 NA NA 85 NA NA
## [127] 39 NA NA NA 94 50 NA 85 100 NA NA NA 78 87 NA NA NA 86
## [145] NA NA NA 40 NA NA 19 NA 100 68 99 NA 16 100 45 60 62 NA
## [163] 0 NA 52 NA 6 92 NA NA NA 96 17 NA NA 87 NA NA NA 100
## [181] 0 NA NA NA NA 76 70 NA 68 NA NA 72 61 NA 75 NA 73 NA
## [199] NA NA NA NA NA NA 51 NA NA NA 93 78 73 NA NA NA NA NA
## [217] 71 NA NA NA NA NA NA 86 77 NA NA 82 NA 61 69 29 67 NA
## [235] NA NA NA 32 NA 51 50 57 73 NA NA NA NA 40 38 20 NA 83
## [253] 59 NA NA 32 NA 64 100 78 71 NA NA NA 78 NA NA 82 NA 51
## [271] NA NA NA NA 70 NA NA 18 NA NA NA 24 NA NA NA NA 74 NA
## [289] 25 41 NA 37 47 NA 61 NA NA NA NA NA NA NA 70 20 21 NA
## [307] NA 35 43 NA NA NA NA 53 NA NA 52 NA 42 NA NA NA 81 NA
## [325] NA NA 50 0 NA NA NA NA 52 69 82 NA 81 51 NA NA 96 87
## [343] NA 18 52 NA NA NA 73 50 NA NA 39 69 87 78 NA NA 53 54
## [361] NA 63 72 NA NA 29 NA NA NA 90 NA NA 51 NA NA NA 67 36
## [379] 71 89 NA 81 72 NA NA NA 73 NA NA NA NA NA 75 NA NA NA
## [397] NA NA 100 65 52 NA 84 85 NA NA NA NA NA 0 28 NA NA 90
## [415] 51 NA NA 64 NA NA NA NA 28 NA NA NA NA 63 NA NA NA 79
## [433] NA NA NA NA 71 NA NA NA 77 72 NA 70 NA NA 70 100 NA 65
## [451] NA NA NA NA NA NA 84 NA NA NA 76 NA 86 NA NA 100 NA NA
## [469] NA NA 70 NA NA 93 NA 83 NA NA 91 NA NA NA NA 88 25 84
## [487] NA NA 100 80 100 100 91 NA 91 NA NA NA 22 91 NA 100 NA 96
## [505] NA NA NA 100 100 100 NA 0 100 NA 16 100 100 50 NA 15 NA NA
## [523] NA 51 NA NA NA NA NA NA 0 0 72 24 100 NA NA 74 NA NA
## [541] 100 NA 100 8 81 NA NA NA NA 0 85 82 88 69 65 NA 21 NA
## [559] 78 NA 6 29 NA 75 NA NA 1 7 40 100 0 23 89 NA NA 88
## [577] NA NA 100 NA NA NA 74 NA NA NA 21 NA 93 87 96 85 58 30
## [595] 100 NA NA 15 0 NA 52 89 45 NA 39 5 NA NA NA 66 NA NA
## [613] 34 91 96 93 37 NA 40 52 65 49 NA NA 92 62 NA NA 63 NA
## [631] NA 51 65 100 NA NA 52 35 NA 72 NA 44 0 34 64 52 47 NA
## [649] NA NA NA NA 67 53 51 52 NA NA NA NA NA 23 NA 13 60 NA
## [667] NA NA 0 100 56 NA NA 53 50 NA NA 53 NA NA NA NA NA 80
## [685] 75 76 NA 54 NA NA 43 NA NA NA 59 NA NA NA 72 NA 59 58
## [703] NA NA 27 0 40 68 100 NA NA 73 NA NA 98 NA 39 47 NA 71
## [721] 62 NA NA NA 9 NA 91 66 12 46 74 NA NA NA 100 50 33 NA
## [739] NA NA 57 NA NA 32 78 NA 32 74 NA 100 NA NA 66 81 NA NA
## [757] NA NA 84 9 100 78 58 98 64 NA 69 83 NA NA 88 NA 88 0
## [775] NA NA NA 87 90 NA 53 NA 81 NA 24 38 NA 83 NA 19 91 88
## [793] 93 NA 94 NA 52 100 24 66 NA NA NA NA NA NA 73 100 81 100
## [811] 100 83 100 100 99 NA NA 0 22 0 NA 100 NA 29 NA NA 89 81
## [829] 69 87 70 83 NA 100 NA 70 NA 0 98 57 27 99 60 50 21 3
## [847] NA 52 63 NA NA 63 0 NA 56 67 92 100 69 100 23 80 22 66
## [865] 91 64 0 76 60 70 96 NA 45 50 80 100 58 85 63 58 31 90
## [883] 12 NA NA 6 93 61 70 9 68 70 NA NA 82 49 NA 91 NA 100
## [901] NA 76 43 NA NA 65 100 61 NA 56 47 84 76 4 33 65 NA 100
## [919] 77 74 62 78 78 95 25 64 39 NA 78 73 52 100 0 NA NA 62
## [937] 74 NA 80 53 NA 71 100 74 69 NA NA NA 44 56 52 37 69 75
## [955] 50 NA 50 80 38 52 90 47 100 NA NA 64 52 NA 99 65 72 28
## [973] 78 78 1 32 69 24 NA NA 69 NA 13 53 87 50 48 65 30 67
## [991] 47 1 52 46 NA 32 69 73 40 52 NA 16 79 100 NAPP$Control_PBPB## [1] NA 45 87 NA 100 NA NA NA NA NA NA NA NA NA NA NA NA NA
## [19] NA 80 NA NA NA NA NA 2 NA NA NA NA 100 NA NA 100 0 NA
## [37] 0 100 NA 100 NA 76 100 NA NA 38 100 NA NA 100 NA 74 100 NA
## [55] 63 97 NA NA NA NA 68 NA NA NA 0 NA NA 79 0 16 0 100
## [73] NA 100 60 NA NA NA NA NA NA NA NA 79 100 NA NA 43 NA 87
## [91] 81 NA 36 NA 29 79 53 NA NA NA 90 64 NA 94 NA NA 40 NA
## [109] 80 NA 100 97 90 26 NA NA NA NA 0 86 NA 52 NA NA NA 83
## [127] 83 NA 59 NA NA NA 66 NA NA NA 67 NA NA NA 100 NA 100 71
## [145] NA NA NA 31 NA NA NA 82 NA NA NA 97 NA NA NA NA NA NA
## [163] 60 87 NA 52 74 NA 79 NA 31 NA NA NA NA NA 100 72 NA NA
## [181] 0 50 NA NA 99 NA NA 87 NA 72 80 84 NA NA NA 64 NA 0
## [199] 82 69 77 89 NA NA NA 33 64 NA 52 75 NA NA 73 66 53 NA
## [217] NA NA 79 42 58 NA 79 84 38 NA 100 NA NA NA NA NA 76 NA
## [235] NA NA NA NA NA 53 NA NA NA NA NA 38 81 80 NA NA NA NA
## [253] 85 87 71 NA NA NA NA NA NA 60 75 61 100 NA 100 NA NA 51
## [271] NA 65 100 21 NA 53 NA NA 100 65 78 85 NA NA NA NA 54 NA
## [289] 52 NA 28 NA 26 100 53 20 100 52 NA 28 49 55 NA NA NA 82
## [307] 94 35 45 NA NA NA 38 NA NA NA NA NA NA 28 47 NA NA NA
## [325] 100 75 50 0 50 67 100 NA NA 58 NA NA NA 51 NA NA NA NA
## [343] 60 NA NA NA 69 52 NA 55 55 NA 42 NA NA NA 76 53 NA NA
## [361] 75 NA 67 NA 35 58 NA 73 100 93 NA NA NA 50 53 59 66 NA
## [379] 72 NA NA 62 66 NA NA 100 63 58 75 NA 75 60 NA NA 80 80
## [397] NA 64 NA NA NA 65 NA NA NA 18 35 70 52 NA 60 88 100 NA
## [415] NA NA NA NA 91 NA NA NA 88 NA NA 54 NA NA NA NA 70 NA
## [433] 33 NA NA NA NA 82 NA 100 NA NA NA NA 64 60 72 NA 79 NA
## [451] NA 64 NA 93 78 NA NA 88 NA NA 80 NA NA 79 NA NA 27 NA
## [469] 78 NA 83 60 93 NA NA NA NA NA NA 88 NA 85 89 NA 100 NA
## [487] NA 28 NA NA NA NA NA NA NA NA 68 16 NA NA NA 11 NA NA
## [505] NA NA NA NA NA NA NA NA NA NA NA NA NA 85 NA 52 87 0
## [523] NA NA 58 0 100 100 100 100 NA 100 27 NA 64 NA 100 NA 77 82
## [541] NA NA 38 NA NA NA NA NA 47 NA 98 71 75 NA NA 74 NA 10
## [559] 23 30 NA NA 67 91 27 76 15 0 NA NA 0 89 37 41 NA 93
## [577] 82 NA NA 65 NA 79 74 100 89 81 NA 64 69 NA NA NA NA NA
## [595] 100 90 NA 98 NA 53 NA NA NA NA NA NA 53 NA 100 NA 58 28
## [613] 73 83 NA 80 NA 60 52 71 70 17 61 0 NA 39 40 NA NA NA
## [631] 14 NA NA 100 67 NA NA NA 61 NA 38 NA NA NA NA 52 NA NA
## [649] 77 74 66 79 NA 100 NA 90 100 NA NA 54 52 NA NA 87 6 100
## [667] 29 55 NA NA NA 53 53 NA NA NA NA NA NA 56 59 56 NA NA
## [685] NA NA 65 NA NA NA NA 34 84 53 66 NA 52 NA 73 43 54 NA
## [703] 72 52 43 71 NA 30 NA 19 NA NA 59 26 50 25 71 63 35 NA
## [721] NA 100 100 29 NA 67 71 NA 14 NA 70 76 85 61 NA 63 NA 58
## [739] 52 20 NA 3 NA NA NA 74 NA 62 64 79 3 NA 63 0 67 100
## [757] 56 NA NA 100 100 NA 63 100 NA 86 NA NA 52 100 NA 100 70 NA
## [775] 63 81 22 83 NA 75 NA 55 NA NA NA NA 82 NA NA NA 100 95
## [793] NA 85 NA NA 49 100 NA NA 100 96 NA NA NA NA NA NA NA 99
## [811] NA NA 100 100 100 66 100 NA NA NA 100 NA 85 NA 26 100 NA 76
## [829] 63 NA 38 71 55 29 35 NA 75 69 NA 75 NA NA 54 50 88 49
## [847] 47 NA 60 84 97 NA NA 62 NA 45 89 100 28 100 71 77 79 73
## [865] 95 70 37 85 79 NA 93 70 90 NA 90 NA 45 100 74 100 24 78
## [883] 37 72 53 NA 66 44 NA NA 61 87 100 15 NA 47 85 100 100 NA
## [901] 100 71 100 55 75 65 100 53 97 69 55 38 79 21 33 60 65 NA
## [919] 83 71 NA 76 82 NA 30 43 68 94 65 NA 84 NA NA 74 61 50
## [937] NA 65 90 82 34 56 92 64 70 80 33 75 74 NA 55 NA 69 51
## [955] NA 63 65 NA NA NA 99 NA NA 81 100 61 69 71 98 52 98 31
## [973] 100 82 NA NA 23 86 19 53 71 30 70 47 3 50 57 60 NA 27
## [991] 52 NA 77 65 37 79 NA NA 34 NA 51 12 NA 98 55PP$Control_PBFB## [1] NA 44 NA NA NA 100 52 100 100 100 0 NA NA NA NA NA 71 NA
## [19] 11 NA 100 18 NA NA NA NA 100 100 0 20 NA 0 87 100 NA NA
## [37] NA 100 100 0 100 83 100 0 84 NA NA 78 NA 68 58 NA 100 36
## [55] 100 NA 0 NA 1 100 NA 100 NA 100 0 52 93 23 NA NA NA NA
## [73] 0 NA NA NA NA NA 52 NA 71 100 88 86 NA NA 100 27 80 NA
## [91] NA 100 NA 3 NA NA 75 NA NA NA 63 91 90 80 NA NA NA 4
## [109] NA NA NA NA NA NA 80 NA 91 76 NA NA NA NA NA NA NA 51
## [127] NA 76 NA 83 NA 26 NA 88 NA 87 NA NA NA NA 50 53 NA NA
## [145] 70 85 NA NA 81 27 NA NA NA NA NA NA NA NA NA NA NA 36
## [163] NA NA NA 52 NA NA NA 74 NA NA NA 77 NA 80 NA NA NA NA
## [181] NA NA 98 50 100 NA NA NA NA NA NA NA NA 73 NA NA NA NA
## [199] NA NA NA NA NA NA NA NA NA NA NA NA 50 67 NA 64 NA 78
## [217] NA 57 NA 36 NA 73 74 NA NA 100 80 58 NA NA 42 NA NA 20
## [235] 29 85 73 91 66 NA NA NA NA 64 5 NA 77 NA 52 NA NA 100
## [253] NA NA NA NA NA NA 100 NA NA NA 34 NA NA NA NA NA 39 NA
## [271] 11 NA NA NA 95 69 NA NA 85 59 NA NA NA 84 NA 60 NA 52
## [289] NA NA NA NA NA NA NA NA 100 NA 100 NA NA NA NA 67 NA NA
## [307] 96 NA NA NA NA NA 20 NA 70 NA NA 44 NA NA NA NA 75 89
## [325] NA 80 NA NA 50 NA NA 0 52 NA NA 51 NA NA 94 NA NA NA
## [343] NA NA NA 52 NA NA 63 NA NA 53 NA NA NA NA 75 NA NA 50
## [361] NA NA NA 10 NA NA 59 NA 100 NA NA NA 38 NA 82 NA NA 60
## [379] NA 92 76 NA NA 78 NA NA NA 36 NA 66 NA NA NA NA NA NA
## [397] NA NA NA NA NA NA NA NA 76 NA NA NA NA NA NA NA 100 NA
## [415] NA 76 82 66 20 NA 73 68 NA NA 100 11 78 65 64 65 NA NA
## [433] NA 100 20 NA NA NA 85 100 NA NA 69 67 NA NA NA NA NA NA
## [451] 71 NA 90 93 NA NA 88 NA 100 55 NA 76 72 81 33 24 NA 60
## [469] NA NA NA NA NA NA 85 NA 100 NA NA 89 71 73 NA NA NA 81
## [487] NA 74 NA NA NA NA 83 95 77 71 79 NA 83 NA 66 NA 92 96
## [505] 100 35 100 NA NA NA NA NA 100 100 16 NA NA NA 100 NA 53 0
## [523] 93 51 NA 0 NA NA 59 NA NA NA NA 92 NA 0 15 100 NA 81
## [541] NA 5 NA NA NA 88 0 84 46 NA NA NA NA 83 NA NA NA 12
## [559] NA 78 NA 0 45 NA 81 NA NA NA NA 97 NA NA NA 33 100 NA
## [577] NA 16 NA 33 52 96 NA NA NA 83 30 NA NA NA NA 40 NA 63
## [595] NA NA 100 NA NA 52 NA 24 NA 60 NA 68 NA 88 NA NA 53 NA
## [613] NA NA 95 NA NA NA NA NA NA NA NA 50 NA NA NA 29 NA 18
## [631] NA NA NA NA 63 85 NA NA 66 28 NA 56 0 NA 8 NA NA 55
## [649] 68 61 NA 74 NA NA NA NA NA 64 51 54 51 NA 66 NA NA 98
## [667] 3 NA NA NA NA 52 52 NA NA 7 75 53 52 54 38 14 28 NA
## [685] NA NA NA 63 38 74 NA 43 62 NA NA NA 52 35 NA NA NA NA
## [703] 44 58 NA NA NA NA 71 94 43 NA 57 NA NA NA NA NA 66 59
## [721] NA 100 NA NA 29 29 NA 0 NA 46 NA 80 NA NA 100 NA NA NA
## [739] 62 NA 91 96 83 NA 75 77 NA NA 52 NA NA 78 NA NA 69 75
## [757] 74 72 NA NA NA NA NA NA 67 84 17 NA 85 100 88 100 NA NA
## [775] NA 75 NA NA NA NA 72 50 72 87 NA 57 NA NA 81 100 NA NA
## [793] NA 81 84 81 NA NA NA NA 100 97 71 98 100 12 NA 98 100 NA
## [811] 100 NA NA NA NA NA NA NA 75 53 65 100 73 38 34 90 96 72
## [829] 69 92 43 NA 64 79 80 34 89 NA 67 NA 69 96 64 NA NA 50
## [847] 15 35 NA 96 98 68 0 50 49 NA NA 97 79 100 38 80 59 52
## [865] 89 70 NA NA NA 35 100 72 80 50 91 4 64 NA NA NA 31 83
## [883] 29 38 52 12 NA 52 68 43 37 12 100 2 64 46 99 99 0 100
## [901] 94 78 0 39 29 65 100 47 80 NA NA NA 45 72 70 61 64 81
## [919] 81 76 87 NA 80 74 NA 27 NA 81 72 39 NA 100 62 58 81 NA
## [937] 71 71 NA 70 24 43 NA NA 63 71 22 80 73 60 44 89 51 NA
## [955] 98 70 NA 33 33 52 88 59 92 24 100 NA 79 63 NA 57 80 31
## [973] NA 89 92 40 NA NA 41 69 NA 81 NA NA NA NA NA NA 79 71
## [991] 54 37 52 58 12 82 85 43 NA 41 61 78 73 90 51PP$Control_VB ## [1] NA NA NA NA NA NA NA NA NA NA NA 100 100 100 NA NA NA NA
## [19] NA NA NA NA NA NA 100 NA NA 100 100 26 NA NA NA NA NA 100
## [37] 100 NA NA NA NA NA NA 0 NA 67 NA NA 100 NA NA 23 NA NA
## [55] NA NA 86 1 13 100 NA NA 99 100 NA 52 100 NA NA 3 52 NA
## [73] NA NA NA 0 11 85 93 58 NA 100 NA NA NA 100 NA NA NA NA
## [91] NA 94 NA 0 NA NA NA 83 NA NA NA NA NA NA 45 94 NA 97
## [109] NA 100 NA NA NA NA 100 83 89 21 NA 77 41 52 88 22 80 NA
## [127] NA NA 80 NA NA NA 83 NA NA 28 100 100 NA 91 NA 53 NA NA
## [145] NA NA 85 NA NA 83 NA 83 NA 34 83 100 90 NA NA 96 23 76
## [163] NA 95 NA NA NA 42 NA 31 29 NA 22 67 64 NA NA NA 100 73
## [181] NA 50 100 50 NA NA 82 73 NA NA 79 NA 64 NA NA NA NA 51
## [199] NA 80 78 NA 31 48 20 21 NA 100 NA NA NA 65 73 NA 76 93
## [217] NA 66 70 NA NA 83 NA NA NA 85 NA NA 84 52 NA NA NA 43
## [235] 50 95 NA NA NA NA NA NA 77 76 53 45 NA NA NA 86 0 NA
## [253] NA 100 NA NA 52 NA NA 100 70 NA NA NA NA 29 NA 81 NA NA
## [271] NA NA 100 81 NA NA 73 NA NA NA 100 NA 88 NA 67 NA NA 52
## [289] NA 34 NA 57 NA NA NA 3 NA NA NA NA 51 NA 51 NA NA 71
## [307] NA NA NA 100 51 41 NA 67 NA 90 0 100 57 36 NA 59 NA 39
## [325] 8 NA NA NA NA NA NA 23 NA NA NA 51 75 NA 100 80 NA NA
## [343] NA 16 NA 52 58 52 NA NA 45 NA NA 52 96 81 NA NA 100 NA
## [361] NA NA NA 19 NA NA 61 33 NA NA 61 51 NA NA NA 60 NA NA
## [379] NA NA NA NA NA 100 60 NA NA NA NA 63 61 40 NA 61 NA 55
## [397] 39 NA NA NA NA 92 NA NA 69 NA NA 69 69 NA NA NA NA 95
## [415] NA 74 91 NA NA 66 61 64 NA 53 87 NA 70 NA NA NA NA 20
## [433] 18 100 NA 60 25 NA NA NA 70 68 NA NA NA 59 NA 100 NA NA
## [451] 88 NA 100 NA NA 73 NA 73 34 NA NA NA NA NA 21 NA 77 NA
## [469] NA 68 NA 82 NA NA NA 41 100 100 NA NA 96 NA 90 89 NA NA
## [487] 98 NA NA NA NA 11 NA NA NA 96 NA NA NA NA 66 NA 82 NA
## [505] 100 NA NA NA NA NA 100 53 NA 1 NA 100 100 NA 100 NA NA NA
## [523] 70 NA 52 NA 83 100 NA 100 60 NA NA NA NA NA NA NA 100 NA
## [541] 28 9 NA 80 64 69 100 65 NA 98 NA NA NA NA 73 14 90 NA
## [559] NA NA 65 NA NA NA NA 82 NA NA 1 NA NA NA NA NA 100 NA
## [577] 16 20 100 NA 82 NA NA 13 87 NA NA 0 NA 100 75 NA 26 NA
## [595] NA 89 100 NA 0 NA 99 NA 25 31 78 NA 60 100 29 92 NA 57
## [613] NA NA NA NA 43 100 NA NA NA NA 58 NA 88 NA 30 56 71 38
## [631] 59 67 69 NA NA 99 52 45 NA NA 50 NA NA 66 NA NA 0 4
## [649] NA NA 61 NA 62 NA 74 NA NA NA 51 NA NA 52 89 NA NA NA
## [667] NA 52 0 52 52 NA NA 52 100 25 50 NA 57 NA NA NA 26 53
## [685] 80 63 34 NA 62 100 77 NA NA 51 NA 34 NA 58 NA 85 NA 72
## [703] NA NA NA NA 65 NA NA NA 52 81 NA 80 NA 80 NA NA NA NA
## [721] 57 NA 98 100 NA NA NA NA NA NA NA NA 85 72 NA NA 67 70
## [739] NA 52 NA NA 79 86 NA NA 82 NA NA NA 76 72 NA NA NA NA
## [757] NA 100 69 NA NA 70 NA NA NA NA NA 94 NA NA NA NA NA 81
## [775] 66 NA 86 NA 83 74 NA NA NA 72 78 NA 83 65 95 NA NA NA
## [793] 28 NA NA 89 NA NA 100 64 NA NA 64 53 99 84 100 NA NA NA
## [811] NA 47 NA NA NA 44 77 93 NA NA NA NA 83 44 22 80 90 NA
## [829] NA 91 NA 77 29 NA 25 29 26 72 98 30 85 93 NA 61 100 NA
## [847] 81 52 75 82 76 55 0 44 100 64 86 NA NA NA NA NA NA NA
## [865] NA NA 100 20 71 32 NA 58 NA 50 NA 69 NA 100 53 96 NA NA
## [883] NA 75 52 2 85 NA 71 35 NA NA 100 79 74 NA 74 NA 68 74
## [901] 100 NA NA 53 90 NA NA NA 15 54 62 85 NA NA NA NA 65 80
## [919] NA NA 70 97 NA 89 71 NA 69 12 NA 75 40 97 42 82 65 100
## [937] 70 NA 95 NA 31 NA 98 60 NA 50 45 50 NA 55 NA 85 NA 77
## [955] 95 72 82 100 35 52 NA 46 71 44 100 95 NA 35 99 NA NA NA
## [973] 93 NA 100 NA 100 85 38 100 37 36 74 52 0 50 80 61 63 NA
## [991] NA 22 NA NA 62 NA 52 100 43 59 51 NA 77 NA 36length(PP$Naturalness_Score_GFFB_Tot)## [1] 1005length(PP$Naturalness_Score_GFPRB_Tot)## [1] 1005length(PP$Naturalness_Score_CBB_Tot)## [1] 1005length(PP$Naturalness_Score_PBPB_Tot)## [1] 1005length(PP$Naturalness_Score_PBFB_Tot)## [1] 1005length(PP$Naturalness_Score_VB_Tot)## [1] 1005length(PP$Behav_Score_GFFB)## [1] 1005length(PP$Behav_Score_GFPRB)## [1] 1005length(PP$Behav_Score_CBB)## [1] 1005length(PP$Behav_Score_PBPB)## [1] 1005length(PP$Behav_Score_PBFB)## [1] 1005length(PP$Behav_Score_VB)## [1] 1005length(PP$Ben_Score_GFFB)## [1] 1005length(PP$Ben_Score_GFPRB)## [1] 1005length(PP$Ben_Score_CBB)## [1] 1005length(PP$Ben_Score_PBPB)## [1] 1005length(PP$Ben_Score_PBFB)## [1] 1005length(PP$Ben_Score_VB)## [1] 1005length(PP$Control_GFFB )## [1] 1005length(PP$Control_GFPRB)## [1] 1005length(PP$Control_CBB)## [1] 1005length(PP$Control_PBPB)## [1] 1005length(PP$Control_PBFB)## [1] 1005length(PP$Control_VB)## [1] 1005length(PP$Familiarity_GFFB)## [1] 1005length(PP$Familiarity_GFPRB) ## [1] 1005length(PP$Familiarity_CBB)## [1] 1005length(PP$Familiarity_PBPB)## [1] 1005length(PP$Familiarity_PBFB)## [1] 1005length(PP$Familiarity_VB )## [1] 1005length(PP$Understanding_GFFB)## [1] 1005length(PP$Understanding_GFPRB)## [1] 1005length(PP$Understanding_CBB)## [1] 1005length(PP$Understanding_PBPB)## [1] 1005length(PP$Understanding_PBFB )## [1] 1005length(PP$Understanding_VB)## [1] 1005length(PP$Risk_Score_GFFB)## [1] 1005length(PP$Risk_Score_GFPRB)## [1] 1005length(PP$Risk_Score_CBB)## [1] 1005length(PP$Risk_Score_PBPB)## [1] 1005length(PP$Risk_Score_PBFB)## [1] 1005length(PP$Risk_Score_VB)## [1] 1005length(PP$Disgust_GFFB)## [1] 1005length(PP$Disgust_GFPRB)## [1] 1005length(PP$Disgust_CBB)## [1] 1005length(PP$Disgust_PBPB)## [1] 1005length(PP$Disgust_PBFB)## [1] 1005length(PP$Disgust_VB)## [1] 1005length(PP$CCBelief_Score)## [1] 1005length(PP$CNS_Score)## [1] 1005length(PP$DS_Score)## [1] 1005length(PP$Ideology)## [1] 1005length(PP$ATNS_Score )## [1] 1005length(PP$AW_Score )## [1] 1005length(PP$Collectivism_Score)## [1] 1005length(PP$Individualism_Score)## [1] 1005Long Form
#Renaming variables to fit pivot_longer command
PP$Naturalness.GFFB <- PP$Naturalness_Score_GFFB_Tot
length(PP$Naturalness.GFFB)## [1] 1005PP$Naturalness.GFPRB <- PP$Naturalness_Score_GFPRB_Tot
length(PP$Naturalness.GFPRB)## [1] 1005PP$Naturalness.CBB <- PP$Naturalness_Score_CBB_Tot
length(PP$Naturalness.CBB)## [1] 1005PP$Naturalness.PBPB <- PP$Naturalness_Score_PBPB_Tot
length(PP$Naturalness.PBPB)## [1] 1005PP$Naturalness.PBFB <- PP$Naturalness_Score_PBFB_Tot
length(PP$Naturalness.PBFB)## [1] 1005PP$Naturalness.VB <- PP$Naturalness_Score_VB_Tot
length(PP$Naturalness.VB)## [1] 1005PP$Behav.GFFB <- PP$Behav_Score_GFFB
length(PP$Behav.GFFB)## [1] 1005PP$Behav.GFPRB <- PP$Behav_Score_GFPRB
length(PP$Behav.GFPRB)## [1] 1005PP$Behav.CBB <- PP$Behav_Score_CBB
length(PP$Behav.CBB)## [1] 1005PP$Behav.PBPB <- PP$Behav_Score_PBPB
length(PP$Behav.PBPB)## [1] 1005PP$Behav.PBFB <- PP$Behav_Score_PBFB
length(PP$Behav.PBFB)## [1] 1005PP$Behav.VB <- PP$Behav_Score_VB
length(PP$Behav.VB)## [1] 1005PP$Familiarity.GFFB <- PP$Familiarity_GFFB
length(PP$Familiarity.GFFB)## [1] 1005PP$Familiarity.GFPRB <- PP$Familiarity_GFPRB
length(PP$Familiarity.GFPRB)## [1] 1005PP$Familiarity.CBB <- PP$Familiarity_CBB
length(PP$Familiarity.CBB)## [1] 1005PP$Familiarity.PBPB <- PP$Familiarity_PBPB
length(PP$Familiarity.PBPB)## [1] 1005PP$Familiarity.PBFB <- PP$Familiarity_PBFB
length(PP$Familiarity.PBFB)## [1] 1005PP$Familiarity.VB <- PP$Familiarity_VB
length(PP$Familiarity.VB)## [1] 1005PP$Understanding.GFFB <- PP$Understanding_GFFB
length(PP$Understanding.GFFB)## [1] 1005PP$Understanding.GFPRB <- PP$Understanding_GFPRB
length(PP$Understanding.GFPRB)## [1] 1005PP$Understanding.CBB <- PP$Understanding_CBB
length(PP$Understanding.CBB)## [1] 1005PP$Understanding.PBPB <- PP$Understanding_PBPB
length(PP$Understanding.PBPB)## [1] 1005PP$Understanding.PBFB <- PP$Understanding_PBFB
length(PP$Understanding.PBFB)## [1] 1005PP$Understanding.VB <- PP$Understanding_VB
length(PP$Understanding.VB)## [1] 1005PP$Disgust.GFFB <-PP$Disgust_GFFB
length(PP$Disgust.GFFB)## [1] 1005PP$Disgust.GFPRB <-PP$Disgust_GFPRB
length(PP$Disgust.GFPRB)## [1] 1005PP$Disgust.CBB <-PP$Disgust_CBB
length(PP$Disgust.CBB)## [1] 1005PP$Disgust.PBPB <-PP$Disgust_PBPB
length(PP$Disgust.PBPB)## [1] 1005PP$Disgust.PBFB <-PP$Disgust_PBFB
length(PP$Disgust.PBFB)## [1] 1005PP$Disgust.VB <-PP$Disgust_VB
length(PP$Disgust.VB)## [1] 1005PP$Control.GFFB <- PP$Control_GFFB
length(PP$Control.GFFB)## [1] 1005PP$Control.GFPRB <- PP$Control_GFPRB
length(PP$Control.GFPRB)## [1] 1005PP$Control.CBB <- PP$Control_CBB
length(PP$Control.CBB)## [1] 1005PP$Control.PBPB <- PP$Control_PBPB
length(PP$Control.PBPB)## [1] 1005PP$Control.PBFB <- PP$Control_PBFB
length(PP$Control.PBFB)## [1] 1005PP$Control.VB <- PP$Control_VB
length(PP$Control.VB)## [1] 1005PP$Ben.GFFB <- PP$Ben_Score_GFFB
length(PP$Ben.GFFB)## [1] 1005PP$Ben.GFPRB <- PP$Ben_Score_GFPRB
length(PP$Ben.GFPRB)## [1] 1005PP$Ben.CBB <- PP$Ben_Score_CBB
length(PP$Ben.CBB) ## [1] 1005PP$Ben.PBPB <- PP$Ben_Score_PBPB
length(PP$Ben.PBPB)## [1] 1005PP$Ben.PBFB <- PP$Ben_Score_PBFB
length(PP$Ben.PBFB) ## [1] 1005PP$Ben.VB <- PP$Ben_Score_VB
length(PP$Ben.VB) ## [1] 1005##Risk Length
PP$Risk.GFFB <- PP$Risk_Score_GFFB
length(PP$Risk.GFFB)## [1] 1005PP$Risk.GFPRB <- PP$Risk_Score_GFPRB
length(PP$Risk.GFPRB)## [1] 1005PP$Risk.CBB <- PP$Risk_Score_CBB
length(PP$Risk.CBB)## [1] 1005PP$Risk.PBPB <- PP$Risk_Score_PBPB
length(PP$Risk.PBPB) ## [1] 1005PP$Risk.PBFB <- PP$Risk_Score_PBFB
length(PP$Risk.PBFB)## [1] 1005PP$Risk.VB <- PP$Risk_Score_VB
length(PP$Risk.VB)## [1] 1005library(lmerTest)##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## steplibrary(lme4)
#Reshape to long form
PPvector <- c("Naturalness.GFFB", "Naturalness.GFPRB", "Naturalness.CBB","Naturalness.PBPB", "Naturalness.PBFB", "Naturalness.VB", "Behav.GFFB", "Behav.GFPRB", "Behav.CBB","Behav.PBPB", "Behav.PBFB", "Behav.VB", "Familiarity.GFFB","Familiarity.GFPRB", "Familiarity.CBB", "Familiarity.PBPB","Familiarity.PBFB", "Familiarity.VB", "Understanding.GFFB", "Understanding.GFPRB", "Understanding.CBB", "Understanding.PBPB", "Understanding.PBFB", "Understanding.VB", "Disgust.GFFB", "Disgust.GFPRB", "Disgust.CBB", "Disgust.PBPB", "Disgust.PBFB","Disgust.VB", "Ben.GFFB","Ben.GFPRB","Ben.CBB", "Ben.PBPB", "Ben.PBFB", "Ben.VB", "Risk.GFFB", "Risk.GFPRB","Risk.CBB","Risk.PBPB", "Risk.PBFB", "Risk.VB", "BRDiff.GFFB", "BRDiff.GFPRB", "BRDiff.CBB", "BRDiff.PBPB", "BRDiff.PBFB", "BRDiff.VB", "FR.GFFB", "FR.GFFB", "FR.GFPRB", "FR.PBPB", "FR.PBFB", "FR.VB")
L <- reshape(data = PP,
varying = PPvector,
timevar = "Type",
direction = "long")Correlations
length(L$Ben)## [1] 6030length(L$FR)## [1] 6030length(L$Naturalness)## [1] 6030length(L$Risk)## [1] 6030length(L$Behav)## [1] 6030length(L$AW_Score)## [1] 6030length(L$ATNS_Score)## [1] 6030length(L$CCBelief_Score)## [1] 6030length(L$CNS_Score)## [1] 6030length(L$Individualism_Score)## [1] 6030length(L$Ideology)## [1] 6030length(L$Ideology)## [1] 6030length(L$SESNum)## [1] 6030length(L$EduNum)## [1] 0length(L$Dem_Age)## [1] 6030length(L$Dem_Gen)## [1] 6030L$corR <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Behav, L$Dem_Age, L$Dem_Gen, L$EdNum, L$SESNum, L$AW_Score, L$ATNS_Score, L$CCBelief_Score, L$Collectivism_Score, L$CNS_Score, PP$DS_Score, L$Individualism_Score, L$Ideology)
mydata.cor11A = cor(L$corR, use = "pairwise.complete.obs")
head(round(mydata.cor11A,2))## L.Ben L.FR L.Naturalness L.Risk L.Behav L.Dem_Age L.Dem_Gen
## L.Ben 1.00 0.44 0.27 -0.26 0.80 -0.10 0.06
## L.FR 0.44 1.00 0.10 -0.09 0.48 -0.01 0.06
## L.Naturalness 0.27 0.10 1.00 -0.39 0.28 0.05 0.01
## L.Risk -0.26 -0.09 -0.39 1.00 -0.25 -0.08 -0.02
## L.Behav 0.80 0.48 0.28 -0.25 1.00 -0.18 0.07
## L.Dem_Age -0.10 -0.01 0.05 -0.08 -0.18 1.00 0.00
## L.EdNum L.SESNum L.AW_Score L.ATNS_Score L.CCBelief_Score
## L.Ben 0.06 0.06 0.20 0.05 0.23
## L.FR 0.08 0.08 0.20 0.10 0.22
## L.Naturalness 0.02 0.02 0.02 0.00 0.02
## L.Risk -0.03 -0.03 0.02 0.11 -0.03
## L.Behav 0.08 0.06 0.22 0.01 0.25
## L.Dem_Age 0.08 0.06 0.06 0.03 0.02
## L.Collectivism_Score L.CNS_Score PP.DS_Score
## L.Ben 0.24 0.06 0.05
## L.FR 0.19 0.09 -0.03
## L.Naturalness -0.01 0.04 -0.01
## L.Risk 0.08 -0.08 0.04
## L.Behav 0.19 0.06 0.02
## L.Dem_Age 0.10 0.11 0.04
## L.Individualism_Score L.Ideology
## L.Ben 0.17 -0.06
## L.FR 0.20 -0.03
## L.Naturalness 0.01 0.03
## L.Risk -0.03 -0.05
## L.Behav 0.12 -0.05
## L.Dem_Age 0.08 0.00library("Hmisc")
mydata.rcorr11A = rcorr(as.matrix(mydata.cor11A))
mydata.rcorr11A## L.Ben L.FR L.Naturalness L.Risk L.Behav L.Dem_Age
## L.Ben 1.00 0.71 0.46 -0.57 0.97 -0.38
## L.FR 0.71 1.00 0.21 -0.36 0.73 -0.27
## L.Naturalness 0.46 0.21 1.00 -0.74 0.46 -0.04
## L.Risk -0.57 -0.36 -0.74 1.00 -0.55 -0.09
## L.Behav 0.97 0.73 0.46 -0.55 1.00 -0.45
## L.Dem_Age -0.38 -0.27 -0.04 -0.09 -0.45 1.00
## L.Dem_Gen -0.02 -0.04 -0.02 -0.09 0.01 -0.09
## L.EdNum -0.08 -0.05 -0.05 -0.12 -0.04 0.03
## L.SESNum -0.08 -0.07 -0.05 -0.11 -0.07 0.00
## L.AW_Score 0.20 0.24 -0.09 -0.05 0.21 -0.08
## L.ATNS_Score -0.13 -0.04 -0.23 0.18 -0.17 -0.05
## L.CCBelief_Score 0.27 0.29 -0.05 -0.15 0.28 -0.14
## L.Collectivism_Score 0.21 0.19 -0.17 0.06 0.15 -0.03
## L.CNS_Score -0.04 0.02 -0.04 -0.16 -0.04 0.08
## PP.DS_Score -0.08 -0.20 -0.15 0.11 -0.12 -0.01
## L.Individualism_Score 0.12 0.18 -0.13 -0.07 0.07 -0.01
## L.Ideology -0.26 -0.24 -0.01 -0.08 -0.24 -0.04
## L.Dem_Gen L.EdNum L.SESNum L.AW_Score L.ATNS_Score
## L.Ben -0.02 -0.08 -0.08 0.20 -0.13
## L.FR -0.04 -0.05 -0.07 0.24 -0.04
## L.Naturalness -0.02 -0.05 -0.05 -0.09 -0.23
## L.Risk -0.09 -0.12 -0.11 -0.05 0.18
## L.Behav 0.01 -0.04 -0.07 0.21 -0.17
## L.Dem_Age -0.09 0.03 0.00 -0.08 -0.05
## L.Dem_Gen 1.00 0.24 0.34 -0.39 -0.38
## L.EdNum 0.24 1.00 0.57 -0.28 -0.31
## L.SESNum 0.34 0.57 1.00 -0.36 -0.33
## L.AW_Score -0.39 -0.28 -0.36 1.00 0.47
## L.ATNS_Score -0.38 -0.31 -0.33 0.47 1.00
## L.CCBelief_Score -0.32 -0.19 -0.26 0.70 0.39
## L.Collectivism_Score -0.28 -0.27 -0.13 0.39 0.43
## L.CNS_Score -0.27 -0.15 -0.26 0.49 0.43
## PP.DS_Score -0.34 -0.21 -0.23 0.04 0.21
## L.Individualism_Score -0.36 -0.30 -0.24 0.62 0.54
## L.Ideology -0.11 -0.11 -0.07 -0.18 -0.09
## L.CCBelief_Score L.Collectivism_Score L.CNS_Score
## L.Ben 0.27 0.21 -0.04
## L.FR 0.29 0.19 0.02
## L.Naturalness -0.05 -0.17 -0.04
## L.Risk -0.15 0.06 -0.16
## L.Behav 0.28 0.15 -0.04
## L.Dem_Age -0.14 -0.03 0.08
## L.Dem_Gen -0.32 -0.28 -0.27
## L.EdNum -0.19 -0.27 -0.15
## L.SESNum -0.26 -0.13 -0.26
## L.AW_Score 0.70 0.39 0.49
## L.ATNS_Score 0.39 0.43 0.43
## L.CCBelief_Score 1.00 0.25 0.58
## L.Collectivism_Score 0.25 1.00 0.04
## L.CNS_Score 0.58 0.04 1.00
## PP.DS_Score -0.05 0.30 -0.19
## L.Individualism_Score 0.56 0.67 0.41
## L.Ideology -0.23 -0.29 -0.02
## PP.DS_Score L.Individualism_Score L.Ideology
## L.Ben -0.08 0.12 -0.26
## L.FR -0.20 0.18 -0.24
## L.Naturalness -0.15 -0.13 -0.01
## L.Risk 0.11 -0.07 -0.08
## L.Behav -0.12 0.07 -0.24
## L.Dem_Age -0.01 -0.01 -0.04
## L.Dem_Gen -0.34 -0.36 -0.11
## L.EdNum -0.21 -0.30 -0.11
## L.SESNum -0.23 -0.24 -0.07
## L.AW_Score 0.04 0.62 -0.18
## L.ATNS_Score 0.21 0.54 -0.09
## L.CCBelief_Score -0.05 0.56 -0.23
## L.Collectivism_Score 0.30 0.67 -0.29
## L.CNS_Score -0.19 0.41 -0.02
## PP.DS_Score 1.00 0.16 -0.16
## L.Individualism_Score 0.16 1.00 -0.14
## L.Ideology -0.16 -0.14 1.00
##
## n= 17
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Behav L.Dem_Age
## L.Ben 0.0015 0.0607 0.0163 0.0000 0.1283
## L.FR 0.0015 0.4153 0.1547 0.0009 0.2855
## L.Naturalness 0.0607 0.4153 0.0007 0.0609 0.8795
## L.Risk 0.0163 0.1547 0.0007 0.0209 0.7178
## L.Behav 0.0000 0.0009 0.0609 0.0209 0.0706
## L.Dem_Age 0.1283 0.2855 0.8795 0.7178 0.0706
## L.Dem_Gen 0.9465 0.8936 0.9350 0.7394 0.9819 0.7280
## L.EdNum 0.7729 0.8455 0.8534 0.6544 0.8662 0.9101
## L.SESNum 0.7580 0.7975 0.8484 0.6839 0.7988 0.9892
## L.AW_Score 0.4330 0.3558 0.7417 0.8359 0.4115 0.7498
## L.ATNS_Score 0.6190 0.8698 0.3821 0.4988 0.5120 0.8560
## L.CCBelief_Score 0.2959 0.2657 0.8602 0.5729 0.2702 0.5800
## L.Collectivism_Score 0.4152 0.4632 0.5234 0.8100 0.5684 0.8949
## L.CNS_Score 0.8839 0.9309 0.8823 0.5425 0.8916 0.7538
## PP.DS_Score 0.7539 0.4498 0.5781 0.6846 0.6384 0.9772
## L.Individualism_Score 0.6538 0.4875 0.6065 0.8035 0.8013 0.9553
## L.Ideology 0.3179 0.3532 0.9565 0.7636 0.3473 0.8713
## L.Dem_Gen L.EdNum L.SESNum L.AW_Score L.ATNS_Score
## L.Ben 0.9465 0.7729 0.7580 0.4330 0.6190
## L.FR 0.8936 0.8455 0.7975 0.3558 0.8698
## L.Naturalness 0.9350 0.8534 0.8484 0.7417 0.3821
## L.Risk 0.7394 0.6544 0.6839 0.8359 0.4988
## L.Behav 0.9819 0.8662 0.7988 0.4115 0.5120
## L.Dem_Age 0.7280 0.9101 0.9892 0.7498 0.8560
## L.Dem_Gen 0.3566 0.1791 0.1209 0.1341
## L.EdNum 0.3566 0.0175 0.2734 0.2299
## L.SESNum 0.1791 0.0175 0.1601 0.1909
## L.AW_Score 0.1209 0.2734 0.1601 0.0557
## L.ATNS_Score 0.1341 0.2299 0.1909 0.0557
## L.CCBelief_Score 0.2072 0.4740 0.3149 0.0018 0.1207
## L.Collectivism_Score 0.2727 0.2964 0.6081 0.1265 0.0814
## L.CNS_Score 0.2983 0.5535 0.3102 0.0448 0.0879
## PP.DS_Score 0.1777 0.4257 0.3732 0.8901 0.4246
## L.Individualism_Score 0.1542 0.2447 0.3448 0.0079 0.0238
## L.Ideology 0.6791 0.6778 0.7821 0.4797 0.7298
## L.CCBelief_Score L.Collectivism_Score L.CNS_Score
## L.Ben 0.2959 0.4152 0.8839
## L.FR 0.2657 0.4632 0.9309
## L.Naturalness 0.8602 0.5234 0.8823
## L.Risk 0.5729 0.8100 0.5425
## L.Behav 0.2702 0.5684 0.8916
## L.Dem_Age 0.5800 0.8949 0.7538
## L.Dem_Gen 0.2072 0.2727 0.2983
## L.EdNum 0.4740 0.2964 0.5535
## L.SESNum 0.3149 0.6081 0.3102
## L.AW_Score 0.0018 0.1265 0.0448
## L.ATNS_Score 0.1207 0.0814 0.0879
## L.CCBelief_Score 0.3251 0.0144
## L.Collectivism_Score 0.3251 0.8684
## L.CNS_Score 0.0144 0.8684
## PP.DS_Score 0.8543 0.2389 0.4668
## L.Individualism_Score 0.0186 0.0031 0.1028
## L.Ideology 0.3799 0.2645 0.9282
## PP.DS_Score L.Individualism_Score L.Ideology
## L.Ben 0.7539 0.6538 0.3179
## L.FR 0.4498 0.4875 0.3532
## L.Naturalness 0.5781 0.6065 0.9565
## L.Risk 0.6846 0.8035 0.7636
## L.Behav 0.6384 0.8013 0.3473
## L.Dem_Age 0.9772 0.9553 0.8713
## L.Dem_Gen 0.1777 0.1542 0.6791
## L.EdNum 0.4257 0.2447 0.6778
## L.SESNum 0.3732 0.3448 0.7821
## L.AW_Score 0.8901 0.0079 0.4797
## L.ATNS_Score 0.4246 0.0238 0.7298
## L.CCBelief_Score 0.8543 0.0186 0.3799
## L.Collectivism_Score 0.2389 0.0031 0.2645
## L.CNS_Score 0.4668 0.1028 0.9282
## PP.DS_Score 0.5505 0.5336
## L.Individualism_Score 0.5505 0.6009
## L.Ideology 0.5336 0.6009library(corrplot)
corrplot(mydata.cor11A, method="color")
corrplot(mydata.cor11A, addCoef.col = 1, number.cex = 0.3, method = 'number')
### Tech Ratings
L$corT <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Behav)
mydata.cor11AT = cor(L$corT, use = "pairwise.complete.obs")
head(round(mydata.cor11AT,2))## L.Ben L.FR L.Naturalness L.Risk L.Behav
## L.Ben 1.00 0.44 0.27 -0.26 0.80
## L.FR 0.44 1.00 0.10 -0.09 0.48
## L.Naturalness 0.27 0.10 1.00 -0.39 0.28
## L.Risk -0.26 -0.09 -0.39 1.00 -0.25
## L.Behav 0.80 0.48 0.28 -0.25 1.00library("Hmisc")
mydata.rcorr11AT = rcorr(as.matrix(mydata.cor11AT))
mydata.rcorr11AT## L.Ben L.FR L.Naturalness L.Risk L.Behav
## L.Ben 1.00 0.55 0.35 -0.78 0.96
## L.FR 0.55 1.00 0.01 -0.49 0.58
## L.Naturalness 0.35 0.01 1.00 -0.82 0.34
## L.Risk -0.78 -0.49 -0.82 1.00 -0.78
## L.Behav 0.96 0.58 0.34 -0.78 1.00
##
## n= 5
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Behav
## L.Ben 0.3385 0.5665 0.1194 0.0109
## L.FR 0.3385 0.9929 0.4066 0.3024
## L.Naturalness 0.5665 0.9929 0.0883 0.5705
## L.Risk 0.1194 0.4066 0.0883 0.1196
## L.Behav 0.0109 0.3024 0.5705 0.1196library(corrplot)
corrplot(mydata.cor11AT, method="color")
corrplot(mydata.cor11AT, addCoef.col = 1, number.cex = 0.3, method = 'number')
Mixed Effects Models
Center Variables
table(L$Type) ##
## CBB GFFB GFPRB PBFB PBPB VB
## 1005 1005 1005 1005 1005 1005describe(L$Ben) ## L$Ben
## n missing distinct Info Mean Gmd .05 .10
## 2996 3034 296 0.999 60.36 30.63 2.333 22.667
## .25 .50 .75 .90 .95
## 44.667 61.000 82.000 98.167 100.000
##
## lowest : 0.0000000 0.3333333 0.6666667 1.0000000 1.3333333
## highest: 98.6666667 99.0000000 99.3333333 99.6666667 100.0000000describe(L$Control) ##
## NULLdescribe(L$Familiarity) ## L$Familiarity
## n missing distinct Info Mean Gmd .05 .10
## 2996 3034 101 0.998 57.8 36.28 0 6
## .25 .50 .75 .90 .95
## 32 63 85 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$Naturalness) ## L$Naturalness
## n missing distinct Info Mean Gmd .05 .10
## 3006 3024 399 1 50.22 26.67 7.00 22.50
## .25 .50 .75 .90 .95
## 34.75 49.38 65.50 82.00 96.19
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00describe(L$Risk) ## L$Risk
## n missing distinct Info Mean Gmd .05 .10
## 3319 2711 506 1 44.56 32.81 0.00 2.50
## .25 .50 .75 .90 .95
## 21.25 46.50 65.00 84.00 95.00
##
## lowest : 0.0000000 0.2500000 0.3333333 0.5000000 0.7500000
## highest: 99.2500000 99.5000000 99.6666667 99.7500000 100.0000000describe(L$Behav) ## L$Behav
## n missing distinct Info Mean Gmd .05 .10
## 3008 3022 402 0.999 56.27 34.23 0.000 6.925
## .25 .50 .75 .90 .95
## 36.000 58.500 80.250 96.750 100.000
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00describe(L$Understanding) ## L$Understanding
## n missing distinct Info Mean Gmd .05 .10
## 2999 3031 101 0.995 65.01 32.66 5 21
## .25 .50 .75 .90 .95
## 47 70 89 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100describe(L$BRDiff) ## L$BRDiff
## n missing distinct Info Mean Gmd .05 .10
## 2993 3037 1596 1 15.62 48.87 -68.333 -33.200
## .25 .50 .75 .90 .95
## -5.917 7.250 45.750 80.400 96.700
##
## lowest : -100.00000 -99.75000 -99.66667 -99.33333 -99.00000
## highest: 99.33333 99.50000 99.66667 99.75000 100.00000describe(L$FR) ## L$FR
## n missing distinct Info Mean Gmd .05 .10
## 2986 3044 197 0.999 63.81 29.08 16.00 28.25
## .25 .50 .75 .90 .95
## 49.12 64.50 85.00 99.50 100.00
##
## lowest : 0.0 0.5 1.0 1.5 2.5, highest: 98.0 98.5 99.0 99.5 100.0L$Benefit.c <- L$Ben - 60.36
L$Familiarity.c <- L$Familiarity - 57.8
L$Naturalness.c <- L$Naturalness - 46.43
L$Risk.c <- L$Risk - 44.56
L$Behav.c <- L$Behav - mean(L$Behav) - 56.27
L$Understanding.c <- L$Understanding - 65.01
L$FR.c <- L$FR - 63.81
describe(L$AW_Score)## L$AW_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 172 0.995 70.53 27.4 25.0 39.5
## .25 .50 .75 .90 .95
## 52.0 73.5 92.5 100.0 100.0
##
## lowest : 0.0 1.0 2.0 2.5 3.0, highest: 98.0 98.5 99.0 99.5 100.0L$AW_Score.c <- L$AW_Score - 70.53
describe(L$ATNS_Score)## L$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 313 1 62.48 19.38 34.2 42.4
## .25 .50 .75 .90 .95
## 51.2 62.0 73.4 84.4 94.8
##
## lowest : 0.0 1.0 2.4 3.6 7.2, highest: 98.8 99.2 99.6 99.8 100.0L$ATNS_Score.c <- L$ATNS_Score - 62.48
describe(L$CCBelief_Score)## L$CCBelief_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 275 0.997 72.51 25.71 31.75 44.75
## .25 .50 .75 .90 .95
## 56.00 75.25 93.25 100.00 100.00
##
## lowest : 0.00 0.50 0.75 1.00 1.25, highest: 99.00 99.25 99.50 99.75 100.00L$CCBelief_Score.c <- L$CCBelief_Score - 72.51
describe(L$CNS_Score)## L$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 240 1 57.53 12.96 40.0 45.0
## .25 .50 .75 .90 .95
## 50.6 56.6 61.8 71.8 80.2
##
## lowest : 18.0 20.0 21.8 22.2 22.6, highest: 98.0 98.8 99.6 99.8 100.0L$CNS_Score.c <- L$CNS_Score - 57.53
describe(L$DS_Score)## L$DS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 237 1 57.55 23.19 20.67 32.67
## .25 .50 .75 .90 .95
## 45.33 58.67 67.67 86.00 96.00
##
## lowest : 0.0000000 0.6666667 1.6666667 2.6666667 3.6666667
## highest: 98.0000000 98.3333333 99.3333333 99.6666667 100.0000000L$DS_Score.c <- L$DS_Score - 57.55
describe(L$Individualism_Score)## L$Individualism_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 250 0.999 73.77 20.34 45.50 51.00
## .25 .50 .75 .90 .95
## 60.50 75.00 88.00 98.75 100.00
##
## lowest : 0.00 11.50 22.75 25.00 27.75, highest: 99.00 99.25 99.50 99.75 100.00L$Individualism_Score.c <- L$Individualism_Score - 73.77
describe(L$Collectivism_Score)## L$Collectivism_Score
## n missing distinct Info Mean Gmd .05 .10
## 6018 12 292 1 66.53 23.13 30.25 40.00
## .25 .50 .75 .90 .95
## 52.25 66.75 81.75 94.00 100.00
##
## lowest : 0.00 1.25 7.00 9.50 10.50, highest: 99.00 99.25 99.50 99.75 100.00L$Collectivism_Score.c <- L$Collectivism_Score - 66.53
describe(L$Ben)## L$Ben
## n missing distinct Info Mean Gmd .05 .10
## 2996 3034 296 0.999 60.36 30.63 2.333 22.667
## .25 .50 .75 .90 .95
## 44.667 61.000 82.000 98.167 100.000
##
## lowest : 0.0000000 0.3333333 0.6666667 1.0000000 1.3333333
## highest: 98.6666667 99.0000000 99.3333333 99.6666667 100.0000000L$Benefit.c <- L$Ben - 60.36
describe(L$Disgust)## L$Disgust
## n missing distinct Info Mean Gmd .05 .10
## 2996 3034 101 0.997 47.64 39.28 0 0
## .25 .50 .75 .90 .95
## 17 50 77 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100L$Disgust.c <- L$Disgust - 47.64
describe(L$Familiarity)## L$Familiarity
## n missing distinct Info Mean Gmd .05 .10
## 2996 3034 101 0.998 57.8 36.28 0 6
## .25 .50 .75 .90 .95
## 32 63 85 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100L$Familiarity.c <- L$Familiarity - 57.8
describe(L$Naturalness)## L$Naturalness
## n missing distinct Info Mean Gmd .05 .10
## 3006 3024 399 1 50.22 26.67 7.00 22.50
## .25 .50 .75 .90 .95
## 34.75 49.38 65.50 82.00 96.19
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00L$Naturalness.c <- L$Naturalness - 46.43
describe(L$Risk)## L$Risk
## n missing distinct Info Mean Gmd .05 .10
## 3319 2711 506 1 44.56 32.81 0.00 2.50
## .25 .50 .75 .90 .95
## 21.25 46.50 65.00 84.00 95.00
##
## lowest : 0.0000000 0.2500000 0.3333333 0.5000000 0.7500000
## highest: 99.2500000 99.5000000 99.6666667 99.7500000 100.0000000L$Risk.c <- L$Risk - 44.56
describe(L$Behav)## L$Behav
## n missing distinct Info Mean Gmd .05 .10
## 3008 3022 402 0.999 56.27 34.23 0.000 6.925
## .25 .50 .75 .90 .95
## 36.000 58.500 80.250 96.750 100.000
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00L$Behav.c <- L$Behav - 56.27
describe(L$Understanding)## L$Understanding
## n missing distinct Info Mean Gmd .05 .10
## 2999 3031 101 0.995 65.01 32.66 5 21
## .25 .50 .75 .90 .95
## 47 70 89 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100L$Understanding.c <- L$Understanding - 65.01
describe(L$FR)## L$FR
## n missing distinct Info Mean Gmd .05 .10
## 2986 3044 197 0.999 63.81 29.08 16.00 28.25
## .25 .50 .75 .90 .95
## 49.12 64.50 85.00 99.50 100.00
##
## lowest : 0.0 0.5 1.0 1.5 2.5, highest: 98.0 98.5 99.0 99.5 100.0L$FR.c <- L$FR - 63.81
describe(L$BRDiff)## L$BRDiff
## n missing distinct Info Mean Gmd .05 .10
## 2993 3037 1596 1 15.62 48.87 -68.333 -33.200
## .25 .50 .75 .90 .95
## -5.917 7.250 45.750 80.400 96.700
##
## lowest : -100.00000 -99.75000 -99.66667 -99.33333 -99.00000
## highest: 99.33333 99.50000 99.66667 99.75000 100.00000Contrast Codes
#CBB, PBPB,PBFB vs. VB, GFPRB, GFFB - NEWTECH
L$C1 <- (1/2)*(L$Type == 'CBB') + (-1/2)*(L$Type == 'GFFB') + (-1/2)*(L$Type == 'GFPRB') +(1/2)*(L$Type == 'PBFB') +(1/2)*(L$Type == 'PBPB') + (-1/2)*(L$Type == 'VB')
#CBB vs. PFPB, PBPB - LABCULTURED
L$C2 <- (2/3)*(L$Type == 'CBB') + (-1/3)*(L$Type == 'PBFB') + (-1/3)*(L$Type == 'PBPB') +(0)*(L$Type == 'VB') + (0)*(L$Type == 'GFFB') + (0)*(L$Type == 'GFPRB')
#GFFB, GFPRB vs. CBB, PFPB, PBPB, VB - TRADITIONAL BEEF
L$C3 <- (0)*(L$Type == 'CBB') + (0)*(L$Type == 'PBFB') + (0)*(L$Type == 'PBPB') +(2/3)*(L$Type == 'VB') + (-1/3)*(L$Type == 'GFFB') + (-1/3)*(L$Type == 'GFPRB')
#GFFB vs. GFPRB - GRAIN FED BEEF vs. GRASS FED BEEF
L$C4 <- (0)*(L$Type == 'CBB') + (0)*(L$Type == 'PBFB') + (0)*(L$Type == 'PBPB') + (0)*(L$Type == 'VB') + (1/2)*(L$Type == 'GFFB') + (-1/2)*(L$Type == 'GFPRB')
#VB vs. GFPRB - VEGGIE vs. GRASS FED (Orthogonality Code - Not meaningful comparison)
L$C5 <- (0)*(L$Type == 'CBB') + (1/2)*(L$Type == 'PBFB') + (-1/2)*(L$Type == 'PBPB') + (0)*(L$Type == 'VB') + (0)*(L$Type == 'GFFB') + (0)*(L$Type == 'GFPRB')ANOVAs
Support (Behavioral Intent)
modA.1 <- lmer(Behav ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.1 <- lmer(Behav ~ 1 + (1|id), data = L)
summary(modA.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28169.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5456 -0.4179 0.0521 0.4301 3.3434
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 449.2 21.19
## Residual 431.8 20.78
## Number of obs: 3008, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.4847 0.7747 1001.8181 72.908 < 2e-16 ***
## C1 -6.4914 0.8047 2201.0504 -8.066 1.18e-15 ***
## C2 -6.1986 1.2296 2292.4556 -5.041 4.99e-07 ***
## C3 5.1803 1.2795 2341.2427 4.049 5.32e-05 ***
## C4 0.2924 1.4180 2293.3067 0.206 0.836669
## C5 -5.1045 1.4401 2316.5191 -3.545 0.000401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.003
## C2 -0.020 0.006
## C3 -0.007 -0.057 0.027
## C4 -0.017 -0.063 0.051 0.000
## C5 -0.014 0.067 -0.029 0.064 0.074tab_model(modA.1,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.48 | 0.77 | 54.97 – 58.00 | 72.91 | <0.001 |
| C1 | -6.49 | 0.80 | -8.07 – -4.91 | -8.07 | <0.001 |
| C2 | -6.20 | 1.23 | -8.61 – -3.79 | -5.04 | <0.001 |
| C3 | 5.18 | 1.28 | 2.67 – 7.69 | 4.05 | <0.001 |
| C4 | 0.29 | 1.42 | -2.49 – 3.07 | 0.21 | 0.837 |
| C5 | -5.10 | 1.44 | -7.93 – -2.28 | -3.54 | <0.001 |
| Random Effects | |||||
| σ2 | 431.78 | ||||
| τ00 id | 449.18 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 3008 | ||||
| Marginal R2 / Conditional R2 | 0.022 / 0.521 | ||||
summary(modC.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28293.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3388 -0.4207 0.0627 0.4499 3.2169
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 443.6 21.06
## Residual 454.7 21.32
## Number of obs: 3008, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.3747 0.7759 1002.0581 72.66 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.1,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.37 | 0.78 | 54.85 – 57.90 | 72.66 | <0.001 |
| Random Effects | |||||
| σ2 | 454.75 | ||||
| τ00 id | 443.56 | ||||
| ICC | 0.49 | ||||
| N id | 1003 | ||||
| Observations | 3008 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.494 | ||||
anova(modC.1, modA.1)## refitting model(s) with ML (instead of REML)Naturalness
modA.2 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.2 <- lmer(Naturalness ~ 1 + (1|id), data = L)## boundary (singular) fit: see ?isSingularsummary(modA.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26923.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92273 -0.65184 -0.03425 0.61938 3.08384
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.535 1.239
## Residual 455.241 21.336
## Number of obs: 3006, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.3942 0.3914 1002.1123 128.753 < 2e-16 ***
## C1 -8.1457 0.7797 2793.3500 -10.447 < 2e-16 ***
## C2 -17.9940 1.1570 2769.4763 -15.552 < 2e-16 ***
## C3 -4.6214 1.1903 2759.6843 -3.883 0.000106 ***
## C4 -12.9528 1.3423 2791.1878 -9.650 < 2e-16 ***
## C5 19.8994 1.3486 2753.0711 14.755 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.012
## C2 -0.009 -0.009
## C3 0.023 -0.024 0.000
## C4 0.008 -0.008 0.000 -0.008
## C5 0.025 0.025 -0.025 0.000 0.000tab_model(modA.2,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.63 – 51.16 | 128.75 | <0.001 |
| C1 | -8.15 | 0.78 | -9.67 – -6.62 | -10.45 | <0.001 |
| C2 | -17.99 | 1.16 | -20.26 – -15.73 | -15.55 | <0.001 |
| C3 | -4.62 | 1.19 | -6.96 – -2.29 | -3.88 | <0.001 |
| C4 | -12.95 | 1.34 | -15.58 – -10.32 | -9.65 | <0.001 |
| C5 | 19.90 | 1.35 | 17.26 – 22.54 | 14.76 | <0.001 |
| Random Effects | |||||
| σ2 | 455.24 | ||||
| τ00 id | 1.54 | ||||
| ICC | 0.00 | ||||
| N id | 1004 | ||||
| Observations | 3006 | ||||
| Marginal R2 / Conditional R2 | 0.185 / 0.187 | ||||
summary(modC.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27548.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1231 -0.6539 -0.0356 0.6461 2.1047
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.0 0.00
## Residual 559.5 23.65
## Number of obs: 3006, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.2170 0.4314 3005.0000 116.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingulartab_model(modC.2,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.22 | 0.43 | 49.37 – 51.06 | 116.40 | <0.001 |
| Random Effects | |||||
| σ2 | 559.47 | ||||
| τ00 id | 0.00 | ||||
| N id | 1004 | ||||
| Observations | 3006 | ||||
| Marginal R2 / Conditional R2 | 0.000 / NA | ||||
anova(modC.2, modA.2)## refitting model(s) with ML (instead of REML)Risk
modA.3 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.3 <- lmer(Risk ~ 1 + (1|id), data = L)
summary(modA.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30939.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94921 -0.55876 0.01162 0.51048 2.97133
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 329.8 18.16
## Residual 455.4 21.34
## Number of obs: 3319, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.4194 0.6869 1017.0632 64.663 < 2e-16 ***
## C1 5.9265 0.8118 2645.8442 7.300 3.79e-13 ***
## C2 9.4518 1.1967 2620.9392 7.898 4.13e-15 ***
## C3 -7.7368 1.2733 2595.6429 -6.076 1.41e-09 ***
## C4 8.3421 1.4406 2612.5948 5.791 7.84e-09 ***
## C5 2.5476 1.2299 2412.7516 2.071 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.044
## C2 0.031 0.049
## C3 0.013 -0.039 0.006
## C4 0.005 -0.001 0.004 -0.016
## C5 -0.065 -0.134 0.129 0.008 -0.007tab_model(modA.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.69 | 43.07 – 45.77 | 64.66 | <0.001 |
| C1 | 5.93 | 0.81 | 4.33 – 7.52 | 7.30 | <0.001 |
| C2 | 9.45 | 1.20 | 7.11 – 11.80 | 7.90 | <0.001 |
| C3 | -7.74 | 1.27 | -10.23 – -5.24 | -6.08 | <0.001 |
| C4 | 8.34 | 1.44 | 5.52 – 11.17 | 5.79 | <0.001 |
| C5 | 2.55 | 1.23 | 0.14 – 4.96 | 2.07 | 0.038 |
| Random Effects | |||||
| σ2 | 455.41 | ||||
| τ00 id | 329.82 | ||||
| ICC | 0.42 | ||||
| N id | 1004 | ||||
| Observations | 3319 | ||||
| Marginal R2 / Conditional R2 | 0.035 / 0.440 | ||||
summary(modC.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 31126.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.69790 -0.57939 0.03569 0.51198 3.16194
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 331.9 18.22
## Residual 485.3 22.03
## Number of obs: 3319, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.6050 0.6919 999.3441 64.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.3,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.61 | 0.69 | 43.25 – 45.96 | 64.47 | <0.001 |
| Random Effects | |||||
| σ2 | 485.35 | ||||
| τ00 id | 331.87 | ||||
| ICC | 0.41 | ||||
| N id | 1004 | ||||
| Observations | 3319 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.406 | ||||
anova(modC.3, modA.3)## refitting model(s) with ML (instead of REML)Benefit
modA.4 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.4 <- lmer(Ben ~ 1 + (1|id), data = L)
summary(modA.4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27808
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3345 -0.4773 0.0739 0.5582 2.6462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 238.7 15.45
## Residual 463.2 21.52
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.3899 0.6270 999.7844 96.314 < 2e-16 ***
## C1 -6.6494 0.8415 2369.0189 -7.901 4.18e-15 ***
## C2 -7.1582 1.2462 2352.7388 -5.744 1.04e-08 ***
## C3 6.3360 1.2802 2351.7235 4.949 7.98e-07 ***
## C4 -10.4003 1.4478 2368.4324 -7.184 9.05e-13 ***
## C5 -4.9473 1.4516 2347.8427 -3.408 0.000665 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.007
## C2 -0.006 -0.014
## C3 0.016 -0.033 0.002
## C4 0.006 -0.004 0.005 -0.017
## C5 0.016 0.023 -0.032 0.010 -0.008tab_model(modA.4,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.39 | 0.63 | 59.16 – 61.62 | 96.31 | <0.001 |
| C1 | -6.65 | 0.84 | -8.30 – -5.00 | -7.90 | <0.001 |
| C2 | -7.16 | 1.25 | -9.60 – -4.71 | -5.74 | <0.001 |
| C3 | 6.34 | 1.28 | 3.83 – 8.85 | 4.95 | <0.001 |
| C4 | -10.40 | 1.45 | -13.24 – -7.56 | -7.18 | <0.001 |
| C5 | -4.95 | 1.45 | -7.79 – -2.10 | -3.41 | 0.001 |
| Random Effects | |||||
| σ2 | 463.15 | ||||
| τ00 id | 238.71 | ||||
| ICC | 0.34 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.044 / 0.369 | ||||
summary(modC.4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27994.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86155 -0.44300 0.05094 0.59017 2.47464
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 232.2 15.24
## Residual 500.6 22.37
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.3521 0.6316 999.7847 95.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.4,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.35 | 0.63 | 59.11 – 61.59 | 95.55 | <0.001 |
| Random Effects | |||||
| σ2 | 500.63 | ||||
| τ00 id | 232.23 | ||||
| ICC | 0.32 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.317 | ||||
anova(modC.4, modA.4)## refitting model(s) with ML (instead of REML)Difference Benefit/Risk Scores
modA.5 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.5 <- lmer(BRDiff ~ 1 + (1|id), data = L)
summary(modA.5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30825.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3908 -0.5246 -0.0378 0.5578 2.8665
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 367.7 19.18
## Residual 1452.6 38.11
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.6984 0.9238 1000.3810 16.993 < 2e-16 ***
## C1 -13.5195 1.4614 2515.2585 -9.251 < 2e-16 ***
## C2 -15.5764 2.1646 2491.4027 -7.196 8.17e-13 ***
## C3 13.8468 2.2252 2488.0575 6.223 5.72e-10 ***
## C4 -19.4601 2.5134 2511.0443 -7.742 1.40e-14 ***
## C5 -10.2906 2.5228 2484.1148 -4.079 4.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.009
## C2 -0.008 -0.013
## C3 0.019 -0.030 0.001
## C4 0.007 -0.006 0.003 -0.014
## C5 0.019 0.023 -0.029 0.007 -0.005tab_model(modA.5,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.70 | 0.92 | 13.89 – 17.51 | 16.99 | <0.001 |
| C1 | -13.52 | 1.46 | -16.38 – -10.65 | -9.25 | <0.001 |
| C2 | -15.58 | 2.16 | -19.82 – -11.33 | -7.20 | <0.001 |
| C3 | 13.85 | 2.23 | 9.48 – 18.21 | 6.22 | <0.001 |
| C4 | -19.46 | 2.51 | -24.39 – -14.53 | -7.74 | <0.001 |
| C5 | -10.29 | 2.52 | -15.24 – -5.34 | -4.08 | <0.001 |
| Random Effects | |||||
| σ2 | 1452.65 | ||||
| τ00 id | 367.70 | ||||
| ICC | 0.20 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.068 / 0.257 | ||||
summary(modC.5)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 31083.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0684 -0.4196 -0.1273 0.5750 2.6013
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 357 18.89
## Residual 1599 39.99
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.5946 0.9439 1001.4498 16.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.5,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.59 | 0.94 | 13.74 – 17.45 | 16.52 | <0.001 |
| Random Effects | |||||
| σ2 | 1599.27 | ||||
| τ00 id | 356.98 | ||||
| ICC | 0.18 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.182 | ||||
anova(modC.5, modA.5)## refitting model(s) with ML (instead of REML)Combined Familiarity/Understanding Mean Scores
modA.6 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.6 <- lmer(FR ~ 1 + (1|id), data = L)
summary(modA.6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27138.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8457 -0.5009 0.0654 0.5426 3.3148
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.0 16.43
## Residual 351.5 18.75
## Number of obs: 2986, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.369e+01 6.271e-01 9.978e+02 101.549 < 2e-16 ***
## C1 -4.554e+00 7.688e-01 2.474e+03 -5.923 3.59e-09 ***
## C2 1.780e+01 1.101e+00 2.316e+03 16.174 < 2e-16 ***
## C3 1.941e-01 1.171e+00 2.461e+03 0.166 0.868
## C4 5.538e-18 1.188e+00 1.987e+03 0.000 1.000
## C5 -6.948e+00 1.278e+00 2.306e+03 -5.437 5.97e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.041
## C2 -0.005 -0.011
## C3 -0.020 0.041 0.016
## C4 0.000 0.000 0.000 0.000
## C5 0.013 0.034 -0.026 0.018 0.000tab_model(modA.6,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.69 | 0.63 | 62.46 – 64.91 | 101.55 | <0.001 |
| C1 | -4.55 | 0.77 | -6.06 – -3.05 | -5.92 | <0.001 |
| C2 | 17.80 | 1.10 | 15.65 – 19.96 | 16.17 | <0.001 |
| C3 | 0.19 | 1.17 | -2.10 – 2.49 | 0.17 | 0.868 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -6.95 | 1.28 | -9.45 – -4.44 | -5.44 | <0.001 |
| Random Effects | |||||
| σ2 | 351.49 | ||||
| τ00 id | 269.97 | ||||
| ICC | 0.43 | ||||
| N id | 1004 | ||||
| Observations | 2986 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.472 | ||||
summary(modC.6)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27445.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5675 -0.5080 0.0726 0.5561 2.7932
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 255.9 16.00
## Residual 404.8 20.12
## Number of obs: 2986, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.6394 0.6298 996.7018 101 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.6,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.64 | 0.63 | 62.40 – 64.87 | 101.05 | <0.001 |
| Random Effects | |||||
| σ2 | 404.84 | ||||
| τ00 id | 255.85 | ||||
| ICC | 0.39 | ||||
| N id | 1004 | ||||
| Observations | 2986 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.387 | ||||
anova(modC.6, modA.6)## refitting model(s) with ML (instead of REML)Familiarity
modA.666 <- lmer(Familiarity ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.666 <- lmer(Familiarity ~ 1 + (1|id), data = L)
summary(modA.666)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Familiarity ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28753.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7016 -0.6524 0.1022 0.6685 2.4891
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 244.3 15.63
## Residual 680.0 26.08
## Number of obs: 2996, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.7996 0.6862 1000.6565 84.226 < 2e-16 ***
## C1 -16.6408 1.0094 2445.6747 -16.485 < 2e-16 ***
## C2 -5.0384 1.4935 2428.5250 -3.374 0.000754 ***
## C3 -5.6872 1.5364 2422.1864 -3.702 0.000219 ***
## C4 -10.5014 1.7428 2445.4320 -6.025 1.94e-09 ***
## C5 -7.4767 1.7369 2415.4238 -4.305 1.74e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.011
## C2 -0.006 -0.013
## C3 0.015 -0.029 0.001
## C4 0.007 -0.006 0.004 -0.017
## C5 0.018 0.024 -0.032 0.008 -0.007tab_model(modA.666,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.80 | 0.69 | 56.45 – 59.15 | 84.23 | <0.001 |
| C1 | -16.64 | 1.01 | -18.62 – -14.66 | -16.49 | <0.001 |
| C2 | -5.04 | 1.49 | -7.97 – -2.11 | -3.37 | 0.001 |
| C3 | -5.69 | 1.54 | -8.70 – -2.67 | -3.70 | <0.001 |
| C4 | -10.50 | 1.74 | -13.92 – -7.08 | -6.03 | <0.001 |
| C5 | -7.48 | 1.74 | -10.88 – -4.07 | -4.30 | <0.001 |
| Random Effects | |||||
| σ2 | 680.02 | ||||
| τ00 id | 244.32 | ||||
| ICC | 0.26 | ||||
| N id | 1004 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.089 / 0.330 | ||||
summary(modC.666)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Familiarity ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 29099
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1988 -0.6852 0.1375 0.6931 1.8970
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 220.1 14.84
## Residual 790.7 28.12
## Number of obs: 2996, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.7845 0.6952 1000.7999 83.11 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.666,
show.stat = T, show.se = T)| Familiarity | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.78 | 0.70 | 56.42 – 59.15 | 83.11 | <0.001 |
| Random Effects | |||||
| σ2 | 790.66 | ||||
| τ00 id | 220.08 | ||||
| ICC | 0.22 | ||||
| N id | 1004 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.218 | ||||
anova(modC.666, modA.666)## refitting model(s) with ML (instead of REML)Understanding
modA.667 <- lmer(Understanding ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
modC.667 <- lmer(Understanding ~ 1 + (1|id), data = L)
summary(modA.667)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Understanding ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28235.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1222 -0.4857 0.1551 0.5938 2.5603
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 293.1 17.12
## Residual 519.5 22.79
## Number of obs: 2999, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 64.9813 0.6824 1000.4267 95.226 < 2e-16 ***
## C1 -10.8116 0.8934 2354.1796 -12.101 < 2e-16 ***
## C2 -2.1528 1.3216 2337.6734 -1.629 0.10347
## C3 -1.8419 1.3585 2330.2043 -1.356 0.17528
## C4 -4.0505 1.5397 2355.2868 -2.631 0.00858 **
## C5 -6.0354 1.5376 2329.6665 -3.925 8.92e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.009
## C2 -0.006 -0.014
## C3 0.015 -0.033 0.002
## C4 0.005 -0.004 0.005 -0.016
## C5 0.017 0.025 -0.033 0.011 -0.008tab_model(modA.667,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 64.98 | 0.68 | 63.64 – 66.32 | 95.23 | <0.001 |
| C1 | -10.81 | 0.89 | -12.56 – -9.06 | -12.10 | <0.001 |
| C2 | -2.15 | 1.32 | -4.74 – 0.44 | -1.63 | 0.103 |
| C3 | -1.84 | 1.36 | -4.51 – 0.82 | -1.36 | 0.175 |
| C4 | -4.05 | 1.54 | -7.07 – -1.03 | -2.63 | 0.009 |
| C5 | -6.04 | 1.54 | -9.05 – -3.02 | -3.93 | <0.001 |
| Random Effects | |||||
| σ2 | 519.49 | ||||
| τ00 id | 293.05 | ||||
| ICC | 0.36 | ||||
| N id | 1004 | ||||
| Observations | 2999 | ||||
| Marginal R2 / Conditional R2 | 0.041 / 0.387 | ||||
summary(modC.667)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Understanding ~ 1 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28414.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7863 -0.4427 0.1491 0.5878 2.2837
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 283.5 16.84
## Residual 561.0 23.69
## Number of obs: 2999, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 64.9741 0.6853 1000.4997 94.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(modC.667,
show.stat = T, show.se = T)| Understanding | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 64.97 | 0.69 | 63.63 – 66.32 | 94.81 | <0.001 |
| Random Effects | |||||
| σ2 | 560.98 | ||||
| τ00 id | 283.48 | ||||
| ICC | 0.34 | ||||
| N id | 1004 | ||||
| Observations | 2999 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.336 | ||||
anova(modC.667, modA.667)## refitting model(s) with ML (instead of REML)Mixed Models
Support
Q.1: (SIMPLE MODEL) How do burger contrasts predict support?
#Do burger contrasts predict support?
modA.71 <- lmer(Behav ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.71)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28169.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5456 -0.4179 0.0521 0.4301 3.3434
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 449.2 21.19
## Residual 431.8 20.78
## Number of obs: 3008, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.4847 0.7747 1001.8181 72.908 < 2e-16 ***
## C1 -6.4914 0.8047 2201.0504 -8.066 1.18e-15 ***
## C2 -6.1986 1.2296 2292.4556 -5.041 4.99e-07 ***
## C3 5.1803 1.2795 2341.2427 4.049 5.32e-05 ***
## C4 0.2924 1.4180 2293.3067 0.206 0.836669
## C5 -5.1045 1.4401 2316.5191 -3.545 0.000401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.003
## C2 -0.020 0.006
## C3 -0.007 -0.057 0.027
## C4 -0.017 -0.063 0.051 0.000
## C5 -0.014 0.067 -0.029 0.064 0.074tab_model(modA.71,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.48 | 0.77 | 54.97 – 58.00 | 72.91 | <0.001 |
| C1 | -6.49 | 0.80 | -8.07 – -4.91 | -8.07 | <0.001 |
| C2 | -6.20 | 1.23 | -8.61 – -3.79 | -5.04 | <0.001 |
| C3 | 5.18 | 1.28 | 2.67 – 7.69 | 4.05 | <0.001 |
| C4 | 0.29 | 1.42 | -2.49 – 3.07 | 0.21 | 0.837 |
| C5 | -5.10 | 1.44 | -7.93 – -2.28 | -3.54 | <0.001 |
| Random Effects | |||||
| σ2 | 431.78 | ||||
| τ00 id | 449.18 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 3008 | ||||
| Marginal R2 / Conditional R2 | 0.022 / 0.521 | ||||
confint(modA.71)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 19.962311 22.470893
## .sigma 20.130427 21.412665
## (Intercept) 54.966128 58.003963
## C1 -8.067509 -4.915172
## C2 -8.606936 -3.790127
## C3 2.674390 7.686242
## C4 -2.484845 3.069674
## C5 -7.924868 -2.283917Q.2: Does naturalness predict support, over and above burger contrasts?
#Does naturalness predict support?
modA.7 <- lmer(Behav ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.7)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25168.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8393 -0.4549 0.0449 0.4905 3.1256
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 401.6 20.04
## Residual 413.7 20.34
## Number of obs: 2697, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.86608 0.76821 1093.90157 71.421 < 2e-16 ***
## Naturalness.c 0.38839 0.02155 2147.25232 18.023 < 2e-16 ***
## C1 -3.20872 0.91118 1942.24470 -3.521 0.000439 ***
## C2 0.67094 1.26579 1984.39275 0.530 0.596131
## C3 7.17041 1.41061 2064.02901 5.083 4.05e-07 ***
## C4 5.42802 1.91507 2091.24654 2.834 0.004636 **
## C5 -12.72932 1.47319 1998.79491 -8.641 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4
## Naturlnss.c -0.119
## C1 -0.137 0.206
## C2 -0.058 0.298 0.076
## C3 -0.126 0.085 0.174 0.056
## C4 -0.195 0.157 0.294 0.094 0.310
## C5 0.011 -0.284 0.018 -0.114 0.048 0.034tab_model(modA.7,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.87 | 0.77 | 53.36 – 56.37 | 71.42 | <0.001 |
| Naturalness c | 0.39 | 0.02 | 0.35 – 0.43 | 18.02 | <0.001 |
| C1 | -3.21 | 0.91 | -5.00 – -1.42 | -3.52 | <0.001 |
| C2 | 0.67 | 1.27 | -1.81 – 3.15 | 0.53 | 0.596 |
| C3 | 7.17 | 1.41 | 4.40 – 9.94 | 5.08 | <0.001 |
| C4 | 5.43 | 1.92 | 1.67 – 9.18 | 2.83 | 0.005 |
| C5 | -12.73 | 1.47 | -15.62 – -9.84 | -8.64 | <0.001 |
| Random Effects | |||||
| σ2 | 413.74 | ||||
| τ00 id | 401.64 | ||||
| ICC | 0.49 | ||||
| N id | 1003 | ||||
| Observations | 2697 | ||||
| Marginal R2 / Conditional R2 | 0.097 / 0.542 | ||||
confint(modA.7)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 18.8126988 21.3089627
## .sigma 19.6435140 21.0107228
## (Intercept) 53.3606674 56.3721977
## Naturalness.c 0.3461825 0.4305892
## C1 -4.9927247 -1.4246943
## C2 -1.8076880 3.1496554
## C3 4.4085408 9.9322837
## C4 1.6785146 9.1775222
## C5 -15.6137199 -9.8449348Q.3: Does risk predict support, over and above burger contrasts?
#Does naturalness predict support?
modA.7555 <- lmer(Behav ~ Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.7555)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Risk.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25049.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7089 -0.4164 0.0447 0.4703 3.2790
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 509.0 22.56
## Residual 356.3 18.88
## Number of obs: 2696, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.54358 0.81563 1012.03451 69.325 < 2e-16 ***
## Risk.c -0.40669 0.01822 2514.57008 -22.320 < 2e-16 ***
## C1 -3.66421 0.84596 1848.96017 -4.331 1.56e-05 ***
## C2 -2.76227 1.14257 1878.28771 -2.418 0.0157 *
## C3 2.26946 1.32976 1942.18453 1.707 0.0880 .
## C4 3.63849 1.79116 1958.48891 2.031 0.0424 *
## C5 -2.77778 1.32996 1891.55057 -2.089 0.0369 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c C1 C2 C3 C4
## Risk.c 0.001
## C1 -0.102 -0.158
## C2 -0.024 -0.132 0.039
## C3 -0.104 0.095 0.144 0.024
## C4 -0.160 -0.093 0.282 0.068 0.295
## C5 -0.025 -0.081 0.100 -0.022 0.077 0.099tab_model(modA.7555,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.54 | 0.82 | 54.94 – 58.14 | 69.32 | <0.001 |
| Risk c | -0.41 | 0.02 | -0.44 – -0.37 | -22.32 | <0.001 |
| C1 | -3.66 | 0.85 | -5.32 – -2.01 | -4.33 | <0.001 |
| C2 | -2.76 | 1.14 | -5.00 – -0.52 | -2.42 | 0.016 |
| C3 | 2.27 | 1.33 | -0.34 – 4.88 | 1.71 | 0.088 |
| C4 | 3.64 | 1.79 | 0.13 – 7.15 | 2.03 | 0.042 |
| C5 | -2.78 | 1.33 | -5.39 – -0.17 | -2.09 | 0.037 |
| Random Effects | |||||
| σ2 | 356.31 | ||||
| τ00 id | 508.95 | ||||
| ICC | 0.59 | ||||
| N id | 1003 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.147 / 0.649 | ||||
confint(modA.7555)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 21.2801439 23.8870952
## .sigma 18.2202550 19.5074689
## (Intercept) 54.9447831 58.1425639
## Risk.c -0.4437423 -0.3697838
## C1 -5.3221799 -2.0066169
## C2 -5.0000586 -0.5247321
## C3 -0.3345562 4.8736271
## C4 0.1308248 7.1460134
## C5 -5.3824314 -0.1733656Q.4: Does benefit predict support, over and above burger contrasts?
#Does naturalness predict support?
modA.7557 <- lmer(Behav ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.7557)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 23125.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6328 -0.3589 0.0909 0.4318 4.9998
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 73.96 8.60
## Residual 252.54 15.89
## Number of obs: 2695, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.660e+01 4.257e-01 1.064e+03 132.970 < 2e-16 ***
## Benefit.c 8.508e-01 1.344e-02 2.583e+03 63.297 < 2e-16 ***
## C1 -1.130e+00 6.798e-01 2.146e+03 -1.662 0.0967 .
## C2 -5.092e-03 9.138e-01 2.208e+03 -0.006 0.9956
## C3 -7.797e-01 1.050e+00 2.331e+03 -0.743 0.4576
## C4 7.780e+00 1.406e+00 2.394e+03 5.532 3.51e-08 ***
## C5 -9.878e-01 1.062e+00 2.213e+03 -0.930 0.3524
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c C1 C2 C3 C4
## Benefit.c 0.004
## C1 -0.154 0.126
## C2 -0.020 0.102 0.018
## C3 -0.145 -0.086 0.150 0.009
## C4 -0.228 0.090 0.279 0.037 0.276
## C5 -0.010 0.060 0.063 -0.023 0.036 0.052tab_model(modA.7557,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.60 | 0.43 | 55.76 – 57.43 | 132.97 | <0.001 |
| Benefit c | 0.85 | 0.01 | 0.82 – 0.88 | 63.30 | <0.001 |
| C1 | -1.13 | 0.68 | -2.46 – 0.20 | -1.66 | 0.097 |
| C2 | -0.01 | 0.91 | -1.80 – 1.79 | -0.01 | 0.996 |
| C3 | -0.78 | 1.05 | -2.84 – 1.28 | -0.74 | 0.458 |
| C4 | 7.78 | 1.41 | 5.02 – 10.54 | 5.53 | <0.001 |
| C5 | -0.99 | 1.06 | -3.07 – 1.09 | -0.93 | 0.352 |
| Random Effects | |||||
| σ2 | 252.54 | ||||
| τ00 id | 73.96 | ||||
| ICC | 0.23 | ||||
| N id | 1002 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.620 / 0.706 | ||||
confint(modA.7557)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 7.6325743 9.5348447
## .sigma 15.3470273 16.4209925
## (Intercept) 55.7648759 57.4331273
## Benefit.c 0.8233087 0.8781252
## C1 -2.4605722 0.2018481
## C2 -1.7948907 1.7854811
## C3 -2.8372021 1.2768814
## C4 5.0262791 10.5350124
## C5 -3.0674964 1.0919172Q.5: Does familiarity/understanding predict support, over and above burger contrasts?
#Does naturalness predict support?
modA.7559 <- lmer(Behav ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.7559)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 22169.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6217 -0.4179 0.0794 0.4888 3.3278
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 230.9 15.19
## Residual 437.2 20.91
## Number of obs: 2397, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.8184 0.6765 1090.1110 83.993 < 2e-16 ***
## FR.c 0.5057 0.0214 2373.4846 23.633 < 2e-16 ***
## C1 -4.1952 1.0331 2015.2496 -4.061 5.08e-05 ***
## C2 -13.8671 1.8624 2184.7225 -7.446 1.38e-13 ***
## C3 4.6123 1.4419 2020.5776 3.199 0.0014 **
## C4 -0.5034 1.7637 1612.1718 -0.285 0.7754
## C5 -1.6884 1.4378 1827.7286 -1.174 0.2404
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3 C4
## FR.c 0.004
## C1 -0.008 0.089
## C2 0.183 -0.203 0.310
## C3 -0.140 -0.005 0.170 0.027
## C4 -0.186 -0.001 0.230 0.038 0.283
## C5 -0.015 0.096 0.064 -0.041 0.053 0.060tab_model(modA.7559,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.82 | 0.68 | 55.49 – 58.14 | 83.99 | <0.001 |
| FR c | 0.51 | 0.02 | 0.46 – 0.55 | 23.63 | <0.001 |
| C1 | -4.20 | 1.03 | -6.22 – -2.17 | -4.06 | <0.001 |
| C2 | -13.87 | 1.86 | -17.52 – -10.22 | -7.45 | <0.001 |
| C3 | 4.61 | 1.44 | 1.78 – 7.44 | 3.20 | 0.001 |
| C4 | -0.50 | 1.76 | -3.96 – 2.96 | -0.29 | 0.775 |
| C5 | -1.69 | 1.44 | -4.51 – 1.13 | -1.17 | 0.240 |
| Random Effects | |||||
| σ2 | 437.22 | ||||
| τ00 id | 230.89 | ||||
| ICC | 0.35 | ||||
| N id | 1002 | ||||
| Observations | 2397 | ||||
| Marginal R2 / Conditional R2 | 0.208 / 0.482 | ||||
confint(modA.7559)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 13.8675779 16.5117652
## .sigma 20.1167844 21.6798986
## (Intercept) 55.4935214 58.1446618
## FR.c 0.4625459 0.5486486
## C1 -6.2191908 -2.1707587
## C2 -17.5144053 -10.2195959
## C3 1.7881005 7.4370789
## C4 -3.9564528 2.9504142
## C5 -4.5034552 1.1271255Q.6: Does perceived naturalness predict support, over and above risk perception and burger contrasts?
#Does naturalness predict support, over and above risk perception?
modA.9 <- lmer(Behav ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 24899.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2242 -0.4371 0.0428 0.4864 3.2290
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 481.4 21.94
## Residual 337.8 18.38
## Number of obs: 2695, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 55.43513 0.79854 1034.65647 69.421 < 2e-16 ***
## Naturalness.c 0.25863 0.02116 2022.49137 12.221 < 2e-16 ***
## Risk.c -0.32482 0.01896 2524.90900 -17.131 < 2e-16 ***
## C1 -2.02516 0.83504 1847.66681 -2.425 0.015395 *
## C2 1.02750 1.15637 1874.07389 0.889 0.374356
## C3 4.33149 1.30544 1947.59152 3.318 0.000923 ***
## C4 6.51101 1.75963 1956.51109 3.700 0.000221 ***
## C5 -8.26301 1.37048 1904.05729 -6.029 1.97e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4
## Naturlnss.c -0.112
## Risk.c -0.038 0.353
## C1 -0.118 0.163 -0.088
## C2 -0.053 0.272 -0.022 0.082
## C3 -0.117 0.127 0.133 0.161 0.057
## C4 -0.173 0.134 -0.039 0.298 0.101 0.307
## C5 0.013 -0.328 -0.188 0.039 -0.109 0.031 0.049tab_model(modA.9,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 55.44 | 0.80 | 53.87 – 57.00 | 69.42 | <0.001 |
| Naturalness c | 0.26 | 0.02 | 0.22 – 0.30 | 12.22 | <0.001 |
| Risk c | -0.32 | 0.02 | -0.36 – -0.29 | -17.13 | <0.001 |
| C1 | -2.03 | 0.84 | -3.66 – -0.39 | -2.43 | 0.015 |
| C2 | 1.03 | 1.16 | -1.24 – 3.29 | 0.89 | 0.374 |
| C3 | 4.33 | 1.31 | 1.77 – 6.89 | 3.32 | 0.001 |
| C4 | 6.51 | 1.76 | 3.06 – 9.96 | 3.70 | <0.001 |
| C5 | -8.26 | 1.37 | -10.95 – -5.58 | -6.03 | <0.001 |
| Random Effects | |||||
| σ2 | 337.79 | ||||
| τ00 id | 481.36 | ||||
| ICC | 0.59 | ||||
| N id | 1003 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.169 / 0.657 | ||||
confint(modA.9)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 20.6947144 23.2322603
## .sigma 17.7354126 18.9889071
## (Intercept) 53.8707629 57.0014744
## Naturalness.c 0.2170151 0.3002175
## Risk.c -0.3633841 -0.2864762
## C1 -3.6601487 -0.3900594
## C2 -1.2360134 3.2909847
## C3 1.7748935 6.8876613
## C4 3.0666990 9.9554565
## C5 -10.9488109 -5.5766322Q.7: Does perceived benefit predict behavioral intent, over and above naturalness and burger contrasts?
#Does perceived benefit predict behavioral intent, over and above naturalness and burger contrasts?
modA.10 <- lmer(Behav ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 23070.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7846 -0.3684 0.0898 0.4441 5.2160
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 75.2 8.672
## Residual 246.9 15.714
## Number of obs: 2693, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.11609 0.43026 1090.26423 130.423 < 2e-16 ***
## Naturalness.c 0.11150 0.01644 2514.42808 6.783 1.46e-11 ***
## Benefit.c 0.82152 0.01398 2529.24032 58.779 < 2e-16 ***
## C1 -0.35932 0.68314 2130.20880 -0.526 0.59895
## C2 1.75661 0.94264 2183.56605 1.863 0.06253 .
## C3 0.04077 1.04620 2323.71081 0.039 0.96892
## C4 9.07597 1.40635 2372.77376 6.454 1.32e-10 ***
## C5 -3.30097 1.10666 2230.63257 -2.983 0.00289 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c C1 C2 C3 C4
## Naturlnss.c -0.163
## Benefit.c 0.052 -0.295
## C1 -0.175 0.170 0.069
## C2 -0.064 0.278 0.013 0.065
## C3 -0.160 0.113 -0.115 0.166 0.040
## C4 -0.244 0.136 0.045 0.295 0.074 0.287
## C5 0.040 -0.310 0.147 0.006 -0.107 0.000 0.008tab_model(modA.10,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.12 | 0.43 | 55.27 – 56.96 | 130.42 | <0.001 |
| Naturalness c | 0.11 | 0.02 | 0.08 – 0.14 | 6.78 | <0.001 |
| Benefit c | 0.82 | 0.01 | 0.79 – 0.85 | 58.78 | <0.001 |
| C1 | -0.36 | 0.68 | -1.70 – 0.98 | -0.53 | 0.599 |
| C2 | 1.76 | 0.94 | -0.09 – 3.60 | 1.86 | 0.063 |
| C3 | 0.04 | 1.05 | -2.01 – 2.09 | 0.04 | 0.969 |
| C4 | 9.08 | 1.41 | 6.32 – 11.83 | 6.45 | <0.001 |
| C5 | -3.30 | 1.11 | -5.47 – -1.13 | -2.98 | 0.003 |
| Random Effects | |||||
| σ2 | 246.93 | ||||
| τ00 id | 75.20 | ||||
| ICC | 0.23 | ||||
| N id | 1002 | ||||
| Observations | 2693 | ||||
| Marginal R2 / Conditional R2 | 0.623 / 0.711 | ||||
Q.8: Does perceived benefit predict support, over and above perceived risk, naturalness, and burger contrasts?
#Does perceived benefit predict support, over and above perceived risk, naturalness, and burger contrasts?
modA.10 <- lmer(Behav ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 +
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 23048.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8391 -0.3659 0.0895 0.4452 4.9929
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 79.89 8.938
## Residual 242.68 15.578
## Number of obs: 2691, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.20126 0.43445 1056.32411 129.362 < 2e-16 ***
## Naturalness.c 0.09444 0.01706 2429.95086 5.534 3.46e-08 ***
## Risk.c -0.05006 0.01401 2450.02011 -3.573 0.000359 ***
## Benefit.c 0.80715 0.01436 2383.79904 56.202 < 2e-16 ***
## C1 -0.26934 0.68067 2093.89990 -0.396 0.692367
## C2 1.77549 0.93697 2142.81124 1.895 0.058238 .
## C3 -0.32842 1.04699 2285.11302 -0.314 0.753795
## C4 9.10408 1.40188 2333.04733 6.494 1.02e-10 ***
## C5 -2.73598 1.11153 2188.32855 -2.461 0.013914 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c Bnft.c C1 C2 C3 C4
## Naturlnss.c -0.167
## Risk.c -0.045 0.283
## Benefit.c 0.040 -0.214 0.226
## C1 -0.171 0.146 -0.057 0.055
## C2 -0.063 0.263 -0.013 0.010 0.067
## C3 -0.162 0.136 0.097 -0.092 0.159 0.040
## C4 -0.241 0.122 -0.029 0.037 0.298 0.076 0.285
## C5 0.045 -0.335 -0.143 0.110 0.016 -0.104 -0.012 0.014tab_model(modA.10,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.20 | 0.43 | 55.35 – 57.05 | 129.36 | <0.001 |
| Naturalness c | 0.09 | 0.02 | 0.06 – 0.13 | 5.53 | <0.001 |
| Risk c | -0.05 | 0.01 | -0.08 – -0.02 | -3.57 | <0.001 |
| Benefit c | 0.81 | 0.01 | 0.78 – 0.84 | 56.20 | <0.001 |
| C1 | -0.27 | 0.68 | -1.60 – 1.07 | -0.40 | 0.692 |
| C2 | 1.78 | 0.94 | -0.06 – 3.61 | 1.89 | 0.058 |
| C3 | -0.33 | 1.05 | -2.38 – 1.72 | -0.31 | 0.754 |
| C4 | 9.10 | 1.40 | 6.36 – 11.85 | 6.49 | <0.001 |
| C5 | -2.74 | 1.11 | -4.92 – -0.56 | -2.46 | 0.014 |
| Random Effects | |||||
| σ2 | 242.68 | ||||
| τ00 id | 79.89 | ||||
| ICC | 0.25 | ||||
| N id | 1002 | ||||
| Observations | 2691 | ||||
| Marginal R2 / Conditional R2 | 0.620 / 0.714 | ||||
confint(modA.10)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 7.96639981 9.87924404
## .sigma 15.02983125 16.10015419
## (Intercept) 55.34990497 57.05201768
## Naturalness.c 0.06103335 0.12784468
## Risk.c -0.07812545 -0.02216429
## Benefit.c 0.77678432 0.83715971
## C1 -1.60166267 1.06306171
## C2 -0.05880171 3.61048875
## C3 -2.37960399 1.72196700
## C4 6.36006908 11.84832783
## C5 -4.91198833 -0.56030197Q.9: Does perceived familiarity/understanding predict support, over and above perceived benefit, risk, naturalness, and burger contrasts?
#How does perceived benefit and naturalness predict behavioral intent?
modA.115 <- lmer(Behav ~ Naturalness.c + Risk.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.115)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Naturalness.c + Risk.c + Benefit.c + FR.c + C1 + C2 +
## C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 18524.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8284 -0.4074 0.0962 0.4808 4.6953
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 37.96 6.161
## Residual 205.86 14.348
## Number of obs: 2226, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.73467 0.48133 1724.49406 117.870 < 2e-16 ***
## Naturalness.c 0.09145 0.01681 2127.05776 5.439 5.97e-08 ***
## Risk.c -0.06404 0.01301 1935.82128 -4.920 9.38e-07 ***
## Benefit.c 0.79644 0.01484 2084.54177 53.669 < 2e-16 ***
## FR.c 0.18381 0.01504 2180.78555 12.222 < 2e-16 ***
## C1 -0.33178 0.90432 2044.61149 -0.367 0.71374
## C2 -1.08858 1.24579 2146.28489 -0.874 0.38232
## C3 -1.69893 1.30566 2104.13184 -1.301 0.19333
## C4 6.48938 2.14025 1902.17336 3.032 0.00246 **
## C5 -1.51689 1.02050 1859.29135 -1.486 0.13734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c Bnft.c FR.c C1 C2 C3 C4
## Naturlnss.c -0.137
## Risk.c -0.038 0.255
## Benefit.c 0.021 -0.154 0.198
## FR.c -0.001 -0.064 -0.029 -0.413
## C1 -0.413 0.106 -0.024 0.029 0.040
## C2 0.145 0.226 0.012 0.107 -0.252 0.234
## C3 -0.511 0.091 0.083 -0.073 0.044 0.533 0.009
## C4 -0.605 0.073 -0.008 0.040 -0.014 0.650 0.032 0.672
## C5 0.042 -0.361 -0.142 0.067 0.087 0.006 -0.108 -0.011 0.003tab_model(modA.115,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.73 | 0.48 | 55.79 – 57.68 | 117.87 | <0.001 |
| Naturalness c | 0.09 | 0.02 | 0.06 – 0.12 | 5.44 | <0.001 |
| Risk c | -0.06 | 0.01 | -0.09 – -0.04 | -4.92 | <0.001 |
| Benefit c | 0.80 | 0.01 | 0.77 – 0.83 | 53.67 | <0.001 |
| FR c | 0.18 | 0.02 | 0.15 – 0.21 | 12.22 | <0.001 |
| C1 | -0.33 | 0.90 | -2.11 – 1.44 | -0.37 | 0.714 |
| C2 | -1.09 | 1.25 | -3.53 – 1.35 | -0.87 | 0.382 |
| C3 | -1.70 | 1.31 | -4.26 – 0.86 | -1.30 | 0.193 |
| C4 | 6.49 | 2.14 | 2.29 – 10.69 | 3.03 | 0.002 |
| C5 | -1.52 | 1.02 | -3.52 – 0.48 | -1.49 | 0.137 |
| Random Effects | |||||
| σ2 | 205.86 | ||||
| τ00 id | 37.96 | ||||
| ICC | 0.16 | ||||
| N id | 1002 | ||||
| Observations | 2226 | ||||
| Marginal R2 / Conditional R2 | 0.721 / 0.764 | ||||
confint(modA.115)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 4.94048544 7.21599509
## .sigma 13.76295121 14.90318247
## (Intercept) 55.79290406 57.67654079
## Naturalness.c 0.05855217 0.12434102
## Risk.c -0.08996558 -0.03822462
## Benefit.c 0.76680555 0.82597144
## FR.c 0.15438087 0.21322815
## C1 -2.10110693 1.43745142
## C2 -3.52589895 1.34896348
## C3 -4.25334137 0.85552522
## C4 2.30215141 10.67716744
## C5 -3.51618886 0.48046518Naturalness
Q.1: (SIMPLE MODEL) How do burger contrasts predict naturalness perception?
#How do burger contrasts predict naturalness perception?
modA.89 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.89)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26923.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92273 -0.65184 -0.03425 0.61938 3.08384
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.535 1.239
## Residual 455.241 21.336
## Number of obs: 3006, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.3942 0.3914 1002.1123 128.753 < 2e-16 ***
## C1 -8.1457 0.7797 2793.3500 -10.447 < 2e-16 ***
## C2 -17.9940 1.1570 2769.4763 -15.552 < 2e-16 ***
## C3 -4.6214 1.1903 2759.6843 -3.883 0.000106 ***
## C4 -12.9528 1.3423 2791.1878 -9.650 < 2e-16 ***
## C5 19.8994 1.3486 2753.0711 14.755 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.012
## C2 -0.009 -0.009
## C3 0.023 -0.024 0.000
## C4 0.008 -0.008 0.000 -0.008
## C5 0.025 0.025 -0.025 0.000 0.000tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.63 – 51.16 | 128.75 | <0.001 |
| C1 | -8.15 | 0.78 | -9.67 – -6.62 | -10.45 | <0.001 |
| C2 | -17.99 | 1.16 | -20.26 – -15.73 | -15.55 | <0.001 |
| C3 | -4.62 | 1.19 | -6.96 – -2.29 | -3.88 | <0.001 |
| C4 | -12.95 | 1.34 | -15.58 – -10.32 | -9.65 | <0.001 |
| C5 | 19.90 | 1.35 | 17.26 – 22.54 | 14.76 | <0.001 |
| Random Effects | |||||
| σ2 | 455.24 | ||||
| τ00 id | 1.54 | ||||
| ICC | 0.00 | ||||
| N id | 1004 | ||||
| Observations | 3006 | ||||
| Marginal R2 / Conditional R2 | 0.185 / 0.187 | ||||
Q.2: Does understanding/familiarity (mean score) predict naturalness perception, over and above burger contrasts?
#Does understanding/familiarity (mean score) predict naturalness perception, over and above burger contrasts?
#Note: Understanding/familiarity mean score taken from two item measure.
modA.94 <- lmer(Naturalness ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.94)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21107.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1590 -0.6280 -0.0059 0.6176 3.5432
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 16.75 4.093
## Residual 413.12 20.325
## Number of obs: 2373, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.21663 0.47709 1164.24753 105.256 < 2e-16 ***
## FR.c 0.13960 0.01715 1986.51242 8.139 6.93e-16 ***
## C1 -7.01528 0.93138 2312.76251 -7.532 7.12e-14 ***
## C2 -20.15574 1.60826 2348.79473 -12.533 < 2e-16 ***
## C3 -3.89582 1.29544 2339.72154 -3.007 0.00266 **
## C4 -11.57317 1.74088 1920.81597 -6.648 3.86e-11 ***
## C5 20.69235 1.30165 2096.94317 15.897 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3 C4
## FR.c 0.001
## C1 -0.032 0.082
## C2 0.266 -0.182 0.256
## C3 -0.179 0.011 0.183 -0.006
## C4 -0.275 0.003 0.281 -0.007 0.306
## C5 0.019 0.079 0.027 -0.032 0.003 0.000tab_model(modA.94,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.22 | 0.48 | 49.28 – 51.15 | 105.26 | <0.001 |
| FR c | 0.14 | 0.02 | 0.11 – 0.17 | 8.14 | <0.001 |
| C1 | -7.02 | 0.93 | -8.84 – -5.19 | -7.53 | <0.001 |
| C2 | -20.16 | 1.61 | -23.31 – -17.00 | -12.53 | <0.001 |
| C3 | -3.90 | 1.30 | -6.44 – -1.36 | -3.01 | 0.003 |
| C4 | -11.57 | 1.74 | -14.99 – -8.16 | -6.65 | <0.001 |
| C5 | 20.69 | 1.30 | 18.14 – 23.24 | 15.90 | <0.001 |
| Random Effects | |||||
| σ2 | 413.12 | ||||
| τ00 id | 16.75 | ||||
| ICC | 0.04 | ||||
| N id | 1004 | ||||
| Observations | 2373 | ||||
| Marginal R2 / Conditional R2 | 0.168 / 0.200 | ||||
Q.3: Does familiarity predict naturalness perception, over and above familiarity and burger contrasts?
modA.9433 <- lmer(Naturalness ~ Familiarity.c + Understanding.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.9433)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Familiarity.c + Understanding.c + C1 + C2 + C3 +
## C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26595.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2970 -0.6663 0.0044 0.6304 3.5089
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 19.67 4.435
## Residual 409.00 20.224
## Number of obs: 2990, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.44895 0.39577 948.34326 127.472 < 2e-16 ***
## Familiarity.c 0.13681 0.01449 2878.79040 9.444 < 2e-16 ***
## Understanding.c 0.07162 0.01554 2682.42159 4.608 4.25e-06 ***
## C1 -5.20537 0.77976 2783.44649 -6.676 2.96e-11 ***
## C2 -16.98873 1.11389 2673.32650 -15.252 < 2e-16 ***
## C3 -3.90280 1.14672 2662.97759 -3.403 0.000675 ***
## C4 -10.97047 1.30201 2708.18348 -8.426 < 2e-16 ***
## C5 21.17874 1.29694 2668.53375 16.330 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Fmlrt. Undrs. C1 C2 C3 C4
## Familirty.c 0.000
## Undrstndng. 0.000 -0.495
## C1 -0.013 0.201 0.062
## C2 -0.007 0.049 0.004 0.008
## C3 0.019 0.060 -0.004 -0.004 0.004
## C4 0.010 0.096 -0.016 0.017 0.007 -0.005
## C5 0.024 0.041 0.029 0.043 -0.023 0.006 0.005tab_model(modA.9433,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.45 | 0.40 | 49.67 – 51.22 | 127.47 | <0.001 |
| Familiarity c | 0.14 | 0.01 | 0.11 – 0.17 | 9.44 | <0.001 |
| Understanding c | 0.07 | 0.02 | 0.04 – 0.10 | 4.61 | <0.001 |
| C1 | -5.21 | 0.78 | -6.73 – -3.68 | -6.68 | <0.001 |
| C2 | -16.99 | 1.11 | -19.17 – -14.80 | -15.25 | <0.001 |
| C3 | -3.90 | 1.15 | -6.15 – -1.65 | -3.40 | 0.001 |
| C4 | -10.97 | 1.30 | -13.52 – -8.42 | -8.43 | <0.001 |
| C5 | 21.18 | 1.30 | 18.64 – 23.72 | 16.33 | <0.001 |
| Random Effects | |||||
| σ2 | 409.00 | ||||
| τ00 id | 19.67 | ||||
| ICC | 0.05 | ||||
| N id | 1004 | ||||
| Observations | 2990 | ||||
| Marginal R2 / Conditional R2 | 0.237 / 0.272 | ||||
Risk
Q.1: (SIMPLE MODEL) How do burger contrasts predict risk perception?
#Does naturalness predict risk perception?
modA.82 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.82)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30939.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94921 -0.55876 0.01162 0.51048 2.97133
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 329.8 18.16
## Residual 455.4 21.34
## Number of obs: 3319, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.4194 0.6869 1017.0632 64.663 < 2e-16 ***
## C1 5.9265 0.8118 2645.8442 7.300 3.79e-13 ***
## C2 9.4518 1.1967 2620.9392 7.898 4.13e-15 ***
## C3 -7.7368 1.2733 2595.6429 -6.076 1.41e-09 ***
## C4 8.3421 1.4406 2612.5948 5.791 7.84e-09 ***
## C5 2.5476 1.2299 2412.7516 2.071 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.044
## C2 0.031 0.049
## C3 0.013 -0.039 0.006
## C4 0.005 -0.001 0.004 -0.016
## C5 -0.065 -0.134 0.129 0.008 -0.007tab_model(modA.82,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.69 | 43.07 – 45.77 | 64.66 | <0.001 |
| C1 | 5.93 | 0.81 | 4.33 – 7.52 | 7.30 | <0.001 |
| C2 | 9.45 | 1.20 | 7.11 – 11.80 | 7.90 | <0.001 |
| C3 | -7.74 | 1.27 | -10.23 – -5.24 | -6.08 | <0.001 |
| C4 | 8.34 | 1.44 | 5.52 – 11.17 | 5.79 | <0.001 |
| C5 | 2.55 | 1.23 | 0.14 – 4.96 | 2.07 | 0.038 |
| Random Effects | |||||
| σ2 | 455.41 | ||||
| τ00 id | 329.82 | ||||
| ICC | 0.42 | ||||
| N id | 1004 | ||||
| Observations | 3319 | ||||
| Marginal R2 / Conditional R2 | 0.035 / 0.440 | ||||
Q.2: Does naturalness predict risk perception, over and above burger contrasts?
modA.8 <- lmer(Risk ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.8)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27675.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8800 -0.5493 0.0168 0.5278 3.3519
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 247.5 15.73
## Residual 419.6 20.49
## Number of obs: 3004, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.51704 0.62662 1015.11586 74.235 < 2e-16 ***
## Naturalness.c -0.43977 0.01975 2612.03755 -22.268 < 2e-16 ***
## C1 3.20064 0.81950 2352.40531 3.906 9.66e-05 ***
## C2 0.60365 1.23617 2328.05312 0.488 0.6254
## C3 -9.67707 1.22582 2320.33685 -7.894 4.46e-15 ***
## C4 2.89680 1.40397 2337.11854 2.063 0.0392 *
## C5 14.18949 1.43527 2327.72386 9.886 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4
## Naturlnss.c -0.124
## C1 -0.033 0.197
## C2 -0.040 0.276 0.041
## C3 0.006 0.077 -0.019 0.023
## C4 -0.017 0.174 0.032 0.053 -0.002
## C5 0.049 -0.269 -0.030 -0.105 -0.010 -0.055tab_model(modA.8,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.52 | 0.63 | 45.29 – 47.75 | 74.23 | <0.001 |
| Naturalness c | -0.44 | 0.02 | -0.48 – -0.40 | -22.27 | <0.001 |
| C1 | 3.20 | 0.82 | 1.59 – 4.81 | 3.91 | <0.001 |
| C2 | 0.60 | 1.24 | -1.82 – 3.03 | 0.49 | 0.625 |
| C3 | -9.68 | 1.23 | -12.08 – -7.27 | -7.89 | <0.001 |
| C4 | 2.90 | 1.40 | 0.14 – 5.65 | 2.06 | 0.039 |
| C5 | 14.19 | 1.44 | 11.38 – 17.00 | 9.89 | <0.001 |
| Random Effects | |||||
| σ2 | 419.64 | ||||
| τ00 id | 247.53 | ||||
| ICC | 0.37 | ||||
| N id | 1004 | ||||
| Observations | 3004 | ||||
| Marginal R2 / Conditional R2 | 0.155 / 0.469 | ||||
Q.2: Does naturalness predict familiarity/understanding perception, over and above burger contrasts?
modA.8555 <- lmer(Risk ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.8555)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 22175.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3763 -0.5420 0.0031 0.4851 2.9310
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 369.4 19.22
## Residual 423.1 20.57
## Number of obs: 2372, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.57324 0.77739 1122.19606 57.337 < 2e-16 ***
## FR.c -0.17114 0.02258 2352.39451 -7.578 5.02e-14 ***
## C1 5.40083 1.06786 1832.95548 5.058 4.67e-07 ***
## C2 9.99756 1.88130 1950.29025 5.314 1.19e-07 ***
## C3 -7.57870 1.49256 1833.14045 -5.078 4.21e-07 ***
## C4 8.17638 1.86488 1537.87437 4.384 1.24e-05 ***
## C5 4.49671 1.43619 1680.42877 3.131 0.00177 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3 C4
## FR.c 0.001
## C1 -0.046 0.094
## C2 0.194 -0.219 0.249
## C3 -0.158 -0.004 0.217 -0.028
## C4 -0.208 0.005 0.286 -0.054 0.339
## C5 0.008 0.105 0.035 -0.048 0.022 0.002tab_model(modA.8555,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.57 | 0.78 | 43.05 – 46.10 | 57.34 | <0.001 |
| FR c | -0.17 | 0.02 | -0.22 – -0.13 | -7.58 | <0.001 |
| C1 | 5.40 | 1.07 | 3.31 – 7.49 | 5.06 | <0.001 |
| C2 | 10.00 | 1.88 | 6.31 – 13.69 | 5.31 | <0.001 |
| C3 | -7.58 | 1.49 | -10.51 – -4.65 | -5.08 | <0.001 |
| C4 | 8.18 | 1.86 | 4.52 – 11.83 | 4.38 | <0.001 |
| C5 | 4.50 | 1.44 | 1.68 – 7.31 | 3.13 | 0.002 |
| Random Effects | |||||
| σ2 | 423.07 | ||||
| τ00 id | 369.38 | ||||
| ICC | 0.47 | ||||
| N id | 1004 | ||||
| Observations | 2372 | ||||
| Marginal R2 / Conditional R2 | 0.053 / 0.494 | ||||
Q.3: Does benefit predict risk perception, over and above burger contrasts?
modA.88 <- lmer(Risk ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.88)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27553.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2328 -0.5024 0.0051 0.5075 3.9700
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 414.2 20.35
## Residual 353.9 18.81
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.7685 0.7293 941.2526 61.386 < 2e-16 ***
## Benefit.c -0.4238 0.0173 2805.7194 -24.494 < 2e-16 ***
## C1 3.9575 0.7607 2172.5107 5.203 2.15e-07 ***
## C2 4.9718 1.1186 2156.4956 4.445 9.24e-06 ***
## C3 -4.9123 1.1478 2151.5246 -4.280 1.95e-05 ***
## C4 3.4597 1.3046 2162.5878 2.652 0.00806 **
## C5 3.4698 1.2978 2148.3517 2.674 0.00756 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c C1 C2 C3 C4
## Benefit.c -0.001
## C1 -0.006 0.151
## C2 -0.005 0.113 0.001
## C3 0.013 -0.098 -0.051 -0.008
## C4 0.004 0.135 0.018 0.022 -0.032
## C5 0.013 0.069 0.031 -0.026 0.007 -0.001tab_model(modA.88,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.77 | 0.73 | 43.34 – 46.20 | 61.39 | <0.001 |
| Benefit c | -0.42 | 0.02 | -0.46 – -0.39 | -24.49 | <0.001 |
| C1 | 3.96 | 0.76 | 2.47 – 5.45 | 5.20 | <0.001 |
| C2 | 4.97 | 1.12 | 2.78 – 7.17 | 4.44 | <0.001 |
| C3 | -4.91 | 1.15 | -7.16 – -2.66 | -4.28 | <0.001 |
| C4 | 3.46 | 1.30 | 0.90 – 6.02 | 2.65 | 0.008 |
| C5 | 3.47 | 1.30 | 0.93 – 6.01 | 2.67 | 0.008 |
| Random Effects | |||||
| σ2 | 353.87 | ||||
| τ00 id | 414.23 | ||||
| ICC | 0.54 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.171 / 0.618 | ||||
Q.4: Does benefit predict risk perception, over and above naturalness and burger contrasts?
modA.99 <- lmer(Risk ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.99)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1 |
## id)
## Data: L
##
## REML criterion at convergence: 27296.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7009 -0.5023 0.0214 0.5125 3.7111
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 336.5 18.34
## Residual 339.2 18.42
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 45.99534 0.67469 932.84282 68.172 < 2e-16 ***
## Naturalness.c -0.31493 0.01917 2479.94864 -16.426 < 2e-16 ***
## Benefit.c -0.32160 0.01758 2911.62821 -18.290 < 2e-16 ***
## C1 2.04713 0.75164 2172.57689 2.724 0.00651 **
## C2 0.33157 1.12910 2148.67917 0.294 0.76904
## C3 -7.11117 1.12747 2165.63425 -6.307 3.43e-10 ***
## C4 0.75027 1.28460 2158.22296 0.584 0.55925
## C5 10.16559 1.32882 2185.32793 7.650 2.99e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c C1 C2 C3 C4
## Naturlnss.c -0.112
## Benefit.c 0.034 -0.313
## C1 -0.024 0.160 0.091
## C2 -0.033 0.256 0.023 0.043
## C3 0.000 0.116 -0.128 -0.031 0.021
## C4 -0.011 0.138 0.083 0.039 0.056 -0.015
## C5 0.047 -0.304 0.156 -0.019 -0.101 -0.030 -0.042tab_model(modA.99,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.00 | 0.67 | 44.67 – 47.32 | 68.17 | <0.001 |
| Naturalness c | -0.31 | 0.02 | -0.35 – -0.28 | -16.43 | <0.001 |
| Benefit c | -0.32 | 0.02 | -0.36 – -0.29 | -18.29 | <0.001 |
| C1 | 2.05 | 0.75 | 0.57 – 3.52 | 2.72 | 0.006 |
| C2 | 0.33 | 1.13 | -1.88 – 2.55 | 0.29 | 0.769 |
| C3 | -7.11 | 1.13 | -9.32 – -4.90 | -6.31 | <0.001 |
| C4 | 0.75 | 1.28 | -1.77 – 3.27 | 0.58 | 0.559 |
| C5 | 10.17 | 1.33 | 7.56 – 12.77 | 7.65 | <0.001 |
| Random Effects | |||||
| σ2 | 339.15 | ||||
| τ00 id | 336.50 | ||||
| ICC | 0.50 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.210 / 0.604 | ||||
Q.5: Does understanding/familiarity predict risk perception, over and above naturalness, benefit, and burger contrasts?
modA.100 <- lmer(Risk ~ Naturalness.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.100)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Naturalness.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 +
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21705.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2292 -0.4924 -0.0221 0.4897 3.5853
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 368.2 19.19
## Residual 335.8 18.32
## Number of obs: 2362, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 45.62206 0.75112 1072.43006 60.739 < 2e-16 ***
## Naturalness.c -0.26279 0.02267 1882.02452 -11.593 < 2e-16 ***
## Benefit.c -0.30468 0.02173 2206.53497 -14.024 < 2e-16 ***
## FR.c -0.01605 0.02247 2200.92127 -0.715 0.4750
## C1 1.95922 0.97951 1723.11665 2.000 0.0456 *
## C2 0.50448 1.77223 1833.24652 0.285 0.7759
## C3 -7.17636 1.35779 1721.04391 -5.285 1.41e-07 ***
## C4 1.59629 1.69816 1452.52185 0.940 0.3474
## C5 9.07718 1.38229 1624.84340 6.567 6.90e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c FR.c C1 C2 C3 C4
## Naturlnss.c -0.109
## Benefit.c 0.013 -0.239
## FR.c 0.013 -0.068 -0.352
## C1 -0.057 0.123 0.080 0.038
## C2 0.148 0.220 0.106 -0.274 0.283
## C3 -0.157 0.092 -0.099 0.018 0.218 -0.020
## C4 -0.204 0.107 0.116 -0.057 0.307 -0.008 0.331
## C5 0.044 -0.347 0.122 0.101 -0.007 -0.114 -0.014 -0.029tab_model(modA.100,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 45.62 | 0.75 | 44.15 – 47.09 | 60.74 | <0.001 |
| Naturalness c | -0.26 | 0.02 | -0.31 – -0.22 | -11.59 | <0.001 |
| Benefit c | -0.30 | 0.02 | -0.35 – -0.26 | -14.02 | <0.001 |
| FR c | -0.02 | 0.02 | -0.06 – 0.03 | -0.71 | 0.475 |
| C1 | 1.96 | 0.98 | 0.04 – 3.88 | 2.00 | 0.046 |
| C2 | 0.50 | 1.77 | -2.97 – 3.98 | 0.28 | 0.776 |
| C3 | -7.18 | 1.36 | -9.84 – -4.51 | -5.29 | <0.001 |
| C4 | 1.60 | 1.70 | -1.73 – 4.93 | 0.94 | 0.347 |
| C5 | 9.08 | 1.38 | 6.37 – 11.79 | 6.57 | <0.001 |
| Random Effects | |||||
| σ2 | 335.80 | ||||
| τ00 id | 368.17 | ||||
| ICC | 0.52 | ||||
| N id | 1002 | ||||
| Observations | 2362 | ||||
| Marginal R2 / Conditional R2 | 0.174 / 0.606 | ||||
Benefit
Q.1: (SIMPLE MODEL) How do burger contrasts predict perceived benefit?
#How do burger contrasts predict perceived benefit?
modA.109 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.109)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27808
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3345 -0.4773 0.0739 0.5582 2.6462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 238.7 15.45
## Residual 463.2 21.52
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.3899 0.6270 999.7844 96.314 < 2e-16 ***
## C1 -6.6494 0.8415 2369.0189 -7.901 4.18e-15 ***
## C2 -7.1582 1.2462 2352.7388 -5.744 1.04e-08 ***
## C3 6.3360 1.2802 2351.7235 4.949 7.98e-07 ***
## C4 -10.4003 1.4478 2368.4324 -7.184 9.05e-13 ***
## C5 -4.9473 1.4516 2347.8427 -3.408 0.000665 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.007
## C2 -0.006 -0.014
## C3 0.016 -0.033 0.002
## C4 0.006 -0.004 0.005 -0.017
## C5 0.016 0.023 -0.032 0.010 -0.008tab_model(modA.109,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.39 | 0.63 | 59.16 – 61.62 | 96.31 | <0.001 |
| C1 | -6.65 | 0.84 | -8.30 – -5.00 | -7.90 | <0.001 |
| C2 | -7.16 | 1.25 | -9.60 – -4.71 | -5.74 | <0.001 |
| C3 | 6.34 | 1.28 | 3.83 – 8.85 | 4.95 | <0.001 |
| C4 | -10.40 | 1.45 | -13.24 – -7.56 | -7.18 | <0.001 |
| C5 | -4.95 | 1.45 | -7.79 – -2.10 | -3.41 | 0.001 |
| Random Effects | |||||
| σ2 | 463.15 | ||||
| τ00 id | 238.71 | ||||
| ICC | 0.34 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.044 / 0.369 | ||||
Q.1: (SIMPLE MODEL) How do burger contrasts predict perceived benefit?
#How do burger contrasts predict perceived benefit?
modA.10988 <- lmer(Ben ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.10988)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21499.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3384 -0.4396 0.0716 0.5645 3.0201
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 161.5 12.71
## Residual 396.8 19.92
## Number of obs: 2363, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.85984 0.61365 1124.85299 99.177 < 2e-16 ***
## FR.c 0.44180 0.01985 2302.37057 22.258 < 2e-16 ***
## C1 -5.05552 0.99308 2032.88713 -5.091 3.90e-07 ***
## C2 -14.04730 1.73391 2138.92678 -8.102 9.02e-16 ***
## C3 5.20774 1.38513 2047.86028 3.760 0.000175 ***
## C4 -12.20444 1.77414 1630.66728 -6.879 8.56e-12 ***
## C5 -2.15260 1.35453 1816.89003 -1.589 0.112194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3 C4
## FR.c 0.001
## C1 -0.044 0.090
## C2 0.225 -0.203 0.251
## C3 -0.174 0.002 0.208 -0.021
## C4 -0.238 0.005 0.286 -0.038 0.328
## C5 0.011 0.094 0.030 -0.041 0.014 0.001tab_model(modA.10988,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.86 | 0.61 | 59.66 – 62.06 | 99.18 | <0.001 |
| FR c | 0.44 | 0.02 | 0.40 – 0.48 | 22.26 | <0.001 |
| C1 | -5.06 | 0.99 | -7.00 – -3.11 | -5.09 | <0.001 |
| C2 | -14.05 | 1.73 | -17.45 – -10.65 | -8.10 | <0.001 |
| C3 | 5.21 | 1.39 | 2.49 – 7.92 | 3.76 | <0.001 |
| C4 | -12.20 | 1.77 | -15.68 – -8.73 | -6.88 | <0.001 |
| C5 | -2.15 | 1.35 | -4.81 – 0.50 | -1.59 | 0.112 |
| Random Effects | |||||
| σ2 | 396.77 | ||||
| τ00 id | 161.47 | ||||
| ICC | 0.29 | ||||
| N id | 1002 | ||||
| Observations | 2363 | ||||
| Marginal R2 / Conditional R2 | 0.211 / 0.440 | ||||
Q.2: How does naturalness predict benefit, over and above burger contrasts?
#How does naturalness predict benefit, over and above burger contrasts?
modA.110 <- lmer(Ben ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.110)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27515.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6124 -0.4528 0.0677 0.5674 2.8880
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 251.8 15.87
## Residual 405.7 20.14
## Number of obs: 2994, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.04617 0.62699 1025.17540 94.175 < 2e-16 ***
## Naturalness.c 0.33632 0.01949 2591.06744 17.259 < 2e-16 ***
## C1 -3.90745 0.80804 2340.12551 -4.836 1.41e-06 ***
## C2 -1.45423 1.22028 2316.47510 -1.192 0.233
## C3 8.03627 1.20800 2315.62904 6.653 3.58e-11 ***
## C4 -6.22381 1.38286 2327.83940 -4.501 7.11e-06 ***
## C5 -11.63712 1.41845 2322.76876 -8.204 3.80e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4
## Naturlnss.c -0.123
## C1 -0.030 0.197
## C2 -0.039 0.276 0.041
## C3 0.005 0.079 -0.018 0.023
## C4 -0.015 0.173 0.030 0.053 -0.004
## C5 0.048 -0.270 -0.032 -0.104 -0.011 -0.054tab_model(modA.110,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.05 | 0.63 | 57.82 – 60.28 | 94.17 | <0.001 |
| Naturalness c | 0.34 | 0.02 | 0.30 – 0.37 | 17.26 | <0.001 |
| C1 | -3.91 | 0.81 | -5.49 – -2.32 | -4.84 | <0.001 |
| C2 | -1.45 | 1.22 | -3.85 – 0.94 | -1.19 | 0.233 |
| C3 | 8.04 | 1.21 | 5.67 – 10.40 | 6.65 | <0.001 |
| C4 | -6.22 | 1.38 | -8.94 – -3.51 | -4.50 | <0.001 |
| C5 | -11.64 | 1.42 | -14.42 – -8.86 | -8.20 | <0.001 |
| Random Effects | |||||
| σ2 | 405.68 | ||||
| τ00 id | 251.83 | ||||
| ICC | 0.38 | ||||
| N id | 1003 | ||||
| Observations | 2994 | ||||
| Marginal R2 / Conditional R2 | 0.114 / 0.454 | ||||
Q.3: How does risk perception predict benefit, over and above burger contrasts?
#How does risk perception predict benefit, over and above burger contrasts?
modA.113 <- lmer(Ben ~ Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.113)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Risk.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27337.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5036 -0.4563 0.0628 0.5252 2.9538
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 346.2 18.61
## Residual 341.6 18.48
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.47669 0.67817 919.00276 89.176 < 2e-16 ***
## Risk.c -0.38395 0.01618 2908.99314 -23.728 < 2e-16 ***
## C1 -4.04456 0.74453 2180.61806 -5.432 6.18e-08 ***
## C2 -4.16715 1.09654 2155.51124 -3.800 0.000149 ***
## C3 3.55610 1.12577 2159.60273 3.159 0.001606 **
## C4 -7.18956 1.27241 2159.36233 -5.650 1.81e-08 ***
## C5 -2.95722 1.27155 2151.98526 -2.326 0.020128 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c C1 C2 C3 C4
## Risk.c -0.004
## C1 -0.005 -0.147
## C2 -0.004 -0.120 0.002
## C3 0.013 0.110 -0.052 -0.011
## C4 0.005 -0.100 0.012 0.018 -0.030
## C5 0.014 -0.072 0.032 -0.025 0.005 -0.003tab_model(modA.113,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.48 | 0.68 | 59.15 – 61.81 | 89.18 | <0.001 |
| Risk c | -0.38 | 0.02 | -0.42 – -0.35 | -23.73 | <0.001 |
| C1 | -4.04 | 0.74 | -5.50 – -2.58 | -5.43 | <0.001 |
| C2 | -4.17 | 1.10 | -6.32 – -2.02 | -3.80 | <0.001 |
| C3 | 3.56 | 1.13 | 1.35 – 5.76 | 3.16 | 0.002 |
| C4 | -7.19 | 1.27 | -9.68 – -4.69 | -5.65 | <0.001 |
| C5 | -2.96 | 1.27 | -5.45 – -0.46 | -2.33 | 0.020 |
| Random Effects | |||||
| σ2 | 341.63 | ||||
| τ00 id | 346.19 | ||||
| ICC | 0.50 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.179 / 0.592 | ||||
Q.4: How does risk perception predict benefit, over and above naturalness and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger contrasts?
modA.114 <- lmer(Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27219.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0229 -0.4473 0.0688 0.5333 2.9004
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 335.5 18.32
## Residual 328.0 18.11
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.62620 0.67139 945.55596 88.810 < 2e-16 ***
## Naturalness.c 0.20812 0.01934 2361.07102 10.759 < 2e-16 ***
## Risk.c -0.31577 0.01712 2894.21391 -18.442 < 2e-16 ***
## C1 -2.83392 0.73888 2181.20842 -3.835 0.000129 ***
## C2 -1.20861 1.11100 2153.27043 -1.088 0.276778
## C3 5.14392 1.11304 2170.29747 4.621 4.03e-06 ***
## C4 -5.19257 1.26076 2160.50132 -4.119 3.96e-05 ***
## C5 -7.41943 1.31360 2172.28671 -5.648 1.83e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4
## Naturlnss.c -0.116
## Risk.c -0.047 0.375
## C1 -0.023 0.155 -0.076
## C2 -0.033 0.252 -0.013 0.042
## C3 -0.003 0.131 0.150 -0.031 0.022
## C4 -0.012 0.147 -0.037 0.035 0.054 -0.010
## C5 0.049 -0.316 -0.182 -0.019 -0.102 -0.037 -0.049tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.63 | 0.67 | 58.31 – 60.94 | 88.81 | <0.001 |
| Naturalness c | 0.21 | 0.02 | 0.17 – 0.25 | 10.76 | <0.001 |
| Risk c | -0.32 | 0.02 | -0.35 – -0.28 | -18.44 | <0.001 |
| C1 | -2.83 | 0.74 | -4.28 – -1.39 | -3.84 | <0.001 |
| C2 | -1.21 | 1.11 | -3.39 – 0.97 | -1.09 | 0.277 |
| C3 | 5.14 | 1.11 | 2.96 – 7.33 | 4.62 | <0.001 |
| C4 | -5.19 | 1.26 | -7.66 – -2.72 | -4.12 | <0.001 |
| C5 | -7.42 | 1.31 | -10.00 – -4.84 | -5.65 | <0.001 |
| Random Effects | |||||
| σ2 | 328.04 | ||||
| τ00 id | 335.47 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.197 / 0.603 | ||||
Q.5: How does risk perception predict benefit, over and above naturalness and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger contrasts?
modA.114 <- lmer(Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27219.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0229 -0.4473 0.0688 0.5333 2.9004
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 335.5 18.32
## Residual 328.0 18.11
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 59.62620 0.67139 945.55596 88.810 < 2e-16 ***
## Naturalness.c 0.20812 0.01934 2361.07102 10.759 < 2e-16 ***
## Risk.c -0.31577 0.01712 2894.21391 -18.442 < 2e-16 ***
## C1 -2.83392 0.73888 2181.20842 -3.835 0.000129 ***
## C2 -1.20861 1.11100 2153.27043 -1.088 0.276778
## C3 5.14392 1.11304 2170.29747 4.621 4.03e-06 ***
## C4 -5.19257 1.26076 2160.50132 -4.119 3.96e-05 ***
## C5 -7.41943 1.31360 2172.28671 -5.648 1.83e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3 C4
## Naturlnss.c -0.116
## Risk.c -0.047 0.375
## C1 -0.023 0.155 -0.076
## C2 -0.033 0.252 -0.013 0.042
## C3 -0.003 0.131 0.150 -0.031 0.022
## C4 -0.012 0.147 -0.037 0.035 0.054 -0.010
## C5 0.049 -0.316 -0.182 -0.019 -0.102 -0.037 -0.049tab_model(modA.114,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.63 | 0.67 | 58.31 – 60.94 | 88.81 | <0.001 |
| Naturalness c | 0.21 | 0.02 | 0.17 – 0.25 | 10.76 | <0.001 |
| Risk c | -0.32 | 0.02 | -0.35 – -0.28 | -18.44 | <0.001 |
| C1 | -2.83 | 0.74 | -4.28 – -1.39 | -3.84 | <0.001 |
| C2 | -1.21 | 1.11 | -3.39 – 0.97 | -1.09 | 0.277 |
| C3 | 5.14 | 1.11 | 2.96 – 7.33 | 4.62 | <0.001 |
| C4 | -5.19 | 1.26 | -7.66 – -2.72 | -4.12 | <0.001 |
| C5 | -7.42 | 1.31 | -10.00 – -4.84 | -5.65 | <0.001 |
| Random Effects | |||||
| σ2 | 328.04 | ||||
| τ00 id | 335.47 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.197 / 0.603 | ||||
Q.6: How does understanding/familiarity predict benefit, over and above risk perception, naturalness, and burger contrasts?
#How does risk perception predict benefit, over and above naturalness and burger contrasts?
modA.117 <- lmer(Ben ~ FR.c + Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.117)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ FR.c + Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 +
## (1 | id)
## Data: L
##
## REML criterion at convergence: 21241.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1835 -0.4595 0.0714 0.5421 2.9890
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 209.3 14.47
## Residual 318.2 17.84
## Number of obs: 2362, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.21931 0.62586 1086.63690 96.219 < 2e-16 ***
## FR.c 0.36290 0.01926 2348.37131 18.847 < 2e-16 ***
## Naturalness.c 0.16431 0.02168 1963.13144 7.578 5.38e-14 ***
## Risk.c -0.22380 0.01799 2345.61253 -12.444 < 2e-16 ***
## C1 -2.88337 0.92594 1866.22229 -3.114 0.00187 **
## C2 -8.45758 1.65820 1995.98351 -5.100 3.71e-07 ***
## C3 4.18524 1.28960 1880.11319 3.245 0.00119 **
## C4 -8.33821 1.62488 1510.87771 -5.132 3.25e-07 ***
## C5 -4.71973 1.32563 1706.37318 -3.560 0.00038 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c Ntrln. Risk.c C1 C2 C3 C4
## FR.c 0.018
## Naturlnss.c -0.129 -0.129
## Risk.c -0.036 0.099 0.299
## C1 -0.060 0.061 0.124 -0.057
## C2 0.167 -0.245 0.234 -0.024 0.278
## C3 -0.175 -0.001 0.103 0.128 0.212 -0.009
## C4 -0.231 -0.022 0.117 -0.048 0.302 -0.010 0.331
## C5 0.054 0.129 -0.356 -0.163 -0.009 -0.118 -0.025 -0.035tab_model(modA.117,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.22 | 0.63 | 58.99 – 61.45 | 96.22 | <0.001 |
| FR c | 0.36 | 0.02 | 0.33 – 0.40 | 18.85 | <0.001 |
| Naturalness c | 0.16 | 0.02 | 0.12 – 0.21 | 7.58 | <0.001 |
| Risk c | -0.22 | 0.02 | -0.26 – -0.19 | -12.44 | <0.001 |
| C1 | -2.88 | 0.93 | -4.70 – -1.07 | -3.11 | 0.002 |
| C2 | -8.46 | 1.66 | -11.71 – -5.21 | -5.10 | <0.001 |
| C3 | 4.19 | 1.29 | 1.66 – 6.71 | 3.25 | 0.001 |
| C4 | -8.34 | 1.62 | -11.52 – -5.15 | -5.13 | <0.001 |
| C5 | -4.72 | 1.33 | -7.32 – -2.12 | -3.56 | <0.001 |
| Random Effects | |||||
| σ2 | 318.23 | ||||
| τ00 id | 209.30 | ||||
| ICC | 0.40 | ||||
| N id | 1002 | ||||
| Observations | 2362 | ||||
| Marginal R2 / Conditional R2 | 0.273 / 0.561 | ||||
Difference Benefit - Risk
Q.1: (SIMPLE MODEL) How do burger contrasts predict the difference between perceived benefit and risk?
#How do burger contrasts predict the difference between perceived benefit and risk?
modA.118 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.118)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30825.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3908 -0.5246 -0.0378 0.5578 2.8665
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 367.7 19.18
## Residual 1452.6 38.11
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.6984 0.9238 1000.3810 16.993 < 2e-16 ***
## C1 -13.5195 1.4614 2515.2585 -9.251 < 2e-16 ***
## C2 -15.5764 2.1646 2491.4027 -7.196 8.17e-13 ***
## C3 13.8468 2.2252 2488.0575 6.223 5.72e-10 ***
## C4 -19.4601 2.5134 2511.0443 -7.742 1.40e-14 ***
## C5 -10.2906 2.5228 2484.1148 -4.079 4.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.009
## C2 -0.008 -0.013
## C3 0.019 -0.030 0.001
## C4 0.007 -0.006 0.003 -0.014
## C5 0.019 0.023 -0.029 0.007 -0.005tab_model(modA.118,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.70 | 0.92 | 13.89 – 17.51 | 16.99 | <0.001 |
| C1 | -13.52 | 1.46 | -16.38 – -10.65 | -9.25 | <0.001 |
| C2 | -15.58 | 2.16 | -19.82 – -11.33 | -7.20 | <0.001 |
| C3 | 13.85 | 2.23 | 9.48 – 18.21 | 6.22 | <0.001 |
| C4 | -19.46 | 2.51 | -24.39 – -14.53 | -7.74 | <0.001 |
| C5 | -10.29 | 2.52 | -15.24 – -5.34 | -4.08 | <0.001 |
| Random Effects | |||||
| σ2 | 1452.65 | ||||
| τ00 id | 367.70 | ||||
| ICC | 0.20 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.068 / 0.257 | ||||
Q.2: How does naturalness predict the difference between perceived benefit and risk, over and above burger contrasts?
#How does naturalness predict the difference between benefit and risk?
modA.11 <- lmer(BRDiff ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30267.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0276 -0.5073 -0.0111 0.5314 3.2039
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.5 17.59
## Residual 1204.8 34.71
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.54321 0.85376 1034.40657 14.692 < 2e-16 ***
## Naturalness.c 0.79268 0.03213 2824.71754 24.674 < 2e-16 ***
## C1 -7.04853 1.35733 2523.88888 -5.193 2.24e-07 ***
## C2 -1.68053 2.05214 2491.48835 -0.819 0.413
## C3 17.68583 2.03331 2487.41975 8.698 < 2e-16 ***
## C4 -9.55744 2.32469 2506.80147 -4.111 4.06e-05 ***
## C5 -25.96748 2.38454 2498.24431 -10.890 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4
## Naturlnss.c -0.150
## C1 -0.037 0.193
## C2 -0.048 0.275 0.041
## C3 0.007 0.076 -0.015 0.022
## C4 -0.019 0.172 0.028 0.051 -0.001
## C5 0.058 -0.266 -0.030 -0.100 -0.014 -0.051tab_model(modA.11,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.54 | 0.85 | 10.87 – 14.22 | 14.69 | <0.001 |
| Naturalness c | 0.79 | 0.03 | 0.73 – 0.86 | 24.67 | <0.001 |
| C1 | -7.05 | 1.36 | -9.71 – -4.39 | -5.19 | <0.001 |
| C2 | -1.68 | 2.05 | -5.70 – 2.34 | -0.82 | 0.413 |
| C3 | 17.69 | 2.03 | 13.70 – 21.67 | 8.70 | <0.001 |
| C4 | -9.56 | 2.32 | -14.12 – -5.00 | -4.11 | <0.001 |
| C5 | -25.97 | 2.38 | -30.64 – -21.29 | -10.89 | <0.001 |
| Random Effects | |||||
| σ2 | 1204.75 | ||||
| τ00 id | 309.53 | ||||
| ICC | 0.20 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.218 / 0.378 | ||||
Familiarity/Understanding (Mean score)
Q.1: (SIMPLE MODEL) How do burger contrasts predict understanding and familiarity (mean score)?
#How do burger contrasts predict understanding and familiarity (mean score)?
modA.12 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27138.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8457 -0.5009 0.0654 0.5426 3.3148
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.0 16.43
## Residual 351.5 18.75
## Number of obs: 2986, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.369e+01 6.271e-01 9.978e+02 101.549 < 2e-16 ***
## C1 -4.554e+00 7.688e-01 2.474e+03 -5.923 3.59e-09 ***
## C2 1.780e+01 1.101e+00 2.316e+03 16.174 < 2e-16 ***
## C3 1.941e-01 1.171e+00 2.461e+03 0.166 0.868
## C4 5.538e-18 1.188e+00 1.987e+03 0.000 1.000
## C5 -6.948e+00 1.278e+00 2.306e+03 -5.437 5.97e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.041
## C2 -0.005 -0.011
## C3 -0.020 0.041 0.016
## C4 0.000 0.000 0.000 0.000
## C5 0.013 0.034 -0.026 0.018 0.000tab_model(modA.12,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.69 | 0.63 | 62.46 – 64.91 | 101.55 | <0.001 |
| C1 | -4.55 | 0.77 | -6.06 – -3.05 | -5.92 | <0.001 |
| C2 | 17.80 | 1.10 | 15.65 – 19.96 | 16.17 | <0.001 |
| C3 | 0.19 | 1.17 | -2.10 – 2.49 | 0.17 | 0.868 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -6.95 | 1.28 | -9.45 – -4.44 | -5.44 | <0.001 |
| Random Effects | |||||
| σ2 | 351.49 | ||||
| τ00 id | 269.97 | ||||
| ICC | 0.43 | ||||
| N id | 1004 | ||||
| Observations | 2986 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.472 | ||||
Q.2: How does naturalness predict understanding and familiarity (mean score), over and above burger contrasts?
#How does naturalness predict understanding and familiarity (mean score), over and above burger contrasts?
modA.130 <- lmer(FR ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.130)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21668.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7019 -0.5196 0.0477 0.5813 3.0753
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 252.5 15.89
## Residual 361.5 19.01
## Number of obs: 2373, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 63.11941 0.67690 1173.97210 93.248 < 2e-16 ***
## Naturalness.c 0.17827 0.02181 2048.71805 8.172 5.23e-16 ***
## C1 -3.14892 0.98376 1901.31850 -3.201 0.00139 **
## C2 21.23314 1.71580 1984.09624 12.375 < 2e-16 ***
## C3 0.85070 1.36482 1913.83357 0.623 0.53315
## C4 1.53022 1.73149 1589.24062 0.884 0.37696
## C5 -10.08623 1.37655 1746.99196 -7.327 3.57e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3 C4
## Naturlnss.c -0.120
## C1 -0.065 0.162
## C2 0.176 0.221 0.303
## C3 -0.171 0.065 0.223 -0.012
## C4 -0.231 0.137 0.303 -0.018 0.342
## C5 0.045 -0.309 -0.027 -0.092 -0.001 -0.041tab_model(modA.130,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.12 | 0.68 | 61.79 – 64.45 | 93.25 | <0.001 |
| Naturalness c | 0.18 | 0.02 | 0.14 – 0.22 | 8.17 | <0.001 |
| C1 | -3.15 | 0.98 | -5.08 – -1.22 | -3.20 | 0.001 |
| C2 | 21.23 | 1.72 | 17.87 – 24.60 | 12.38 | <0.001 |
| C3 | 0.85 | 1.36 | -1.83 – 3.53 | 0.62 | 0.533 |
| C4 | 1.53 | 1.73 | -1.87 – 4.93 | 0.88 | 0.377 |
| C5 | -10.09 | 1.38 | -12.79 – -7.39 | -7.33 | <0.001 |
| Random Effects | |||||
| σ2 | 361.53 | ||||
| τ00 id | 252.49 | ||||
| ICC | 0.41 | ||||
| N id | 1004 | ||||
| Observations | 2373 | ||||
| Marginal R2 / Conditional R2 | 0.082 / 0.460 | ||||
Moderators
#Center moderator variables
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
describe(L$ATNS_Score)## L$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 313 1 62.48 19.38 34.2 42.4
## .25 .50 .75 .90 .95
## 51.2 62.0 73.4 84.4 94.8
##
## lowest : 0.0 1.0 2.4 3.6 7.2, highest: 98.8 99.2 99.6 99.8 100.0describe(L$AW_Score)## L$AW_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 172 0.995 70.53 27.4 25.0 39.5
## .25 .50 .75 .90 .95
## 52.0 73.5 92.5 100.0 100.0
##
## lowest : 0.0 1.0 2.0 2.5 3.0, highest: 98.0 98.5 99.0 99.5 100.0describe(L$CNS_Score)## L$CNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 240 1 57.53 12.96 40.0 45.0
## .25 .50 .75 .90 .95
## 50.6 56.6 61.8 71.8 80.2
##
## lowest : 18.0 20.0 21.8 22.2 22.6, highest: 98.0 98.8 99.6 99.8 100.0describe(L$CCBelief_Score)## L$CCBelief_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 275 0.997 72.51 25.71 31.75 44.75
## .25 .50 .75 .90 .95
## 56.00 75.25 93.25 100.00 100.00
##
## lowest : 0.00 0.50 0.75 1.00 1.25, highest: 99.00 99.25 99.50 99.75 100.00describe(L$DS_Score)## L$DS_Score
## n missing distinct Info Mean Gmd .05 .10
## 6006 24 237 1 57.55 23.19 20.67 32.67
## .25 .50 .75 .90 .95
## 45.33 58.67 67.67 86.00 96.00
##
## lowest : 0.0000000 0.6666667 1.6666667 2.6666667 3.6666667
## highest: 98.0000000 98.3333333 99.3333333 99.6666667 100.0000000describe(L$Collectivism_Score)## L$Collectivism_Score
## n missing distinct Info Mean Gmd .05 .10
## 6018 12 292 1 66.53 23.13 30.25 40.00
## .25 .50 .75 .90 .95
## 52.25 66.75 81.75 94.00 100.00
##
## lowest : 0.00 1.25 7.00 9.50 10.50, highest: 99.00 99.25 99.50 99.75 100.00describe(L$Individualism_Score)## L$Individualism_Score
## n missing distinct Info Mean Gmd .05 .10
## 6012 18 250 0.999 73.77 20.34 45.50 51.00
## .25 .50 .75 .90 .95
## 60.50 75.00 88.00 98.75 100.00
##
## lowest : 0.00 11.50 22.75 25.00 27.75, highest: 99.00 99.25 99.50 99.75 100.00describe(L$Ideology) ## L$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 6018 12 14 0.949 1.785 1.154 -0.5 0.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.5 3.0 3.5
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 3.5 4.0 4.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
## Frequency 150 162 210 480 474 852 2142 756 312 294 84
## Proportion 0.025 0.027 0.035 0.080 0.079 0.142 0.356 0.126 0.052 0.049 0.014
##
## Value 4.5 5.0 6.0
## Frequency 36 54 12
## Proportion 0.006 0.009 0.002L$ATNS_Score.c <- L$ATNS_Score - 62.48
L$AW_Score.c <- L$AW_Score - 70.53
L$CNS_Score.c <- L$CNS_Score - 57.53
L$CCBelief_Score.c <- L$CCBelief_Score - 72.51
L$DS_Score.c <- L$DS_Score - 57.55
L$Collectivism_Score.c <- L$Collectivism_Score - 66.53
L$Individualism_Score.c <- L$Individualism_Score - 73.77
L$Ideology.c <- L$Ideology - 2.956Support
### Aversion to Tampering with Nature #### Q.1 (AVERSION TO TAMPERING
WITH NATURE) How does aversion to tampering with nature predict support,
over and above burger contrasts?
modA.8901 <- lmer(Behav ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.8901)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c *
## C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c *
## C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28124.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5218 -0.3930 0.0439 0.4277 3.3857
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 456.5 21.37
## Residual 419.8 20.49
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.51938 0.77734 998.95409 72.709 < 2e-16 ***
## ATNS_Score.c 0.02073 0.04486 1006.01573 0.462 0.644066
## C1 -6.51128 0.79495 2188.56906 -8.191 4.36e-16 ***
## C2 -6.55557 1.21602 2277.88893 -5.391 7.73e-08 ***
## C3 5.16168 1.26371 2325.12260 4.085 4.57e-05 ***
## C4 0.19965 1.40048 2277.86144 0.143 0.886651
## C5 -5.01140 1.42432 2300.31014 -3.518 0.000443 ***
## ATNS_Score.c:C1 -0.28971 0.04638 2207.30709 -6.247 5.01e-10 ***
## ATNS_Score.c:C2 -0.22090 0.07142 2287.78167 -3.093 0.002006 **
## ATNS_Score.c:C3 0.05207 0.07430 2360.48116 0.701 0.483489
## ATNS_Score.c:C4 0.14716 0.08090 2272.15851 1.819 0.069050 .
## ATNS_Score.c:C5 -0.25028 0.08229 2319.94653 -3.041 0.002381 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c 0.002
## C1 -0.002 -0.004
## C2 -0.020 0.006 0.005
## C3 -0.008 -0.005 -0.057 0.027
## C4 -0.018 -0.011 -0.064 0.052 0.000
## C5 -0.013 -0.021 0.068 -0.031 0.065 0.075
## ATNS_Sc.:C1 -0.004 -0.004 0.008 0.026 0.006 0.014 -0.027
## ATNS_Sc.:C2 0.006 -0.010 0.026 -0.002 0.000 -0.004 0.046 0.033
## ATNS_Sc.:C3 -0.005 -0.008 0.006 0.000 -0.009 0.021 0.003 -0.067
## ATNS_Sc.:C4 -0.011 -0.017 0.014 -0.004 0.020 0.008 0.005 -0.058
## ATNS_Sc.:C5 -0.022 -0.020 -0.027 0.046 0.003 0.006 -0.017 0.060
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.029
## ATNS_Sc.:C4 0.052 0.013
## ATNS_Sc.:C5 -0.010 0.077 0.077tab_model(modA.8901,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.52 | 0.78 | 55.00 – 58.04 | 72.71 | <0.001 |
| ATNS Score c | 0.02 | 0.04 | -0.07 – 0.11 | 0.46 | 0.644 |
| C1 | -6.51 | 0.79 | -8.07 – -4.95 | -8.19 | <0.001 |
| C2 | -6.56 | 1.22 | -8.94 – -4.17 | -5.39 | <0.001 |
| C3 | 5.16 | 1.26 | 2.68 – 7.64 | 4.08 | <0.001 |
| C4 | 0.20 | 1.40 | -2.55 – 2.95 | 0.14 | 0.887 |
| C5 | -5.01 | 1.42 | -7.80 – -2.22 | -3.52 | <0.001 |
| ATNS Score c * C1 | -0.29 | 0.05 | -0.38 – -0.20 | -6.25 | <0.001 |
| ATNS Score c * C2 | -0.22 | 0.07 | -0.36 – -0.08 | -3.09 | 0.002 |
| ATNS Score c * C3 | 0.05 | 0.07 | -0.09 – 0.20 | 0.70 | 0.483 |
| ATNS Score c * C4 | 0.15 | 0.08 | -0.01 – 0.31 | 1.82 | 0.069 |
| ATNS Score c * C5 | -0.25 | 0.08 | -0.41 – -0.09 | -3.04 | 0.002 |
| Random Effects | |||||
| σ2 | 419.85 | ||||
| τ00 id | 456.55 | ||||
| ICC | 0.52 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.033 / 0.537 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on naturalness in predicting support, over and above burger contrasts?
modA.89012 <- lmer(Behav ~ ATNS_Score.c + Naturalness.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.89012)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ ATNS_Score.c + Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25124.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8948 -0.4308 0.0529 0.4773 3.0369
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 410.8 20.27
## Residual 400.1 20.00
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.92978 0.77126 1090.29922 71.220 < 2e-16 ***
## ATNS_Score.c 0.03067 0.04392 1047.84815 0.698 0.485060
## Naturalness.c 0.39099 0.02152 2131.26191 18.168 < 2e-16 ***
## C1 -3.15021 0.90167 1929.39496 -3.494 0.000487 ***
## C2 0.27259 1.24912 1966.30313 0.218 0.827275
## C3 7.14563 1.39336 2044.60797 5.128 3.20e-07 ***
## C4 5.28740 1.89654 2069.73142 2.788 0.005353 **
## C5 -12.53192 1.45260 1978.49005 -8.627 < 2e-16 ***
## ATNS_Score.c:C1 -0.27263 0.04995 1909.54208 -5.458 5.45e-08 ***
## ATNS_Score.c:C2 -0.06369 0.07059 1974.99391 -0.902 0.367015
## ATNS_Score.c:C3 0.01887 0.07972 2069.37493 0.237 0.812887
## ATNS_Score.c:C4 0.13783 0.10329 2060.70613 1.334 0.182192
## ATNS_Score.c:C5 -0.36976 0.08077 1993.81104 -4.578 4.99e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.89012,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.93 | 0.77 | 53.42 – 56.44 | 71.22 | <0.001 |
| ATNS Score c | 0.03 | 0.04 | -0.06 – 0.12 | 0.70 | 0.485 |
| Naturalness c | 0.39 | 0.02 | 0.35 – 0.43 | 18.17 | <0.001 |
| C1 | -3.15 | 0.90 | -4.92 – -1.38 | -3.49 | <0.001 |
| C2 | 0.27 | 1.25 | -2.18 – 2.72 | 0.22 | 0.827 |
| C3 | 7.15 | 1.39 | 4.41 – 9.88 | 5.13 | <0.001 |
| C4 | 5.29 | 1.90 | 1.57 – 9.01 | 2.79 | 0.005 |
| C5 | -12.53 | 1.45 | -15.38 – -9.68 | -8.63 | <0.001 |
| ATNS Score c * C1 | -0.27 | 0.05 | -0.37 – -0.17 | -5.46 | <0.001 |
| ATNS Score c * C2 | -0.06 | 0.07 | -0.20 – 0.07 | -0.90 | 0.367 |
| ATNS Score c * C3 | 0.02 | 0.08 | -0.14 – 0.18 | 0.24 | 0.813 |
| ATNS Score c * C4 | 0.14 | 0.10 | -0.06 – 0.34 | 1.33 | 0.182 |
| ATNS Score c * C5 | -0.37 | 0.08 | -0.53 – -0.21 | -4.58 | <0.001 |
| Random Effects | |||||
| σ2 | 400.11 | ||||
| τ00 id | 410.84 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.109 / 0.560 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict support, over and above burger contrasts?
modA.8991 <- lmer(Behav ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.8991)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c * C1 +
## AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 + AW_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28031.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9820 -0.4159 0.0569 0.4614 3.5325
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 408.9 20.22
## Residual 420.5 20.51
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.661e+01 7.464e-01 9.933e+02 75.835 < 2e-16 ***
## AW_Score.c 2.669e-01 3.051e-02 1.004e+03 8.747 < 2e-16 ***
## C1 -6.656e+00 7.943e-01 2.198e+03 -8.379 < 2e-16 ***
## C2 -6.424e+00 1.213e+00 2.294e+03 -5.297 1.29e-07 ***
## C3 4.781e+00 1.262e+00 2.346e+03 3.790 0.000155 ***
## C4 4.394e-01 1.401e+00 2.295e+03 0.314 0.753787
## C5 -5.402e+00 1.420e+00 2.319e+03 -3.804 0.000146 ***
## AW_Score.c:C1 6.134e-03 3.263e-02 2.199e+03 0.188 0.850918
## AW_Score.c:C2 -1.154e-01 5.008e-02 2.322e+03 -2.304 0.021302 *
## AW_Score.c:C3 2.679e-01 5.255e-02 2.384e+03 5.098 3.71e-07 ***
## AW_Score.c:C4 -3.476e-01 5.808e-02 2.305e+03 -5.986 2.49e-09 ***
## AW_Score.c:C5 -7.985e-02 5.767e-02 2.314e+03 -1.384 0.166353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c 0.002
## C1 -0.003 0.006
## C2 -0.020 -0.004 0.006
## C3 -0.008 0.000 -0.054 0.027
## C4 -0.017 -0.022 -0.063 0.050 -0.002
## C5 -0.014 -0.013 0.065 -0.027 0.063 0.073
## AW_Scr.c:C1 0.006 -0.011 0.006 0.003 -0.007 0.041 -0.010
## AW_Scr.c:C2 -0.004 -0.017 0.004 0.022 -0.001 0.013 0.029 0.016
## AW_Scr.c:C3 0.000 -0.012 -0.008 -0.001 -0.012 0.045 0.006 -0.052 0.028
## AW_Scr.c:C4 -0.022 -0.009 0.041 0.013 0.045 -0.016 0.007 -0.073 0.053
## AW_Scr.c:C5 -0.013 -0.018 -0.011 0.028 0.006 0.007 0.026 0.062 -0.024
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 -0.030
## AW_Scr.c:C5 0.057 0.070tab_model(modA.8991,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.61 | 0.75 | 55.14 – 58.07 | 75.83 | <0.001 |
| AW Score c | 0.27 | 0.03 | 0.21 – 0.33 | 8.75 | <0.001 |
| C1 | -6.66 | 0.79 | -8.21 – -5.10 | -8.38 | <0.001 |
| C2 | -6.42 | 1.21 | -8.80 – -4.05 | -5.30 | <0.001 |
| C3 | 4.78 | 1.26 | 2.31 – 7.25 | 3.79 | <0.001 |
| C4 | 0.44 | 1.40 | -2.31 – 3.19 | 0.31 | 0.754 |
| C5 | -5.40 | 1.42 | -8.19 – -2.62 | -3.80 | <0.001 |
| AW Score c * C1 | 0.01 | 0.03 | -0.06 – 0.07 | 0.19 | 0.851 |
| AW Score c * C2 | -0.12 | 0.05 | -0.21 – -0.02 | -2.30 | 0.021 |
| AW Score c * C3 | 0.27 | 0.05 | 0.16 – 0.37 | 5.10 | <0.001 |
| AW Score c * C4 | -0.35 | 0.06 | -0.46 – -0.23 | -5.99 | <0.001 |
| AW Score c * C5 | -0.08 | 0.06 | -0.19 – 0.03 | -1.38 | 0.166 |
| Random Effects | |||||
| σ2 | 420.52 | ||||
| τ00 id | 408.93 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.082 / 0.535 | ||||
Q.2 (ANIMAL WELFARE) Does animal welfare depend on naturalness in predicting support, over and above burger contrasts?
modA.89913 <- lmer(Behav ~ AW_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89913)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ AW_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## AW_Score.c * C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c *
## C4 + AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25075.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6831 -0.4372 0.0584 0.5032 3.1340
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 369.2 19.21
## Residual 404.6 20.11
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.503e+01 7.448e-01 1.090e+03 73.890 < 2e-16 ***
## AW_Score.c 2.548e-01 3.047e-02 1.101e+03 8.363 < 2e-16 ***
## Naturalness.c 3.779e-01 2.170e-02 2.183e+03 17.410 < 2e-16 ***
## C1 -3.426e+00 9.029e-01 1.947e+03 -3.794 0.000153 ***
## C2 2.625e-01 1.254e+00 1.988e+03 0.209 0.834211
## C3 6.773e+00 1.396e+00 2.073e+03 4.851 1.32e-06 ***
## C4 5.272e+00 1.892e+00 2.097e+03 2.786 0.005388 **
## C5 -1.274e+01 1.456e+00 2.001e+03 -8.754 < 2e-16 ***
## AW_Score.c:Naturalness.c -2.841e-04 8.072e-04 2.272e+03 -0.352 0.724921
## AW_Score.c:C1 -1.352e-02 3.776e-02 1.960e+03 -0.358 0.720299
## AW_Score.c:C2 -3.607e-02 5.130e-02 2.024e+03 -0.703 0.482067
## AW_Score.c:C3 2.264e-01 5.811e-02 2.101e+03 3.896 0.000101 ***
## AW_Score.c:C4 -3.001e-01 7.819e-02 2.137e+03 -3.838 0.000128 ***
## AW_Score.c:C5 -1.239e-01 5.876e-02 1.985e+03 -2.109 0.035111 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89913,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 55.03 | 0.74 | 53.57 – 56.49 | 73.89 | <0.001 |
| AW Score c | 0.25 | 0.03 | 0.20 – 0.31 | 8.36 | <0.001 |
| Naturalness c | 0.38 | 0.02 | 0.34 – 0.42 | 17.41 | <0.001 |
| C1 | -3.43 | 0.90 | -5.20 – -1.66 | -3.79 | <0.001 |
| C2 | 0.26 | 1.25 | -2.20 – 2.72 | 0.21 | 0.834 |
| C3 | 6.77 | 1.40 | 4.04 – 9.51 | 4.85 | <0.001 |
| C4 | 5.27 | 1.89 | 1.56 – 8.98 | 2.79 | 0.005 |
| C5 | -12.74 | 1.46 | -15.60 – -9.89 | -8.75 | <0.001 |
|
AW Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.35 | 0.725 |
| AW Score c * C1 | -0.01 | 0.04 | -0.09 – 0.06 | -0.36 | 0.720 |
| AW Score c * C2 | -0.04 | 0.05 | -0.14 – 0.06 | -0.70 | 0.482 |
| AW Score c * C3 | 0.23 | 0.06 | 0.11 – 0.34 | 3.90 | <0.001 |
| AW Score c * C4 | -0.30 | 0.08 | -0.45 – -0.15 | -3.84 | <0.001 |
| AW Score c * C5 | -0.12 | 0.06 | -0.24 – -0.01 | -2.11 | 0.035 |
| Random Effects | |||||
| σ2 | 404.55 | ||||
| τ00 id | 369.18 | ||||
| ICC | 0.48 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.149 / 0.555 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict support, over and above burger contrasts?
modA.8971 <- lmer(Behav ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.8971)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c *
## C1 + CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c *
## C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28144.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3227 -0.4325 0.0614 0.4358 3.9903
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 447.7 21.16
## Residual 427.1 20.67
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.46505 0.77311 999.72255 73.037 < 2e-16 ***
## CNS_Score.c 0.16056 0.06219 998.43670 2.582 0.009972 **
## C1 -6.57168 0.80105 2193.95159 -8.204 3.92e-16 ***
## C2 -6.27999 1.22553 2284.09903 -5.124 3.24e-07 ***
## C3 5.13715 1.27314 2333.97435 4.035 5.64e-05 ***
## C4 0.53765 1.41180 2286.98531 0.381 0.703365
## C5 -4.99429 1.43317 2309.97720 -3.485 0.000502 ***
## CNS_Score.c:C1 -0.01532 0.06419 2189.05591 -0.239 0.811415
## CNS_Score.c:C2 -0.17692 0.10139 2282.15224 -1.745 0.081127 .
## CNS_Score.c:C3 0.30285 0.10194 2322.76919 2.971 0.003001 **
## CNS_Score.c:C4 -0.43609 0.11205 2269.92617 -3.892 0.000102 ***
## CNS_Score.c:C5 -0.14688 0.11227 2309.28277 -1.308 0.190895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c -0.003
## C1 -0.002 0.009
## C2 -0.018 0.013 0.007
## C3 -0.007 0.008 -0.056 0.027
## C4 -0.017 0.002 -0.064 0.051 -0.001
## C5 -0.014 0.009 0.066 -0.029 0.064 0.075
## CNS_Scr.:C1 0.009 -0.001 -0.001 0.031 -0.016 0.004 0.007
## CNS_Scr.:C2 0.013 0.012 0.029 0.046 -0.002 0.005 -0.006 0.079
## CNS_Scr.:C3 0.007 -0.005 -0.016 -0.002 -0.013 0.006 -0.004 -0.066
## CNS_Scr.:C4 0.002 -0.027 0.004 0.005 0.005 -0.041 -0.008 -0.031
## CNS_Scr.:C5 0.009 -0.039 0.008 -0.005 -0.004 -0.009 -0.026 0.031
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 0.017
## CNS_Scr.:C4 0.046 0.009
## CNS_Scr.:C5 0.016 0.060 0.086tab_model(modA.8971,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.47 | 0.77 | 54.95 – 57.98 | 73.04 | <0.001 |
| CNS Score c | 0.16 | 0.06 | 0.04 – 0.28 | 2.58 | 0.010 |
| C1 | -6.57 | 0.80 | -8.14 – -5.00 | -8.20 | <0.001 |
| C2 | -6.28 | 1.23 | -8.68 – -3.88 | -5.12 | <0.001 |
| C3 | 5.14 | 1.27 | 2.64 – 7.63 | 4.04 | <0.001 |
| C4 | 0.54 | 1.41 | -2.23 – 3.31 | 0.38 | 0.703 |
| C5 | -4.99 | 1.43 | -7.80 – -2.18 | -3.48 | <0.001 |
| CNS Score c * C1 | -0.02 | 0.06 | -0.14 – 0.11 | -0.24 | 0.811 |
| CNS Score c * C2 | -0.18 | 0.10 | -0.38 – 0.02 | -1.74 | 0.081 |
| CNS Score c * C3 | 0.30 | 0.10 | 0.10 – 0.50 | 2.97 | 0.003 |
| CNS Score c * C4 | -0.44 | 0.11 | -0.66 – -0.22 | -3.89 | <0.001 |
| CNS Score c * C5 | -0.15 | 0.11 | -0.37 – 0.07 | -1.31 | 0.191 |
| Random Effects | |||||
| σ2 | 427.09 | ||||
| τ00 id | 447.68 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.032 / 0.527 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.897133 <- lmer(Behav ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.897133)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25154.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7550 -0.4457 0.0476 0.4859 3.2084
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 400.9 20.02
## Residual 408.7 20.22
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.489e+01 7.667e-01 1.091e+03 71.594 < 2e-16 ***
## CNS_Score.c 8.013e-02 6.261e-02 1.140e+03 1.280 0.200891
## Naturalness.c 3.715e-01 2.211e-02 2.169e+03 16.801 < 2e-16 ***
## C1 -3.301e+00 9.071e-01 1.935e+03 -3.639 0.000281 ***
## C2 5.986e-01 1.263e+00 1.978e+03 0.474 0.635599
## C3 6.964e+00 1.404e+00 2.056e+03 4.960 7.61e-07 ***
## C4 5.549e+00 1.906e+00 2.082e+03 2.911 0.003643 **
## C5 -1.234e+01 1.468e+00 1.994e+03 -8.406 < 2e-16 ***
## CNS_Score.c:Naturalness.c 4.440e-03 1.451e-03 2.157e+03 3.060 0.002241 **
## CNS_Score.c:C1 1.877e-01 7.735e-02 1.975e+03 2.427 0.015315 *
## CNS_Score.c:C2 3.800e-02 1.059e-01 2.014e+03 0.359 0.719823
## CNS_Score.c:C3 3.729e-01 1.153e-01 2.054e+03 3.234 0.001240 **
## CNS_Score.c:C4 -9.741e-02 1.616e-01 2.117e+03 -0.603 0.546652
## CNS_Score.c:C5 -2.389e-01 1.131e-01 1.971e+03 -2.112 0.034829 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.897133,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.89 | 0.77 | 53.39 – 56.39 | 71.59 | <0.001 |
| CNS Score c | 0.08 | 0.06 | -0.04 – 0.20 | 1.28 | 0.201 |
| Naturalness c | 0.37 | 0.02 | 0.33 – 0.41 | 16.80 | <0.001 |
| C1 | -3.30 | 0.91 | -5.08 – -1.52 | -3.64 | <0.001 |
| C2 | 0.60 | 1.26 | -1.88 – 3.08 | 0.47 | 0.636 |
| C3 | 6.96 | 1.40 | 4.21 – 9.72 | 4.96 | <0.001 |
| C4 | 5.55 | 1.91 | 1.81 – 9.29 | 2.91 | 0.004 |
| C5 | -12.34 | 1.47 | -15.22 – -9.46 | -8.41 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 3.06 | 0.002 |
| CNS Score c * C1 | 0.19 | 0.08 | 0.04 – 0.34 | 2.43 | 0.015 |
| CNS Score c * C2 | 0.04 | 0.11 | -0.17 – 0.25 | 0.36 | 0.720 |
| CNS Score c * C3 | 0.37 | 0.12 | 0.15 – 0.60 | 3.23 | 0.001 |
| CNS Score c * C4 | -0.10 | 0.16 | -0.41 – 0.22 | -0.60 | 0.547 |
| CNS Score c * C5 | -0.24 | 0.11 | -0.46 – -0.02 | -2.11 | 0.035 |
| Random Effects | |||||
| σ2 | 408.71 | ||||
| τ00 id | 400.91 | ||||
| ICC | 0.50 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.105 / 0.548 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict support, over and above burger contrasts?
modA.8961 <- lmer(Behav ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.8961)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Behav ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28002.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3112 -0.4142 0.0688 0.4565 3.4842
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 391.4 19.78
## Residual 421.1 20.52
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.52893 0.73457 994.53161 76.955 < 2e-16 ***
## CCBelief_Score.c 0.32077 0.03148 989.79798 10.191 < 2e-16 ***
## C1 -6.46715 0.79374 2204.66417 -8.148 6.15e-16 ***
## C2 -6.34413 1.21279 2302.80453 -5.231 1.84e-07 ***
## C3 5.01424 1.25983 2357.16564 3.980 7.10e-05 ***
## C4 -0.06310 1.39741 2305.18428 -0.045 0.963988
## C5 -5.42516 1.41970 2330.21733 -3.821 0.000136 ***
## CCBelief_Score.c:C1 0.04844 0.03390 2189.13546 1.429 0.153142
## CCBelief_Score.c:C2 -0.15864 0.05305 2315.53363 -2.990 0.002818 **
## CCBelief_Score.c:C3 0.21620 0.05455 2342.64680 3.964 7.60e-05 ***
## CCBelief_Score.c:C4 -0.39110 0.05883 2297.29214 -6.648 3.71e-11 ***
## CCBelief_Score.c:C5 -0.04432 0.05973 2329.22207 -0.742 0.458191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c 0.006
## C1 -0.002 -0.002
## C2 -0.019 -0.008 0.005
## C3 -0.007 -0.010 -0.055 0.026
## C4 -0.018 -0.010 -0.061 0.050 0.000
## C5 -0.013 -0.013 0.065 -0.029 0.063 0.072
## CCBlf_S.:C1 -0.002 -0.006 0.015 -0.012 0.015 0.010 -0.011
## CCBlf_S.:C2 -0.009 0.002 -0.012 0.000 0.001 0.008 0.020 0.049
## CCBlf_S.:C3 -0.010 0.014 0.015 0.000 0.014 0.015 0.015 -0.083
## CCBlf_S.:C4 -0.010 -0.010 0.009 0.008 0.016 0.034 0.003 -0.051
## CCBlf_S.:C5 -0.014 -0.009 -0.011 0.020 0.016 0.003 0.033 0.071
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 0.020
## CCBlf_S.:C4 0.048 -0.021
## CCBlf_S.:C5 -0.030 0.047 0.078tab_model(modA.8961,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.53 | 0.73 | 55.09 – 57.97 | 76.95 | <0.001 |
| CCBelief Score c | 0.32 | 0.03 | 0.26 – 0.38 | 10.19 | <0.001 |
| C1 | -6.47 | 0.79 | -8.02 – -4.91 | -8.15 | <0.001 |
| C2 | -6.34 | 1.21 | -8.72 – -3.97 | -5.23 | <0.001 |
| C3 | 5.01 | 1.26 | 2.54 – 7.48 | 3.98 | <0.001 |
| C4 | -0.06 | 1.40 | -2.80 – 2.68 | -0.05 | 0.964 |
| C5 | -5.43 | 1.42 | -8.21 – -2.64 | -3.82 | <0.001 |
| CCBelief Score c * C1 | 0.05 | 0.03 | -0.02 – 0.11 | 1.43 | 0.153 |
| CCBelief Score c * C2 | -0.16 | 0.05 | -0.26 – -0.05 | -2.99 | 0.003 |
| CCBelief Score c * C3 | 0.22 | 0.05 | 0.11 – 0.32 | 3.96 | <0.001 |
| CCBelief Score c * C4 | -0.39 | 0.06 | -0.51 – -0.28 | -6.65 | <0.001 |
| CCBelief Score c * C5 | -0.04 | 0.06 | -0.16 – 0.07 | -0.74 | 0.458 |
| Random Effects | |||||
| σ2 | 421.13 | ||||
| τ00 id | 391.39 | ||||
| ICC | 0.48 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.097 / 0.532 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perception sof naturalness in predicting support, over and above burger contrasts?
modA.89614 <- lmer(Behav ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89614)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25039.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6866 -0.4357 0.0620 0.5055 3.1245
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 354.1 18.82
## Residual 405.2 20.13
## Number of obs: 2694, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.496e+01 7.344e-01 1.092e+03 74.837
## CCBelief_Score.c 3.083e-01 3.134e-02 1.066e+03 9.836
## Naturalness.c 3.768e-01 2.182e-02 2.195e+03 17.267
## C1 -3.252e+00 9.007e-01 1.951e+03 -3.611
## C2 2.690e-01 1.253e+00 1.994e+03 0.215
## C3 6.964e+00 1.393e+00 2.082e+03 5.000
## C4 5.042e+00 1.889e+00 2.109e+03 2.669
## C5 -1.282e+01 1.458e+00 2.015e+03 -8.789
## CCBelief_Score.c:Naturalness.c -9.168e-05 8.636e-04 2.314e+03 -0.106
## CCBelief_Score.c:C1 4.947e-02 3.887e-02 1.978e+03 1.273
## CCBelief_Score.c:C2 -6.842e-02 5.406e-02 1.999e+03 -1.266
## CCBelief_Score.c:C3 1.621e-01 5.999e-02 2.084e+03 2.703
## CCBelief_Score.c:C4 -3.505e-01 7.825e-02 2.150e+03 -4.479
## CCBelief_Score.c:C5 -1.387e-01 6.028e-02 1.972e+03 -2.301
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 0.000313 ***
## C2 0.830060
## C3 6.23e-07 ***
## C4 0.007668 **
## C5 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.915462
## CCBelief_Score.c:C1 0.203243
## CCBelief_Score.c:C2 0.205775
## CCBelief_Score.c:C3 0.006930 **
## CCBelief_Score.c:C4 7.89e-06 ***
## CCBelief_Score.c:C5 0.021502 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89614,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.96 | 0.73 | 53.52 – 56.40 | 74.84 | <0.001 |
| CCBelief Score c | 0.31 | 0.03 | 0.25 – 0.37 | 9.84 | <0.001 |
| Naturalness c | 0.38 | 0.02 | 0.33 – 0.42 | 17.27 | <0.001 |
| C1 | -3.25 | 0.90 | -5.02 – -1.49 | -3.61 | <0.001 |
| C2 | 0.27 | 1.25 | -2.19 – 2.73 | 0.21 | 0.830 |
| C3 | 6.96 | 1.39 | 4.23 – 9.70 | 5.00 | <0.001 |
| C4 | 5.04 | 1.89 | 1.34 – 8.75 | 2.67 | 0.008 |
| C5 | -12.82 | 1.46 | -15.68 – -9.96 | -8.79 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.11 | 0.915 |
| CCBelief Score c * C1 | 0.05 | 0.04 | -0.03 – 0.13 | 1.27 | 0.203 |
| CCBelief Score c * C2 | -0.07 | 0.05 | -0.17 – 0.04 | -1.27 | 0.206 |
| CCBelief Score c * C3 | 0.16 | 0.06 | 0.04 – 0.28 | 2.70 | 0.007 |
| CCBelief Score c * C4 | -0.35 | 0.08 | -0.50 – -0.20 | -4.48 | <0.001 |
| CCBelief Score c * C5 | -0.14 | 0.06 | -0.26 – -0.02 | -2.30 | 0.021 |
| Random Effects | |||||
| σ2 | 405.21 | ||||
| τ00 id | 354.08 | ||||
| ICC | 0.47 | ||||
| N id | 1001 | ||||
| Observations | 2694 | ||||
| Marginal R2 / Conditional R2 | 0.164 / 0.554 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict support, over and above burger contrasts?
modA.89612 <- lmer(Behav ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.89612)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c * C1 +
## DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 + DS_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28151
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5669 -0.4227 0.0461 0.4341 3.3359
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 450.8 21.23
## Residual 429.8 20.73
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.47957 0.77603 998.46933 72.780 < 2e-16 ***
## DS_Score.c 0.03734 0.03745 1013.09436 0.997 0.318949
## C1 -6.49844 0.80411 2195.72507 -8.082 1.04e-15 ***
## C2 -6.31338 1.22827 2283.04312 -5.140 2.98e-07 ***
## C3 5.11769 1.27787 2333.74039 4.005 6.40e-05 ***
## C4 0.22164 1.41666 2285.86708 0.156 0.875692
## C5 -5.15453 1.43820 2307.52211 -3.584 0.000345 ***
## DS_Score.c:C1 -0.09285 0.03901 2189.66639 -2.380 0.017385 *
## DS_Score.c:C2 -0.16936 0.05859 2295.97763 -2.890 0.003883 **
## DS_Score.c:C3 0.03162 0.06243 2368.29181 0.506 0.612607
## DS_Score.c:C4 -0.03941 0.07047 2307.18688 -0.559 0.576012
## DS_Score.c:C5 -0.09112 0.07029 2317.05333 -1.296 0.195025
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c -0.001
## C1 -0.003 0.002
## C2 -0.019 0.005 0.005
## C3 -0.008 -0.005 -0.057 0.028
## C4 -0.018 -0.008 -0.064 0.052 0.000
## C5 -0.014 -0.012 0.067 -0.029 0.064 0.074
## DS_Scr.c:C1 0.003 -0.010 -0.018 0.021 0.017 0.029 -0.013
## DS_Scr.c:C2 0.005 -0.035 0.022 0.004 -0.013 -0.004 0.021 -0.019
## DS_Scr.c:C3 -0.005 -0.023 0.018 -0.013 -0.023 0.013 0.018 -0.038 0.043
## DS_Scr.c:C4 -0.009 -0.032 0.030 -0.004 0.013 -0.025 0.002 -0.046 0.062
## DS_Scr.c:C5 -0.011 -0.037 -0.011 0.020 0.017 0.001 0.003 0.037 0.018
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 0.016
## DS_Scr.c:C5 0.066 0.085tab_model(modA.89612,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.48 | 0.78 | 54.96 – 58.00 | 72.78 | <0.001 |
| DS Score c | 0.04 | 0.04 | -0.04 – 0.11 | 1.00 | 0.319 |
| C1 | -6.50 | 0.80 | -8.08 – -4.92 | -8.08 | <0.001 |
| C2 | -6.31 | 1.23 | -8.72 – -3.91 | -5.14 | <0.001 |
| C3 | 5.12 | 1.28 | 2.61 – 7.62 | 4.00 | <0.001 |
| C4 | 0.22 | 1.42 | -2.56 – 3.00 | 0.16 | 0.876 |
| C5 | -5.15 | 1.44 | -7.97 – -2.33 | -3.58 | <0.001 |
| DS Score c * C1 | -0.09 | 0.04 | -0.17 – -0.02 | -2.38 | 0.017 |
| DS Score c * C2 | -0.17 | 0.06 | -0.28 – -0.05 | -2.89 | 0.004 |
| DS Score c * C3 | 0.03 | 0.06 | -0.09 – 0.15 | 0.51 | 0.613 |
| DS Score c * C4 | -0.04 | 0.07 | -0.18 – 0.10 | -0.56 | 0.576 |
| DS Score c * C5 | -0.09 | 0.07 | -0.23 – 0.05 | -1.30 | 0.195 |
| Random Effects | |||||
| σ2 | 429.77 | ||||
| τ00 id | 450.77 | ||||
| ICC | 0.51 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.026 / 0.524 | ||||
Q.2 (DISGUST SENSITIVITY) Does sensitivity to disgust depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.896124 <- lmer(Behav ~ DS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.896124)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ DS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## DS_Score.c * C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c *
## C4 + DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25164.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8474 -0.4537 0.0466 0.4897 3.0125
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 401.1 20.03
## Residual 411.3 20.28
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.491e+01 7.683e-01 1.091e+03 71.474 < 2e-16 ***
## DS_Score.c 6.661e-02 3.737e-02 1.133e+03 1.782 0.074949 .
## Naturalness.c 3.904e-01 2.159e-02 2.139e+03 18.084 < 2e-16 ***
## C1 -3.273e+00 9.125e-01 1.938e+03 -3.587 0.000343 ***
## C2 4.446e-01 1.265e+00 1.978e+03 0.351 0.725340
## C3 6.969e+00 1.410e+00 2.055e+03 4.942 8.35e-07 ***
## C4 4.873e+00 1.918e+00 2.083e+03 2.541 0.011120 *
## C5 -1.281e+01 1.470e+00 1.990e+03 -8.711 < 2e-16 ***
## DS_Score.c:Naturalness.c -1.732e-03 9.205e-04 2.128e+03 -1.882 0.060020 .
## DS_Score.c:C1 -1.827e-01 4.641e-02 1.957e+03 -3.936 8.59e-05 ***
## DS_Score.c:C2 -1.395e-01 6.005e-02 1.973e+03 -2.323 0.020302 *
## DS_Score.c:C3 -6.191e-02 6.943e-02 2.096e+03 -0.892 0.372663
## DS_Score.c:C4 -1.598e-01 9.555e-02 2.095e+03 -1.673 0.094503 .
## DS_Score.c:C5 -5.428e-02 7.147e-02 1.983e+03 -0.759 0.447646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.896124,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.91 | 0.77 | 53.41 – 56.42 | 71.47 | <0.001 |
| DS Score c | 0.07 | 0.04 | -0.01 – 0.14 | 1.78 | 0.075 |
| Naturalness c | 0.39 | 0.02 | 0.35 – 0.43 | 18.08 | <0.001 |
| C1 | -3.27 | 0.91 | -5.06 – -1.48 | -3.59 | <0.001 |
| C2 | 0.44 | 1.27 | -2.04 – 2.93 | 0.35 | 0.725 |
| C3 | 6.97 | 1.41 | 4.20 – 9.73 | 4.94 | <0.001 |
| C4 | 4.87 | 1.92 | 1.11 – 8.63 | 2.54 | 0.011 |
| C5 | -12.81 | 1.47 | -15.69 – -9.92 | -8.71 | <0.001 |
|
DS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.88 | 0.060 |
| DS Score c * C1 | -0.18 | 0.05 | -0.27 – -0.09 | -3.94 | <0.001 |
| DS Score c * C2 | -0.14 | 0.06 | -0.26 – -0.02 | -2.32 | 0.020 |
| DS Score c * C3 | -0.06 | 0.07 | -0.20 – 0.07 | -0.89 | 0.373 |
| DS Score c * C4 | -0.16 | 0.10 | -0.35 – 0.03 | -1.67 | 0.094 |
| DS Score c * C5 | -0.05 | 0.07 | -0.19 – 0.09 | -0.76 | 0.448 |
| Random Effects | |||||
| σ2 | 411.30 | ||||
| τ00 id | 401.13 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.102 / 0.545 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict support, over and above burger contrasts?
modA.8951 <- lmer(Behav ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 +(1|id), data = L)
summary(modA.8951)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Behav ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c *
## C1 + Collectivism_Score.c * C2 + Collectivism_Score.c * C3 +
## Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28101.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4490 -0.4202 0.0486 0.4310 3.5493
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 414.7 20.36
## Residual 428.5 20.70
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.51681 0.75167 1001.72085 75.189 < 2e-16 ***
## Collectivism_Score.c 0.30187 0.03683 1005.18595 8.196 7.55e-16 ***
## C1 -6.52985 0.80108 2206.93271 -8.151 5.96e-16 ***
## C2 -6.22912 1.22353 2301.14728 -5.091 3.85e-07 ***
## C3 5.04230 1.27209 2352.75612 3.964 7.60e-05 ***
## C4 0.33719 1.41119 2304.51279 0.239 0.811172
## C5 -5.20822 1.43266 2327.49337 -3.635 0.000284 ***
## Collectivism_Score.c:C1 -0.13331 0.03921 2197.11612 -3.400 0.000686 ***
## Collectivism_Score.c:C2 -0.06726 0.06120 2326.13188 -1.099 0.271845
## Collectivism_Score.c:C3 0.01090 0.06368 2370.51535 0.171 0.864155
## Collectivism_Score.c:C4 0.13297 0.06755 2284.97843 1.969 0.049114 *
## Collectivism_Score.c:C5 -0.08155 0.06966 2325.62037 -1.171 0.241866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. 0.005
## C1 -0.003 -0.002
## C2 -0.019 -0.003 0.005
## C3 -0.007 -0.010 -0.056 0.027
## C4 -0.018 -0.007 -0.062 0.050 0.000
## C5 -0.014 -0.013 0.065 -0.029 0.063 0.073
## Cllct_S.:C1 -0.003 -0.004 0.014 -0.001 0.014 0.000 -0.017
## Cllct_S.:C2 -0.003 -0.010 -0.001 -0.011 -0.004 0.000 0.023 0.034
## Cllct_S.:C3 -0.010 0.012 0.014 -0.004 0.001 0.010 0.009 -0.092 0.035
## Cllct_S.:C4 -0.007 -0.035 0.000 0.000 0.010 0.040 0.009 -0.021 0.051
## Cllct_S.:C5 -0.014 -0.025 -0.018 0.022 0.008 0.009 0.017 0.049 -0.004
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.029
## Cllct_S.:C5 0.061 0.080tab_model(modA.8951,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.52 | 0.75 | 55.04 – 57.99 | 75.19 | <0.001 |
| Collectivism Score c | 0.30 | 0.04 | 0.23 – 0.37 | 8.20 | <0.001 |
| C1 | -6.53 | 0.80 | -8.10 – -4.96 | -8.15 | <0.001 |
| C2 | -6.23 | 1.22 | -8.63 – -3.83 | -5.09 | <0.001 |
| C3 | 5.04 | 1.27 | 2.55 – 7.54 | 3.96 | <0.001 |
| C4 | 0.34 | 1.41 | -2.43 – 3.10 | 0.24 | 0.811 |
| C5 | -5.21 | 1.43 | -8.02 – -2.40 | -3.64 | <0.001 |
| Collectivism Score c * C1 | -0.13 | 0.04 | -0.21 – -0.06 | -3.40 | 0.001 |
| Collectivism Score c * C2 | -0.07 | 0.06 | -0.19 – 0.05 | -1.10 | 0.272 |
| Collectivism Score c * C3 | 0.01 | 0.06 | -0.11 – 0.14 | 0.17 | 0.864 |
| Collectivism Score c * C4 | 0.13 | 0.07 | 0.00 – 0.27 | 1.97 | 0.049 |
| Collectivism Score c * C5 | -0.08 | 0.07 | -0.22 – 0.06 | -1.17 | 0.242 |
| Random Effects | |||||
| σ2 | 428.52 | ||||
| τ00 id | 414.67 | ||||
| ICC | 0.49 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.526 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.89516 <- lmer(Behav ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 +(1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89516)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25094.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1037 -0.4242 0.0598 0.4864 3.2406
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 365.9 19.13
## Residual 408.2 20.21
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.486e+01 7.430e-01 1.096e+03 73.831
## Collectivism_Score.c 2.966e-01 3.641e-02 1.101e+03 8.147
## Naturalness.c 3.999e-01 2.156e-02 2.163e+03 18.553
## C1 -3.109e+00 9.055e-01 1.953e+03 -3.434
## C2 7.186e-01 1.255e+00 1.994e+03 0.572
## C3 7.000e+00 1.397e+00 2.078e+03 5.009
## C4 5.611e+00 1.897e+00 2.106e+03 2.958
## C5 -1.294e+01 1.462e+00 2.010e+03 -8.852
## Collectivism_Score.c:Naturalness.c -8.183e-04 9.234e-04 2.096e+03 -0.886
## Collectivism_Score.c:C1 -1.648e-01 4.480e-02 1.932e+03 -3.680
## Collectivism_Score.c:C2 -1.087e-02 6.291e-02 1.988e+03 -0.173
## Collectivism_Score.c:C3 8.588e-02 7.032e-02 2.111e+03 1.221
## Collectivism_Score.c:C4 1.741e-01 9.336e-02 2.085e+03 1.864
## Collectivism_Score.c:C5 -1.602e-01 7.084e-02 1.994e+03 -2.261
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 1.01e-15 ***
## Naturalness.c < 2e-16 ***
## C1 0.000608 ***
## C2 0.567117
## C3 5.94e-07 ***
## C4 0.003130 **
## C5 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.375603
## Collectivism_Score.c:C1 0.000240 ***
## Collectivism_Score.c:C2 0.862849
## Collectivism_Score.c:C3 0.222132
## Collectivism_Score.c:C4 0.062404 .
## Collectivism_Score.c:C5 0.023865 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89516,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.86 | 0.74 | 53.40 – 56.32 | 73.83 | <0.001 |
| Collectivism Score c | 0.30 | 0.04 | 0.23 – 0.37 | 8.15 | <0.001 |
| Naturalness c | 0.40 | 0.02 | 0.36 – 0.44 | 18.55 | <0.001 |
| C1 | -3.11 | 0.91 | -4.88 – -1.33 | -3.43 | 0.001 |
| C2 | 0.72 | 1.26 | -1.74 – 3.18 | 0.57 | 0.567 |
| C3 | 7.00 | 1.40 | 4.26 – 9.74 | 5.01 | <0.001 |
| C4 | 5.61 | 1.90 | 1.89 – 9.33 | 2.96 | 0.003 |
| C5 | -12.94 | 1.46 | -15.81 – -10.08 | -8.85 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.89 | 0.376 |
| Collectivism Score c * C1 | -0.16 | 0.04 | -0.25 – -0.08 | -3.68 | <0.001 |
| Collectivism Score c * C2 | -0.01 | 0.06 | -0.13 – 0.11 | -0.17 | 0.863 |
| Collectivism Score c * C3 | 0.09 | 0.07 | -0.05 – 0.22 | 1.22 | 0.222 |
| Collectivism Score c * C4 | 0.17 | 0.09 | -0.01 – 0.36 | 1.86 | 0.062 |
| Collectivism Score c * C5 | -0.16 | 0.07 | -0.30 – -0.02 | -2.26 | 0.024 |
| Random Effects | |||||
| σ2 | 408.24 | ||||
| τ00 id | 365.86 | ||||
| ICC | 0.47 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.145 / 0.549 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict support, over and above burger contrasts?
modA.8941 <- lmer(Behav ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.8941)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Behav ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c *
## C1 + Individualism_Score.c * C2 + Individualism_Score.c *
## C3 + Individualism_Score.c * C4 + Individualism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28131
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6014 -0.4157 0.0604 0.4267 3.2460
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 436 20.88
## Residual 431 20.76
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.50183 0.76667 998.92178 73.697 < 2e-16 ***
## Individualism_Score.c 0.19797 0.04281 1008.77202 4.624 4.25e-06 ***
## C1 -6.48859 0.80431 2198.40643 -8.067 1.17e-15 ***
## C2 -6.26771 1.22872 2289.44096 -5.101 3.66e-07 ***
## C3 5.26494 1.27778 2340.78004 4.120 3.91e-05 ***
## C4 0.22734 1.41664 2291.10346 0.160 0.872520
## C5 -5.06142 1.43891 2314.37646 -3.518 0.000444 ***
## Individualism_Score.c:C1 -0.13079 0.04501 2190.17353 -2.906 0.003701 **
## Individualism_Score.c:C2 0.03641 0.06854 2320.41194 0.531 0.595326
## Individualism_Score.c:C3 0.12591 0.07295 2365.50819 1.726 0.084483 .
## Individualism_Score.c:C4 0.10737 0.07918 2294.87325 1.356 0.175212
## Individualism_Score.c:C5 -0.09453 0.08134 2317.00765 -1.162 0.245299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. 0.003
## C1 -0.002 -0.001
## C2 -0.020 0.004 0.005
## C3 -0.008 0.002 -0.056 0.027
## C4 -0.018 -0.010 -0.063 0.051 0.000
## C5 -0.014 -0.013 0.066 -0.029 0.064 0.073
## Indvd_S.:C1 -0.002 -0.008 0.008 0.014 -0.010 0.011 -0.021
## Indvd_S.:C2 0.004 -0.036 0.013 0.002 -0.008 0.000 0.022 -0.014
## Indvd_S.:C3 0.002 0.000 -0.010 -0.009 0.009 0.019 0.005 -0.072 0.040
## Indvd_S.:C4 -0.010 -0.035 0.011 0.000 0.018 0.007 0.006 -0.027 0.066
## Indvd_S.:C5 -0.013 -0.023 -0.021 0.022 0.004 0.007 -0.004 0.056 -0.003
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 0.027
## Indvd_S.:C5 0.064 0.079tab_model(modA.8941,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.50 | 0.77 | 55.00 – 58.01 | 73.70 | <0.001 |
| Individualism Score c | 0.20 | 0.04 | 0.11 – 0.28 | 4.62 | <0.001 |
| C1 | -6.49 | 0.80 | -8.07 – -4.91 | -8.07 | <0.001 |
| C2 | -6.27 | 1.23 | -8.68 – -3.86 | -5.10 | <0.001 |
| C3 | 5.26 | 1.28 | 2.76 – 7.77 | 4.12 | <0.001 |
| C4 | 0.23 | 1.42 | -2.55 – 3.01 | 0.16 | 0.873 |
| C5 | -5.06 | 1.44 | -7.88 – -2.24 | -3.52 | <0.001 |
|
Individualism Score c * C1 |
-0.13 | 0.05 | -0.22 – -0.04 | -2.91 | 0.004 |
|
Individualism Score c * C2 |
0.04 | 0.07 | -0.10 – 0.17 | 0.53 | 0.595 |
|
Individualism Score c * C3 |
0.13 | 0.07 | -0.02 – 0.27 | 1.73 | 0.084 |
|
Individualism Score c * C4 |
0.11 | 0.08 | -0.05 – 0.26 | 1.36 | 0.175 |
|
Individualism Score c * C5 |
-0.09 | 0.08 | -0.25 – 0.06 | -1.16 | 0.245 |
| Random Effects | |||||
| σ2 | 431.03 | ||||
| τ00 id | 435.96 | ||||
| ICC | 0.50 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.038 / 0.522 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.89417 <- lmer(Behav ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.89417)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25126.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6047 -0.4307 0.0578 0.4914 2.9939
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 389.2 19.73
## Residual 408.3 20.21
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.483e+01 7.594e-01 1.092e+03 72.206
## Individualism_Score.c 1.518e-01 4.242e-02 1.102e+03 3.578
## Naturalness.c 3.895e-01 2.281e-02 2.202e+03 17.074
## C1 -2.971e+00 9.071e-01 1.941e+03 -3.276
## C2 8.220e-01 1.259e+00 1.980e+03 0.653
## C3 7.418e+00 1.402e+00 2.063e+03 5.291
## C4 5.809e+00 1.905e+00 2.090e+03 3.050
## C5 -1.295e+01 1.465e+00 1.995e+03 -8.842
## Individualism_Score.c:Naturalness.c 2.177e-03 1.150e-03 2.237e+03 1.893
## Individualism_Score.c:C1 -1.437e-02 5.051e-02 1.946e+03 -0.284
## Individualism_Score.c:C2 2.005e-01 6.895e-02 1.987e+03 2.908
## Individualism_Score.c:C3 2.274e-01 7.948e-02 2.085e+03 2.861
## Individualism_Score.c:C4 3.708e-01 1.056e-01 2.088e+03 3.513
## Individualism_Score.c:C5 -2.755e-01 8.109e-02 1.975e+03 -3.397
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.000361 ***
## Naturalness.c < 2e-16 ***
## C1 0.001073 **
## C2 0.513829
## C3 1.34e-07 ***
## C4 0.002321 **
## C5 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.058513 .
## Individualism_Score.c:C1 0.776131
## Individualism_Score.c:C2 0.003673 **
## Individualism_Score.c:C3 0.004262 **
## Individualism_Score.c:C4 0.000453 ***
## Individualism_Score.c:C5 0.000696 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.89417,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.83 | 0.76 | 53.34 – 56.32 | 72.21 | <0.001 |
| Individualism Score c | 0.15 | 0.04 | 0.07 – 0.23 | 3.58 | <0.001 |
| Naturalness c | 0.39 | 0.02 | 0.34 – 0.43 | 17.07 | <0.001 |
| C1 | -2.97 | 0.91 | -4.75 – -1.19 | -3.28 | 0.001 |
| C2 | 0.82 | 1.26 | -1.65 – 3.29 | 0.65 | 0.514 |
| C3 | 7.42 | 1.40 | 4.67 – 10.17 | 5.29 | <0.001 |
| C4 | 5.81 | 1.90 | 2.07 – 9.54 | 3.05 | 0.002 |
| C5 | -12.95 | 1.46 | -15.82 – -10.08 | -8.84 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.89 | 0.058 |
|
Individualism Score c * C1 |
-0.01 | 0.05 | -0.11 – 0.08 | -0.28 | 0.776 |
|
Individualism Score c * C2 |
0.20 | 0.07 | 0.07 – 0.34 | 2.91 | 0.004 |
|
Individualism Score c * C3 |
0.23 | 0.08 | 0.07 – 0.38 | 2.86 | 0.004 |
|
Individualism Score c * C4 |
0.37 | 0.11 | 0.16 – 0.58 | 3.51 | <0.001 |
|
Individualism Score c * C5 |
-0.28 | 0.08 | -0.43 – -0.12 | -3.40 | 0.001 |
| Random Effects | |||||
| σ2 | 408.25 | ||||
| τ00 id | 389.23 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.118 / 0.548 | ||||
Political Ideology
Q.1 (Ideology) How does ideology predict support, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8931 <- lmer(Behav ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.8931)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c * C1 +
## Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 + Ideology.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28140.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6265 -0.4199 0.0565 0.4312 3.3608
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 447.5 21.15
## Residual 432.0 20.78
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.7566 1.1382 998.8284 48.108 < 2e-16 ***
## Ideology.c -1.4829 0.7109 992.4019 -2.086 0.037238 *
## C1 -6.7146 1.1822 2190.4367 -5.680 1.53e-08 ***
## C2 -6.8995 1.7657 2261.4164 -3.908 9.60e-05 ***
## C3 5.8702 1.9157 2360.6238 3.064 0.002207 **
## C4 -2.0718 2.1048 2291.9477 -0.984 0.325065
## C5 -6.9879 2.1202 2321.3533 -3.296 0.000996 ***
## Ideology.c:C1 -0.2251 0.7386 2198.5176 -0.305 0.760531
## Ideology.c:C2 -0.6187 1.1112 2257.3697 -0.557 0.577767
## Ideology.c:C3 0.5754 1.2069 2366.4176 0.477 0.633577
## Ideology.c:C4 -1.9463 1.2800 2266.6762 -1.521 0.128490
## Ideology.c:C5 -1.6006 1.3156 2312.7229 -1.217 0.223895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.733
## C1 -0.011 -0.010
## C2 -0.026 -0.019 -0.013
## C3 -0.007 0.001 -0.063 0.029
## C4 -0.014 -0.006 -0.073 0.045 0.007
## C5 -0.017 -0.006 0.050 -0.025 0.077 0.080
## Idelgy.c:C1 -0.010 -0.008 0.732 -0.011 -0.056 -0.059 0.040
## Idelgy.c:C2 -0.018 -0.019 -0.011 0.717 0.020 0.029 -0.025 0.001
## Idelgy.c:C3 0.000 0.008 -0.055 0.021 0.744 0.006 0.064 -0.083 0.025
## Idelgy.c:C4 -0.007 -0.005 -0.060 0.030 0.006 0.739 0.056 -0.082 0.042
## Idelgy.c:C5 -0.007 0.002 0.040 -0.025 0.065 0.055 0.734 0.074 -0.048
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.004
## Idelgy.c:C5 0.088 0.065tab_model(modA.8931,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 54.76 | 1.14 | 52.52 – 56.99 | 48.11 | <0.001 |
| Ideology c | -1.48 | 0.71 | -2.88 – -0.09 | -2.09 | 0.037 |
| C1 | -6.71 | 1.18 | -9.03 – -4.40 | -5.68 | <0.001 |
| C2 | -6.90 | 1.77 | -10.36 – -3.44 | -3.91 | <0.001 |
| C3 | 5.87 | 1.92 | 2.11 – 9.63 | 3.06 | 0.002 |
| C4 | -2.07 | 2.10 | -6.20 – 2.06 | -0.98 | 0.325 |
| C5 | -6.99 | 2.12 | -11.15 – -2.83 | -3.30 | 0.001 |
| Ideology c * C1 | -0.23 | 0.74 | -1.67 – 1.22 | -0.30 | 0.761 |
| Ideology c * C2 | -0.62 | 1.11 | -2.80 – 1.56 | -0.56 | 0.578 |
| Ideology c * C3 | 0.58 | 1.21 | -1.79 – 2.94 | 0.48 | 0.634 |
| Ideology c * C4 | -1.95 | 1.28 | -4.46 – 0.56 | -1.52 | 0.128 |
| Ideology c * C5 | -1.60 | 1.32 | -4.18 – 0.98 | -1.22 | 0.224 |
| Random Effects | |||||
| σ2 | 431.97 | ||||
| τ00 id | 447.51 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.026 / 0.521 | ||||
Q.2 (Ideology) Does ideology depend on perceptions of naturalness in predicting support, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.89317 <- lmer(Behav ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.89317)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Behav ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25142.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7800 -0.4433 0.0485 0.4906 3.1291
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 398.5 19.96
## Residual 414.2 20.35
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.57968 1.12541 1083.02344 46.720 < 2e-16 ***
## Ideology.c -1.93546 0.69882 1055.81089 -2.770 0.00571 **
## Naturalness.c 0.40296 0.03157 2135.82332 12.764 < 2e-16 ***
## C1 -2.00928 1.32707 1925.97722 -1.514 0.13017
## C2 0.36414 1.81278 1964.61048 0.201 0.84082
## C3 8.53962 2.09465 2078.07832 4.077 4.74e-05 ***
## C4 5.03251 2.81187 2098.19979 1.790 0.07364 .
## C5 -14.28212 2.15750 1996.79917 -6.620 4.61e-11 ***
## Ideology.c:Naturalness.c 0.01207 0.02211 2131.96046 0.546 0.58517
## Ideology.c:C1 0.95706 0.80954 1913.60990 1.182 0.23726
## Ideology.c:C2 -0.27343 1.14477 1957.53397 -0.239 0.81125
## Ideology.c:C3 1.12498 1.31132 2089.30297 0.858 0.39105
## Ideology.c:C4 -0.33589 1.70259 2072.75754 -0.197 0.84363
## Ideology.c:C5 -1.32200 1.36668 1996.61648 -0.967 0.33351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.89317,
show.stat = T, show.se = T)| Behav | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 52.58 | 1.13 | 50.37 – 54.79 | 46.72 | <0.001 |
| Ideology c | -1.94 | 0.70 | -3.31 – -0.57 | -2.77 | 0.006 |
| Naturalness c | 0.40 | 0.03 | 0.34 – 0.46 | 12.76 | <0.001 |
| C1 | -2.01 | 1.33 | -4.61 – 0.59 | -1.51 | 0.130 |
| C2 | 0.36 | 1.81 | -3.19 – 3.92 | 0.20 | 0.841 |
| C3 | 8.54 | 2.09 | 4.43 – 12.65 | 4.08 | <0.001 |
| C4 | 5.03 | 2.81 | -0.48 – 10.55 | 1.79 | 0.074 |
| C5 | -14.28 | 2.16 | -18.51 – -10.05 | -6.62 | <0.001 |
|
Ideology c * Naturalness c |
0.01 | 0.02 | -0.03 – 0.06 | 0.55 | 0.585 |
| Ideology c * C1 | 0.96 | 0.81 | -0.63 – 2.54 | 1.18 | 0.237 |
| Ideology c * C2 | -0.27 | 1.14 | -2.52 – 1.97 | -0.24 | 0.811 |
| Ideology c * C3 | 1.12 | 1.31 | -1.45 – 3.70 | 0.86 | 0.391 |
| Ideology c * C4 | -0.34 | 1.70 | -3.67 – 3.00 | -0.20 | 0.844 |
| Ideology c * C5 | -1.32 | 1.37 | -4.00 – 1.36 | -0.97 | 0.333 |
| Random Effects | |||||
| σ2 | 414.25 | ||||
| τ00 id | 398.51 | ||||
| ICC | 0.49 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.102 / 0.542 | ||||
Naturalness
Q.1 How do burger contrasts predict naturalness perception?
modA.89 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.89)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26923.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.92273 -0.65184 -0.03425 0.61938 3.08384
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.535 1.239
## Residual 455.241 21.336
## Number of obs: 3006, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.3942 0.3914 1002.1123 128.753 < 2e-16 ***
## C1 -8.1457 0.7797 2793.3500 -10.447 < 2e-16 ***
## C2 -17.9940 1.1570 2769.4763 -15.552 < 2e-16 ***
## C3 -4.6214 1.1903 2759.6843 -3.883 0.000106 ***
## C4 -12.9528 1.3423 2791.1878 -9.650 < 2e-16 ***
## C5 19.8994 1.3486 2753.0711 14.755 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.012
## C2 -0.009 -0.009
## C3 0.023 -0.024 0.000
## C4 0.008 -0.008 0.000 -0.008
## C5 0.025 0.025 -0.025 0.000 0.000tab_model(modA.89,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.63 – 51.16 | 128.75 | <0.001 |
| C1 | -8.15 | 0.78 | -9.67 – -6.62 | -10.45 | <0.001 |
| C2 | -17.99 | 1.16 | -20.26 – -15.73 | -15.55 | <0.001 |
| C3 | -4.62 | 1.19 | -6.96 – -2.29 | -3.88 | <0.001 |
| C4 | -12.95 | 1.34 | -15.58 – -10.32 | -9.65 | <0.001 |
| C5 | 19.90 | 1.35 | 17.26 – 22.54 | 14.76 | <0.001 |
| Random Effects | |||||
| σ2 | 455.24 | ||||
| τ00 id | 1.54 | ||||
| ICC | 0.00 | ||||
| N id | 1004 | ||||
| Observations | 3006 | ||||
| Marginal R2 / Conditional R2 | 0.185 / 0.187 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict naturalness perception, over and above burger contrasts?
modA.890 <- lmer(Naturalness ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.890)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c *
## C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c *
## C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26857.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1995 -0.6522 -0.0341 0.6125 3.6125
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.403 2.098
## Residual 445.114 21.098
## Number of obs: 3002, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.33131 0.39130 998.25511 128.626 < 2e-16 ***
## ATNS_Score.c -0.01779 0.02257 1001.29919 -0.788 0.431
## C1 -8.32172 0.77358 2773.18885 -10.757 < 2e-16 ***
## C2 -17.89675 1.14808 2753.41758 -15.588 < 2e-16 ***
## C3 -4.70907 1.18036 2741.59057 -3.990 6.79e-05 ***
## C4 -13.00438 1.33094 2771.51571 -9.771 < 2e-16 ***
## C5 19.66010 1.33907 2732.81983 14.682 < 2e-16 ***
## ATNS_Score.c:C1 -0.07218 0.04461 2762.90363 -1.618 0.106
## ATNS_Score.c:C2 -0.39061 0.06730 2762.89715 -5.804 7.22e-09 ***
## ATNS_Score.c:C3 0.04072 0.06835 2717.36222 0.596 0.551
## ATNS_Score.c:C4 -0.12212 0.07530 2784.14569 -1.622 0.105
## ATNS_Score.c:C5 0.32453 0.07707 2751.96348 4.211 2.63e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c -0.002
## C1 -0.011 -0.002
## C2 -0.010 0.013 -0.010
## C3 0.023 -0.005 -0.024 0.000
## C4 0.008 -0.014 -0.008 0.000 -0.008
## C5 0.026 -0.033 0.026 -0.027 0.000 0.000
## ATNS_Sc.:C1 -0.002 0.003 -0.002 0.013 0.005 0.014 -0.033
## ATNS_Sc.:C2 0.013 0.008 0.013 0.008 0.000 0.000 0.033 0.008
## ATNS_Sc.:C3 -0.005 0.046 0.005 0.000 -0.005 0.014 0.000 -0.047
## ATNS_Sc.:C4 -0.014 0.030 0.014 0.000 0.014 0.005 0.000 -0.030
## ATNS_Sc.:C5 -0.033 0.013 -0.033 0.034 0.000 0.000 -0.017 0.013
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.000
## ATNS_Sc.:C4 0.000 -0.030
## ATNS_Sc.:C5 -0.013 0.001 0.000tab_model(modA.890,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.33 | 0.39 | 49.56 – 51.10 | 128.63 | <0.001 |
| ATNS Score c | -0.02 | 0.02 | -0.06 – 0.03 | -0.79 | 0.431 |
| C1 | -8.32 | 0.77 | -9.84 – -6.80 | -10.76 | <0.001 |
| C2 | -17.90 | 1.15 | -20.15 – -15.65 | -15.59 | <0.001 |
| C3 | -4.71 | 1.18 | -7.02 – -2.39 | -3.99 | <0.001 |
| C4 | -13.00 | 1.33 | -15.61 – -10.39 | -9.77 | <0.001 |
| C5 | 19.66 | 1.34 | 17.03 – 22.29 | 14.68 | <0.001 |
| ATNS Score c * C1 | -0.07 | 0.04 | -0.16 – 0.02 | -1.62 | 0.106 |
| ATNS Score c * C2 | -0.39 | 0.07 | -0.52 – -0.26 | -5.80 | <0.001 |
| ATNS Score c * C3 | 0.04 | 0.07 | -0.09 – 0.17 | 0.60 | 0.551 |
| ATNS Score c * C4 | -0.12 | 0.08 | -0.27 – 0.03 | -1.62 | 0.105 |
| ATNS Score c * C5 | 0.32 | 0.08 | 0.17 – 0.48 | 4.21 | <0.001 |
| Random Effects | |||||
| σ2 | 445.11 | ||||
| τ00 id | 4.40 | ||||
| ICC | 0.01 | ||||
| N id | 1002 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.199 / 0.207 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict naturalness perception, over and above burger contrasts?
modA.899 <- lmer(Naturalness ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.899)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c *
## C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 +
## AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26856
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1871 -0.6622 -0.0296 0.6232 3.2569
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 3.303 1.817
## Residual 448.082 21.168
## Number of obs: 3000, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.43745 0.39132 998.60849 128.890 < 2e-16 ***
## AW_Score.c 0.01939 0.01597 1004.09550 1.214 0.225066
## C1 -8.17953 0.77589 2773.07299 -10.542 < 2e-16 ***
## C2 -18.06113 1.15101 2753.79740 -15.692 < 2e-16 ***
## C3 -4.76316 1.18388 2745.33000 -4.023 5.89e-05 ***
## C4 -12.80658 1.33600 2773.38119 -9.586 < 2e-16 ***
## C5 19.80282 1.34272 2737.58717 14.748 < 2e-16 ***
## AW_Score.c:C1 0.06605 0.03166 2732.06163 2.086 0.037075 *
## AW_Score.c:C2 -0.23110 0.04722 2782.66706 -4.894 1.04e-06 ***
## AW_Score.c:C3 0.16482 0.04864 2771.78461 3.389 0.000712 ***
## AW_Score.c:C4 -0.05033 0.05421 2773.25186 -0.928 0.353298
## AW_Score.c:C5 0.10577 0.05448 2730.20990 1.941 0.052323 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c -0.003
## C1 -0.012 0.023
## C2 -0.010 -0.002 -0.010
## C3 0.023 0.010 -0.023 0.000
## C4 0.010 -0.025 -0.010 0.000 -0.010
## C5 0.024 -0.019 0.024 -0.025 0.000 0.000
## AW_Scr.c:C1 0.023 -0.012 -0.003 -0.002 -0.011 0.026 -0.019
## AW_Scr.c:C2 -0.002 0.001 -0.002 0.018 0.000 0.000 0.020 0.000
## AW_Scr.c:C3 0.010 0.035 -0.011 0.000 -0.014 0.025 0.000 -0.035 0.000
## AW_Scr.c:C4 -0.026 0.049 0.026 0.000 0.026 -0.037 0.000 -0.049 0.000
## AW_Scr.c:C5 -0.019 0.023 -0.020 0.020 0.000 0.000 0.022 0.023 -0.023
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 -0.049
## AW_Scr.c:C5 0.000 0.000tab_model(modA.899,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.44 | 0.39 | 49.67 – 51.20 | 128.89 | <0.001 |
| AW Score c | 0.02 | 0.02 | -0.01 – 0.05 | 1.21 | 0.225 |
| C1 | -8.18 | 0.78 | -9.70 – -6.66 | -10.54 | <0.001 |
| C2 | -18.06 | 1.15 | -20.32 – -15.80 | -15.69 | <0.001 |
| C3 | -4.76 | 1.18 | -7.08 – -2.44 | -4.02 | <0.001 |
| C4 | -12.81 | 1.34 | -15.43 – -10.19 | -9.59 | <0.001 |
| C5 | 19.80 | 1.34 | 17.17 – 22.44 | 14.75 | <0.001 |
| AW Score c * C1 | 0.07 | 0.03 | 0.00 – 0.13 | 2.09 | 0.037 |
| AW Score c * C2 | -0.23 | 0.05 | -0.32 – -0.14 | -4.89 | <0.001 |
| AW Score c * C3 | 0.16 | 0.05 | 0.07 – 0.26 | 3.39 | 0.001 |
| AW Score c * C4 | -0.05 | 0.05 | -0.16 – 0.06 | -0.93 | 0.353 |
| AW Score c * C5 | 0.11 | 0.05 | -0.00 – 0.21 | 1.94 | 0.052 |
| Random Effects | |||||
| σ2 | 448.08 | ||||
| τ00 id | 3.30 | ||||
| ICC | 0.01 | ||||
| N id | 1001 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.197 / 0.202 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict naturalness perception, over and above burger contrasts?
modA.897 <- lmer(Naturalness ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.897)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c *
## C1 + CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c *
## C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26885
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0451 -0.6427 -0.0312 0.6238 3.3329
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.833 1.354
## Residual 452.458 21.271
## Number of obs: 3002, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.38745 0.39107 999.50938 128.846 < 2e-16 ***
## CNS_Score.c 0.07144 0.03148 1000.92221 2.270 0.02344 *
## C1 -8.20230 0.77841 2781.82071 -10.537 < 2e-16 ***
## C2 -18.09783 1.15623 2760.83836 -15.652 < 2e-16 ***
## C3 -4.67651 1.18807 2750.67831 -3.936 8.48e-05 ***
## C4 -13.03846 1.33961 2780.28732 -9.733 < 2e-16 ***
## C5 19.92231 1.34583 2743.17199 14.803 < 2e-16 ***
## CNS_Score.c:C1 -0.16973 0.06265 2755.93851 -2.709 0.00678 **
## CNS_Score.c:C2 -0.22541 0.09566 2769.86830 -2.356 0.01852 *
## CNS_Score.c:C3 0.02290 0.09559 2746.71669 0.240 0.81069
## CNS_Score.c:C4 -0.28034 0.10758 2796.20417 -2.606 0.00921 **
## CNS_Score.c:C5 0.06294 0.10556 2749.50499 0.596 0.55107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c 0.001
## C1 -0.011 0.009
## C2 -0.008 0.027 -0.008
## C3 0.024 0.006 -0.024 0.000
## C4 0.008 -0.014 -0.008 0.000 -0.008
## C5 0.025 0.009 0.025 -0.025 0.000 0.000
## CNS_Scr.:C1 0.009 -0.009 0.001 0.027 -0.006 0.014 0.009
## CNS_Scr.:C2 0.026 0.044 0.026 0.036 0.000 0.000 -0.008 0.044
## CNS_Scr.:C3 0.006 0.025 -0.006 0.000 -0.002 0.014 0.000 -0.026
## CNS_Scr.:C4 -0.014 -0.017 0.014 0.000 0.014 -0.014 0.000 0.017
## CNS_Scr.:C5 0.009 -0.014 0.009 -0.009 0.000 0.000 -0.018 -0.014
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 0.000
## CNS_Scr.:C4 0.000 0.017
## CNS_Scr.:C5 0.013 0.000 0.000tab_model(modA.897,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.62 – 51.15 | 128.85 | <0.001 |
| CNS Score c | 0.07 | 0.03 | 0.01 – 0.13 | 2.27 | 0.023 |
| C1 | -8.20 | 0.78 | -9.73 – -6.68 | -10.54 | <0.001 |
| C2 | -18.10 | 1.16 | -20.36 – -15.83 | -15.65 | <0.001 |
| C3 | -4.68 | 1.19 | -7.01 – -2.35 | -3.94 | <0.001 |
| C4 | -13.04 | 1.34 | -15.67 – -10.41 | -9.73 | <0.001 |
| C5 | 19.92 | 1.35 | 17.28 – 22.56 | 14.80 | <0.001 |
| CNS Score c * C1 | -0.17 | 0.06 | -0.29 – -0.05 | -2.71 | 0.007 |
| CNS Score c * C2 | -0.23 | 0.10 | -0.41 – -0.04 | -2.36 | 0.019 |
| CNS Score c * C3 | 0.02 | 0.10 | -0.16 – 0.21 | 0.24 | 0.811 |
| CNS Score c * C4 | -0.28 | 0.11 | -0.49 – -0.07 | -2.61 | 0.009 |
| CNS Score c * C5 | 0.06 | 0.11 | -0.14 – 0.27 | 0.60 | 0.551 |
| Random Effects | |||||
| σ2 | 452.46 | ||||
| τ00 id | 1.83 | ||||
| ICC | 0.00 | ||||
| N id | 1002 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.191 / 0.194 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict naturalness perception, over and above burger contrasts?
modA.896 <- lmer(Naturalness ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.896)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Naturalness ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26829.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2004 -0.6525 -0.0308 0.6131 3.2759
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.258 2.064
## Residual 444.592 21.085
## Number of obs: 2999, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.37258 0.39094 995.75207 128.850 < 2e-16 ***
## CCBelief_Score.c 0.02024 0.01680 999.39669 1.204 0.228743
## C1 -8.15242 0.77315 2770.22683 -10.544 < 2e-16 ***
## C2 -17.94346 1.14769 2749.23969 -15.634 < 2e-16 ***
## C3 -4.66527 1.17975 2738.49445 -3.954 7.87e-05 ***
## C4 -12.96146 1.33079 2769.03138 -9.740 < 2e-16 ***
## C5 19.82925 1.33737 2731.49366 14.827 < 2e-16 ***
## CCBelief_Score.c:C1 0.02082 0.03322 2692.55406 0.627 0.530810
## CCBelief_Score.c:C2 -0.25528 0.05005 2773.18468 -5.100 3.62e-07 ***
## CCBelief_Score.c:C3 0.17890 0.05139 2768.79267 3.481 0.000506 ***
## CCBelief_Score.c:C4 -0.07192 0.05676 2786.37665 -1.267 0.205273
## CCBelief_Score.c:C5 0.23548 0.05627 2738.68012 4.184 2.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c -0.002
## C1 -0.011 0.012
## C2 -0.009 -0.012 -0.009
## C3 0.023 0.003 -0.024 0.000
## C4 0.007 0.013 -0.007 0.000 -0.007
## C5 0.025 -0.014 0.025 -0.026 0.000 0.000
## CCBlf_S.:C1 0.012 -0.018 -0.002 -0.012 -0.003 -0.014 -0.014
## CCBlf_S.:C2 -0.012 0.026 -0.012 -0.002 0.000 0.000 0.014 0.026
## CCBlf_S.:C3 0.003 0.043 -0.003 0.000 -0.010 -0.013 0.000 -0.044
## CCBlf_S.:C4 0.013 0.002 -0.014 0.000 -0.013 -0.017 0.000 -0.002
## CCBlf_S.:C5 -0.014 0.028 -0.015 0.015 0.000 0.000 0.024 0.028
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 0.000
## CCBlf_S.:C4 0.000 -0.002
## CCBlf_S.:C5 -0.028 0.000 0.000tab_model(modA.896,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.37 | 0.39 | 49.61 – 51.14 | 128.85 | <0.001 |
| CCBelief Score c | 0.02 | 0.02 | -0.01 – 0.05 | 1.20 | 0.229 |
| C1 | -8.15 | 0.77 | -9.67 – -6.64 | -10.54 | <0.001 |
| C2 | -17.94 | 1.15 | -20.19 – -15.69 | -15.63 | <0.001 |
| C3 | -4.67 | 1.18 | -6.98 – -2.35 | -3.95 | <0.001 |
| C4 | -12.96 | 1.33 | -15.57 – -10.35 | -9.74 | <0.001 |
| C5 | 19.83 | 1.34 | 17.21 – 22.45 | 14.83 | <0.001 |
| CCBelief Score c * C1 | 0.02 | 0.03 | -0.04 – 0.09 | 0.63 | 0.531 |
| CCBelief Score c * C2 | -0.26 | 0.05 | -0.35 – -0.16 | -5.10 | <0.001 |
| CCBelief Score c * C3 | 0.18 | 0.05 | 0.08 – 0.28 | 3.48 | 0.001 |
| CCBelief Score c * C4 | -0.07 | 0.06 | -0.18 – 0.04 | -1.27 | 0.205 |
| CCBelief Score c * C5 | 0.24 | 0.06 | 0.13 – 0.35 | 4.18 | <0.001 |
| Random Effects | |||||
| σ2 | 444.59 | ||||
| τ00 id | 4.26 | ||||
| ICC | 0.01 | ||||
| N id | 1001 | ||||
| Observations | 2999 | ||||
| Marginal R2 / Conditional R2 | 0.199 / 0.207 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict naturalness perception, over and above burger contrasts?
modA.8969 <- lmer(Naturalness ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.8969)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c *
## C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 +
## DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26885.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0754 -0.6627 -0.0502 0.6241 3.2119
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 2.41 1.553
## Residual 453.74 21.301
## Number of obs: 3000, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.38783 0.39258 995.65857 128.351 < 2e-16 ***
## DS_Score.c -0.01247 0.01888 998.54859 -0.661 0.50901
## C1 -8.14844 0.78027 2778.90040 -10.443 < 2e-16 ***
## C2 -18.00958 1.15745 2754.85099 -15.560 < 2e-16 ***
## C3 -4.67038 1.19103 2745.09787 -3.921 9.02e-05 ***
## C4 -12.83318 1.34360 2776.13120 -9.551 < 2e-16 ***
## C5 19.88670 1.34977 2738.34829 14.733 < 2e-16 ***
## DS_Score.c:C1 0.05821 0.03752 2805.28164 1.551 0.12095
## DS_Score.c:C2 -0.14865 0.05517 2740.78403 -2.694 0.00709 **
## DS_Score.c:C3 0.09521 0.05717 2745.75457 1.665 0.09593 .
## DS_Score.c:C4 -0.01702 0.06435 2760.28013 -0.265 0.79139
## DS_Score.c:C5 0.03879 0.06582 2744.03539 0.589 0.55565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c -0.001
## C1 -0.012 0.002
## C2 -0.010 0.009 -0.010
## C3 0.023 -0.015 -0.023 0.000
## C4 0.007 -0.031 -0.007 0.000 -0.007
## C5 0.025 -0.016 0.026 -0.026 0.000 0.000
## DS_Scr.c:C1 0.002 -0.006 -0.001 0.010 0.015 0.031 -0.016
## DS_Scr.c:C2 0.010 -0.033 0.010 0.010 0.000 0.000 0.016 -0.033
## DS_Scr.c:C3 -0.015 0.025 0.015 0.000 -0.018 0.030 0.000 -0.025 0.000
## DS_Scr.c:C4 -0.031 0.027 0.031 0.000 0.030 0.012 0.000 -0.027 0.000
## DS_Scr.c:C5 -0.015 -0.010 -0.016 0.016 0.000 0.000 -0.009 -0.010 0.010
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 -0.027
## DS_Scr.c:C5 0.000 0.000tab_model(modA.8969,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.62 – 51.16 | 128.35 | <0.001 |
| DS Score c | -0.01 | 0.02 | -0.05 – 0.02 | -0.66 | 0.509 |
| C1 | -8.15 | 0.78 | -9.68 – -6.62 | -10.44 | <0.001 |
| C2 | -18.01 | 1.16 | -20.28 – -15.74 | -15.56 | <0.001 |
| C3 | -4.67 | 1.19 | -7.01 – -2.34 | -3.92 | <0.001 |
| C4 | -12.83 | 1.34 | -15.47 – -10.20 | -9.55 | <0.001 |
| C5 | 19.89 | 1.35 | 17.24 – 22.53 | 14.73 | <0.001 |
| DS Score c * C1 | 0.06 | 0.04 | -0.02 – 0.13 | 1.55 | 0.121 |
| DS Score c * C2 | -0.15 | 0.06 | -0.26 – -0.04 | -2.69 | 0.007 |
| DS Score c * C3 | 0.10 | 0.06 | -0.02 – 0.21 | 1.67 | 0.096 |
| DS Score c * C4 | -0.02 | 0.06 | -0.14 – 0.11 | -0.26 | 0.791 |
| DS Score c * C5 | 0.04 | 0.07 | -0.09 – 0.17 | 0.59 | 0.556 |
| Random Effects | |||||
| σ2 | 453.74 | ||||
| τ00 id | 2.41 | ||||
| ICC | 0.01 | ||||
| N id | 1001 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.188 / 0.192 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict naturalness perception, over and above burger contrasts?
modA.895 <- lmer(Naturalness ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)
summary(modA.895)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 +
## Collectivism_Score.c * C1 + Collectivism_Score.c * C2 + Collectivism_Score.c *
## C3 + Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26905.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2383 -0.6543 -0.0342 0.6329 3.1666
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 3.761 1.939
## Residual 450.042 21.214
## Number of obs: 3004, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.38536 0.39227 995.76105 128.447 < 2e-16 ***
## Collectivism_Score.c -0.01632 0.01922 999.70046 -0.849 0.395845
## C1 -8.19171 0.77687 2777.07887 -10.544 < 2e-16 ***
## C2 -17.89384 1.15328 2754.38657 -15.516 < 2e-16 ***
## C3 -4.60256 1.18532 2743.83589 -3.883 0.000106 ***
## C4 -12.97531 1.33682 2775.31622 -9.706 < 2e-16 ***
## C5 19.85849 1.34430 2737.00321 14.772 < 2e-16 ***
## Collectivism_Score.c:C1 0.08393 0.03805 2726.19845 2.206 0.027492 *
## Collectivism_Score.c:C2 -0.15334 0.05738 2789.04967 -2.672 0.007576 **
## Collectivism_Score.c:C3 -0.04488 0.05893 2780.75569 -0.762 0.446305
## Collectivism_Score.c:C4 0.03647 0.06389 2751.18258 0.571 0.568219
## Collectivism_Score.c:C5 0.24842 0.06543 2742.37142 3.797 0.000150 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. -0.001
## C1 -0.011 0.005
## C2 -0.009 -0.006 -0.010
## C3 0.023 -0.006 -0.024 0.000
## C4 0.008 0.010 -0.008 0.000 -0.009
## C5 0.025 -0.019 0.025 -0.025 0.000 0.000
## Cllct_S.:C1 0.005 -0.002 -0.001 -0.006 0.006 -0.011 -0.019
## Cllct_S.:C2 -0.006 0.012 -0.006 -0.001 0.000 0.000 0.019 0.012
## Cllct_S.:C3 -0.006 0.060 0.006 0.000 -0.011 -0.010 0.000 -0.061 0.000
## Cllct_S.:C4 0.011 -0.015 -0.011 0.000 -0.011 0.000 0.000 0.015 0.000
## Cllct_S.:C5 -0.019 0.006 -0.019 0.019 0.000 0.000 0.011 0.006 -0.006
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.014
## Cllct_S.:C5 0.000 0.000tab_model(modA.895,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.39 | 0.39 | 49.62 – 51.15 | 128.45 | <0.001 |
| Collectivism Score c | -0.02 | 0.02 | -0.05 – 0.02 | -0.85 | 0.396 |
| C1 | -8.19 | 0.78 | -9.71 – -6.67 | -10.54 | <0.001 |
| C2 | -17.89 | 1.15 | -20.16 – -15.63 | -15.52 | <0.001 |
| C3 | -4.60 | 1.19 | -6.93 – -2.28 | -3.88 | <0.001 |
| C4 | -12.98 | 1.34 | -15.60 – -10.35 | -9.71 | <0.001 |
| C5 | 19.86 | 1.34 | 17.22 – 22.49 | 14.77 | <0.001 |
| Collectivism Score c * C1 | 0.08 | 0.04 | 0.01 – 0.16 | 2.21 | 0.027 |
| Collectivism Score c * C2 | -0.15 | 0.06 | -0.27 – -0.04 | -2.67 | 0.008 |
| Collectivism Score c * C3 | -0.04 | 0.06 | -0.16 – 0.07 | -0.76 | 0.446 |
| Collectivism Score c * C4 | 0.04 | 0.06 | -0.09 – 0.16 | 0.57 | 0.568 |
| Collectivism Score c * C5 | 0.25 | 0.07 | 0.12 – 0.38 | 3.80 | <0.001 |
| Random Effects | |||||
| σ2 | 450.04 | ||||
| τ00 id | 3.76 | ||||
| ICC | 0.01 | ||||
| N id | 1003 | ||||
| Observations | 3004 | ||||
| Marginal R2 / Conditional R2 | 0.191 / 0.198 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict naturalness perception, over and above burger contrasts?
modA.894 <- lmer(Naturalness ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.894)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 +
## Individualism_Score.c * C1 + Individualism_Score.c * C2 +
## Individualism_Score.c * C3 + Individualism_Score.c * C4 +
## Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26858.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2267 -0.6427 -0.0167 0.5960 3.3905
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 3.103 1.762
## Residual 446.538 21.131
## Number of obs: 3002, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.35893 0.39007 996.44065 129.103 < 2e-16 ***
## Individualism_Score.c 0.01028 0.02174 999.90428 0.473 0.63632
## C1 -8.24394 0.77379 2777.23055 -10.654 < 2e-16 ***
## C2 -17.94431 1.14879 2754.28984 -15.620 < 2e-16 ***
## C3 -4.61169 1.18021 2744.73516 -3.908 9.55e-05 ***
## C4 -12.96660 1.33107 2776.15874 -9.742 < 2e-16 ***
## C5 19.87013 1.33988 2738.46087 14.830 < 2e-16 ***
## Individualism_Score.c:C1 -0.11767 0.04313 2736.86808 -2.728 0.00641 **
## Individualism_Score.c:C2 -0.28616 0.06369 2782.00873 -4.493 7.31e-06 ***
## Individualism_Score.c:C3 0.02253 0.06652 2773.92022 0.339 0.73488
## Individualism_Score.c:C4 -0.11686 0.07271 2759.02190 -1.607 0.10813
## Individualism_Score.c:C5 0.38237 0.07569 2744.03991 5.052 4.66e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. 0.000
## C1 -0.010 0.001
## C2 -0.010 0.007 -0.010
## C3 0.023 0.007 -0.024 0.000
## C4 0.009 -0.010 -0.009 0.000 -0.009
## C5 0.025 -0.020 0.026 -0.026 0.000 0.000
## Indvd_S.:C1 0.001 -0.002 0.000 0.008 -0.008 0.010 -0.020
## Indvd_S.:C2 0.007 -0.029 0.008 0.008 0.000 0.000 0.020 -0.030
## Indvd_S.:C3 0.007 0.053 -0.008 0.000 0.007 0.010 0.000 -0.054 0.000
## Indvd_S.:C4 -0.010 0.005 0.010 0.000 0.010 -0.008 0.000 -0.005 0.000
## Indvd_S.:C5 -0.019 0.010 -0.020 0.020 0.000 0.000 -0.006 0.010 -0.010
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 -0.005
## Indvd_S.:C5 0.000 0.000tab_model(modA.894,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.36 | 0.39 | 49.59 – 51.12 | 129.10 | <0.001 |
| Individualism Score c | 0.01 | 0.02 | -0.03 – 0.05 | 0.47 | 0.636 |
| C1 | -8.24 | 0.77 | -9.76 – -6.73 | -10.65 | <0.001 |
| C2 | -17.94 | 1.15 | -20.20 – -15.69 | -15.62 | <0.001 |
| C3 | -4.61 | 1.18 | -6.93 – -2.30 | -3.91 | <0.001 |
| C4 | -12.97 | 1.33 | -15.58 – -10.36 | -9.74 | <0.001 |
| C5 | 19.87 | 1.34 | 17.24 – 22.50 | 14.83 | <0.001 |
|
Individualism Score c * C1 |
-0.12 | 0.04 | -0.20 – -0.03 | -2.73 | 0.006 |
|
Individualism Score c * C2 |
-0.29 | 0.06 | -0.41 – -0.16 | -4.49 | <0.001 |
|
Individualism Score c * C3 |
0.02 | 0.07 | -0.11 – 0.15 | 0.34 | 0.735 |
|
Individualism Score c * C4 |
-0.12 | 0.07 | -0.26 – 0.03 | -1.61 | 0.108 |
|
Individualism Score c * C5 |
0.38 | 0.08 | 0.23 – 0.53 | 5.05 | <0.001 |
| Random Effects | |||||
| σ2 | 446.54 | ||||
| τ00 id | 3.10 | ||||
| ICC | 0.01 | ||||
| N id | 1002 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.199 / 0.205 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does individualism predict naturalness perception, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.893 <- lmer(Naturalness ~ Ideology.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.893)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Naturalness ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c *
## C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 +
## Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 26868.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09770 -0.64861 -0.02132 0.62192 3.09852
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.956 1.399
## Residual 454.273 21.314
## Number of obs: 3002, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 51.2578 0.5767 999.5969 88.879 < 2e-16 ***
## Ideology.c 0.7231 0.3616 1004.1207 2.000 0.04579 *
## C1 -10.3212 1.1476 2785.3866 -8.994 < 2e-16 ***
## C2 -17.8440 1.6718 2719.7567 -10.674 < 2e-16 ***
## C3 -5.1856 1.7716 2750.4489 -2.927 0.00345 **
## C4 -13.7625 1.9916 2800.1149 -6.910 5.96e-12 ***
## C5 18.4947 1.9810 2754.8577 9.336 < 2e-16 ***
## Ideology.c:C1 -1.8663 0.7195 2787.0988 -2.594 0.00954 **
## Ideology.c:C2 0.1288 1.0507 2734.6488 0.123 0.90247
## Ideology.c:C3 -0.4281 1.1212 2767.1959 -0.382 0.70263
## Ideology.c:C4 -0.7027 1.2476 2792.6325 -0.563 0.57334
## Ideology.c:C5 -1.2121 1.2278 2736.2497 -0.987 0.32362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.734
## C1 -0.031 -0.033
## C2 -0.026 -0.019 -0.026
## C3 0.027 0.025 -0.027 0.000
## C4 0.009 0.002 -0.009 0.000 -0.009
## C5 0.016 0.017 0.016 -0.017 0.000 0.000
## Idelgy.c:C1 -0.033 -0.040 0.734 -0.019 -0.025 -0.001 0.017
## Idelgy.c:C2 -0.019 -0.012 -0.019 0.722 0.000 0.000 -0.018 -0.012
## Idelgy.c:C3 0.024 0.037 -0.025 0.000 0.741 -0.002 0.000 -0.037 0.000
## Idelgy.c:C4 0.002 -0.007 -0.001 0.000 -0.002 0.739 0.000 0.007 0.000
## Idelgy.c:C5 0.017 0.038 0.017 -0.018 0.000 0.000 0.732 0.038 -0.039
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.006
## Idelgy.c:C5 0.001 0.000tab_model(modA.893,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 51.26 | 0.58 | 50.13 – 52.39 | 88.88 | <0.001 |
| Ideology c | 0.72 | 0.36 | 0.01 – 1.43 | 2.00 | 0.046 |
| C1 | -10.32 | 1.15 | -12.57 – -8.07 | -8.99 | <0.001 |
| C2 | -17.84 | 1.67 | -21.12 – -14.57 | -10.67 | <0.001 |
| C3 | -5.19 | 1.77 | -8.66 – -1.71 | -2.93 | 0.003 |
| C4 | -13.76 | 1.99 | -17.67 – -9.86 | -6.91 | <0.001 |
| C5 | 18.49 | 1.98 | 14.61 – 22.38 | 9.34 | <0.001 |
| Ideology c * C1 | -1.87 | 0.72 | -3.28 – -0.46 | -2.59 | 0.010 |
| Ideology c * C2 | 0.13 | 1.05 | -1.93 – 2.19 | 0.12 | 0.902 |
| Ideology c * C3 | -0.43 | 1.12 | -2.63 – 1.77 | -0.38 | 0.703 |
| Ideology c * C4 | -0.70 | 1.25 | -3.15 – 1.74 | -0.56 | 0.573 |
| Ideology c * C5 | -1.21 | 1.23 | -3.62 – 1.20 | -0.99 | 0.324 |
| Random Effects | |||||
| σ2 | 454.27 | ||||
| τ00 id | 1.96 | ||||
| ICC | 0.00 | ||||
| N id | 1002 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.188 / 0.191 | ||||
Risk
Q.1 How do burger contrasts predict risk perception?
modA.860 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.860)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30939.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.94921 -0.55876 0.01162 0.51048 2.97133
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 329.8 18.16
## Residual 455.4 21.34
## Number of obs: 3319, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.4194 0.6869 1017.0632 64.663 < 2e-16 ***
## C1 5.9265 0.8118 2645.8442 7.300 3.79e-13 ***
## C2 9.4518 1.1967 2620.9392 7.898 4.13e-15 ***
## C3 -7.7368 1.2733 2595.6429 -6.076 1.41e-09 ***
## C4 8.3421 1.4406 2612.5948 5.791 7.84e-09 ***
## C5 2.5476 1.2299 2412.7516 2.071 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.044
## C2 0.031 0.049
## C3 0.013 -0.039 0.006
## C4 0.005 -0.001 0.004 -0.016
## C5 -0.065 -0.134 0.129 0.008 -0.007tab_model(modA.860,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.69 | 43.07 – 45.77 | 64.66 | <0.001 |
| C1 | 5.93 | 0.81 | 4.33 – 7.52 | 7.30 | <0.001 |
| C2 | 9.45 | 1.20 | 7.11 – 11.80 | 7.90 | <0.001 |
| C3 | -7.74 | 1.27 | -10.23 – -5.24 | -6.08 | <0.001 |
| C4 | 8.34 | 1.44 | 5.52 – 11.17 | 5.79 | <0.001 |
| C5 | 2.55 | 1.23 | 0.14 – 4.96 | 2.07 | 0.038 |
| Random Effects | |||||
| σ2 | 455.41 | ||||
| τ00 id | 329.82 | ||||
| ICC | 0.42 | ||||
| N id | 1004 | ||||
| Observations | 3319 | ||||
| Marginal R2 / Conditional R2 | 0.035 / 0.440 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict risk perception, over and above burger contrasts?
modA.861 <- lmer(Risk ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.861)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c *
## C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c *
## C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30851.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2013 -0.5355 0.0326 0.5028 3.0857
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 322.4 17.95
## Residual 446.7 21.14
## Number of obs: 3315, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.40688 0.68014 1014.38170 65.291 < 2e-16 ***
## ATNS_Score.c 0.17035 0.03922 1017.43565 4.344 1.54e-05 ***
## C1 6.03696 0.80458 2637.63269 7.503 8.46e-14 ***
## C2 9.68506 1.18653 2614.71617 8.163 5.04e-16 ***
## C3 -7.73229 1.26249 2589.42215 -6.125 1.05e-09 ***
## C4 8.43509 1.42778 2605.46473 5.908 3.92e-09 ***
## C5 2.47426 1.21913 2406.17624 2.030 0.0425 *
## ATNS_Score.c:C1 0.27134 0.04643 2633.63023 5.844 5.71e-09 ***
## ATNS_Score.c:C2 0.27792 0.06992 2626.01719 3.975 7.23e-05 ***
## ATNS_Score.c:C3 0.03244 0.07292 2581.13265 0.445 0.6565
## ATNS_Score.c:C4 0.11902 0.08090 2614.42425 1.471 0.1413
## ATNS_Score.c:C5 0.11369 0.07067 2414.23319 1.609 0.1078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c 0.003
## C1 -0.044 0.005
## C2 0.031 0.000 0.050
## C3 0.013 -0.003 -0.039 0.006
## C4 0.005 -0.008 0.000 0.004 -0.015
## C5 -0.065 -0.011 -0.134 0.129 0.007 -0.008
## ATNS_Sc.:C1 0.005 -0.033 0.014 0.009 0.007 0.016 -0.018
## ATNS_Sc.:C2 0.000 0.039 0.009 0.017 -0.002 -0.004 0.023 0.069
## ATNS_Sc.:C3 -0.004 0.026 0.006 -0.003 -0.009 0.020 -0.001 -0.076
## ATNS_Sc.:C4 -0.008 0.019 0.016 -0.005 0.020 0.007 -0.002 -0.023
## ATNS_Sc.:C5 -0.011 -0.071 -0.019 0.023 -0.002 -0.003 0.024 -0.140
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.007
## ATNS_Sc.:C4 0.004 -0.044
## ATNS_Sc.:C5 0.140 0.011 -0.006tab_model(modA.861,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.41 | 0.68 | 43.07 – 45.74 | 65.29 | <0.001 |
| ATNS Score c | 0.17 | 0.04 | 0.09 – 0.25 | 4.34 | <0.001 |
| C1 | 6.04 | 0.80 | 4.46 – 7.61 | 7.50 | <0.001 |
| C2 | 9.69 | 1.19 | 7.36 – 12.01 | 8.16 | <0.001 |
| C3 | -7.73 | 1.26 | -10.21 – -5.26 | -6.12 | <0.001 |
| C4 | 8.44 | 1.43 | 5.64 – 11.23 | 5.91 | <0.001 |
| C5 | 2.47 | 1.22 | 0.08 – 4.86 | 2.03 | 0.042 |
| ATNS Score c * C1 | 0.27 | 0.05 | 0.18 – 0.36 | 5.84 | <0.001 |
| ATNS Score c * C2 | 0.28 | 0.07 | 0.14 – 0.42 | 3.97 | <0.001 |
| ATNS Score c * C3 | 0.03 | 0.07 | -0.11 – 0.18 | 0.44 | 0.656 |
| ATNS Score c * C4 | 0.12 | 0.08 | -0.04 – 0.28 | 1.47 | 0.141 |
| ATNS Score c * C5 | 0.11 | 0.07 | -0.02 – 0.25 | 1.61 | 0.108 |
| Random Effects | |||||
| σ2 | 446.70 | ||||
| τ00 id | 322.38 | ||||
| ICC | 0.42 | ||||
| N id | 1002 | ||||
| Observations | 3315 | ||||
| Marginal R2 / Conditional R2 | 0.057 / 0.453 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8617 <- lmer(Risk ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8617)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27580.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0294 -0.5387 0.0295 0.5237 3.3940
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 241.7 15.55
## Residual 407.3 20.18
## Number of obs: 3000, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.638e+01 6.194e-01 1.014e+03 74.879 < 2e-16
## ATNS_Score.c 1.932e-01 3.587e-02 1.031e+03 5.387 8.86e-08
## Naturalness.c -4.285e-01 2.005e-02 2.610e+03 -21.372 < 2e-16
## C1 3.160e+00 8.099e-01 2.344e+03 3.902 9.82e-05
## C2 8.454e-01 1.222e+00 2.323e+03 0.692 0.4892
## C3 -9.621e+00 1.210e+00 2.313e+03 -7.950 2.88e-15
## C4 2.944e+00 1.385e+00 2.327e+03 2.125 0.0337
## C5 1.382e+01 1.417e+00 2.316e+03 9.754 < 2e-16
## ATNS_Score.c:Naturalness.c -3.349e-03 9.869e-04 2.593e+03 -3.394 0.0007
## ATNS_Score.c:C1 2.201e-01 4.804e-02 2.349e+03 4.581 4.86e-06
## ATNS_Score.c:C2 -1.016e-02 7.219e-02 2.328e+03 -0.141 0.8880
## ATNS_Score.c:C3 5.423e-02 6.982e-02 2.300e+03 0.777 0.4374
## ATNS_Score.c:C4 1.754e-02 7.888e-02 2.328e+03 0.222 0.8240
## ATNS_Score.c:C5 4.138e-01 8.041e-02 2.325e+03 5.145 2.89e-07
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1 ***
## C2
## C3 ***
## C4 *
## C5 ***
## ATNS_Score.c:Naturalness.c ***
## ATNS_Score.c:C1 ***
## ATNS_Score.c:C2
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8617,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.38 | 0.62 | 45.17 – 47.60 | 74.88 | <0.001 |
| ATNS Score c | 0.19 | 0.04 | 0.12 – 0.26 | 5.39 | <0.001 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.39 | -21.37 | <0.001 |
| C1 | 3.16 | 0.81 | 1.57 – 4.75 | 3.90 | <0.001 |
| C2 | 0.85 | 1.22 | -1.55 – 3.24 | 0.69 | 0.489 |
| C3 | -9.62 | 1.21 | -11.99 – -7.25 | -7.95 | <0.001 |
| C4 | 2.94 | 1.39 | 0.23 – 5.66 | 2.13 | 0.034 |
| C5 | 13.82 | 1.42 | 11.04 – 16.60 | 9.75 | <0.001 |
|
ATNS Score c * Naturalness c |
-0.00 | 0.00 | -0.01 – -0.00 | -3.39 | 0.001 |
| ATNS Score c * C1 | 0.22 | 0.05 | 0.13 – 0.31 | 4.58 | <0.001 |
| ATNS Score c * C2 | -0.01 | 0.07 | -0.15 – 0.13 | -0.14 | 0.888 |
| ATNS Score c * C3 | 0.05 | 0.07 | -0.08 – 0.19 | 0.78 | 0.437 |
| ATNS Score c * C4 | 0.02 | 0.08 | -0.14 – 0.17 | 0.22 | 0.824 |
| ATNS Score c * C5 | 0.41 | 0.08 | 0.26 – 0.57 | 5.15 | <0.001 |
| Random Effects | |||||
| σ2 | 407.32 | ||||
| τ00 id | 241.71 | ||||
| ICC | 0.37 | ||||
| N id | 1002 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.182 / 0.487 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict risk perception, over and above burger contrasts?
modA.862 <- lmer(Risk ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.862)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c * C1 +
## AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 + AW_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30856.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2317 -0.5478 0.0229 0.4893 3.3224
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 332.2 18.23
## Residual 446.3 21.13
## Number of obs: 3313, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.32446 0.68762 1012.80296 64.461 < 2e-16 ***
## AW_Score.c 0.03635 0.02804 1015.84523 1.296 0.1952
## C1 6.01589 0.80526 2627.61696 7.471 1.08e-13 ***
## C2 9.73390 1.18735 2606.73185 8.198 3.79e-16 ***
## C3 -7.56932 1.26353 2582.92827 -5.991 2.38e-09 ***
## C4 7.93207 1.42979 2597.14842 5.548 3.19e-08 ***
## C5 2.67547 1.22067 2402.51672 2.192 0.0285 *
## AW_Score.c:C1 -0.07321 0.03273 2604.67207 -2.237 0.0254 *
## AW_Score.c:C2 0.24051 0.04888 2618.74665 4.920 9.17e-07 ***
## AW_Score.c:C3 -0.24829 0.05209 2602.05117 -4.767 1.98e-06 ***
## AW_Score.c:C4 0.09750 0.05800 2595.97064 1.681 0.0928 .
## AW_Score.c:C5 0.02475 0.04960 2401.82109 0.499 0.6179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c 0.000
## C1 -0.043 0.017
## C2 0.030 -0.005 0.049
## C3 0.013 0.006 -0.038 0.006
## C4 0.006 -0.014 -0.003 0.004 -0.018
## C5 -0.065 -0.010 -0.135 0.130 0.008 -0.007
## AW_Scr.c:C1 0.016 -0.042 -0.003 -0.010 -0.017 0.029 -0.015
## AW_Scr.c:C2 -0.005 0.036 -0.010 0.030 -0.005 0.003 0.022 0.058
## AW_Scr.c:C3 0.006 0.020 -0.018 -0.005 -0.022 0.030 0.000 -0.049 0.000
## AW_Scr.c:C4 -0.015 0.031 0.029 0.004 0.031 -0.036 -0.001 -0.042 0.001
## AW_Scr.c:C5 -0.010 -0.065 -0.015 0.022 0.000 -0.001 0.046 -0.131 0.130
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 -0.069
## AW_Scr.c:C5 0.004 -0.008tab_model(modA.862,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.32 | 0.69 | 42.98 – 45.67 | 64.46 | <0.001 |
| AW Score c | 0.04 | 0.03 | -0.02 – 0.09 | 1.30 | 0.195 |
| C1 | 6.02 | 0.81 | 4.44 – 7.59 | 7.47 | <0.001 |
| C2 | 9.73 | 1.19 | 7.41 – 12.06 | 8.20 | <0.001 |
| C3 | -7.57 | 1.26 | -10.05 – -5.09 | -5.99 | <0.001 |
| C4 | 7.93 | 1.43 | 5.13 – 10.74 | 5.55 | <0.001 |
| C5 | 2.68 | 1.22 | 0.28 – 5.07 | 2.19 | 0.028 |
| AW Score c * C1 | -0.07 | 0.03 | -0.14 – -0.01 | -2.24 | 0.025 |
| AW Score c * C2 | 0.24 | 0.05 | 0.14 – 0.34 | 4.92 | <0.001 |
| AW Score c * C3 | -0.25 | 0.05 | -0.35 – -0.15 | -4.77 | <0.001 |
| AW Score c * C4 | 0.10 | 0.06 | -0.02 – 0.21 | 1.68 | 0.093 |
| AW Score c * C5 | 0.02 | 0.05 | -0.07 – 0.12 | 0.50 | 0.618 |
| Random Effects | |||||
| σ2 | 446.33 | ||||
| τ00 id | 332.16 | ||||
| ICC | 0.43 | ||||
| N id | 1001 | ||||
| Observations | 3313 | ||||
| Marginal R2 / Conditional R2 | 0.047 / 0.454 | ||||
Q.2 (ANIMAL WELFARE) Does concern for animal welfare depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8629 <- lmer(Risk ~ AW_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8629)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ AW_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## AW_Score.c * C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c *
## C4 + AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27629.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7973 -0.5369 0.0298 0.5115 3.4319
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 249.2 15.78
## Residual 414.8 20.37
## Number of obs: 2998, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.641e+01 6.278e-01 1.010e+03 73.933 < 2e-16 ***
## AW_Score.c 5.009e-02 2.564e-02 1.016e+03 1.954 0.050966 .
## Naturalness.c -4.298e-01 2.006e-02 2.619e+03 -21.424 < 2e-16 ***
## C1 3.376e+00 8.187e-01 2.338e+03 4.124 3.85e-05 ***
## C2 9.370e-01 1.234e+00 2.314e+03 0.759 0.447746
## C3 -9.490e+00 1.225e+00 2.311e+03 -7.746 1.41e-14 ***
## C4 2.671e+00 1.399e+00 2.321e+03 1.909 0.056378 .
## C5 1.421e+01 1.432e+00 2.314e+03 9.926 < 2e-16 ***
## AW_Score.c:Naturalness.c -4.467e-04 7.472e-04 2.706e+03 -0.598 0.549973
## AW_Score.c:C1 -4.085e-02 3.412e-02 2.333e+03 -1.197 0.231272
## AW_Score.c:C2 1.222e-01 5.031e-02 2.332e+03 2.430 0.015190 *
## AW_Score.c:C3 -1.888e-01 5.090e-02 2.331e+03 -3.708 0.000213 ***
## AW_Score.c:C4 7.902e-02 5.716e-02 2.328e+03 1.382 0.166953
## AW_Score.c:C5 1.254e-01 5.784e-02 2.304e+03 2.168 0.030274 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8629,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.41 | 0.63 | 45.18 – 47.65 | 73.93 | <0.001 |
| AW Score c | 0.05 | 0.03 | -0.00 – 0.10 | 1.95 | 0.051 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.39 | -21.42 | <0.001 |
| C1 | 3.38 | 0.82 | 1.77 – 4.98 | 4.12 | <0.001 |
| C2 | 0.94 | 1.23 | -1.48 – 3.36 | 0.76 | 0.448 |
| C3 | -9.49 | 1.23 | -11.89 – -7.09 | -7.75 | <0.001 |
| C4 | 2.67 | 1.40 | -0.07 – 5.41 | 1.91 | 0.056 |
| C5 | 14.21 | 1.43 | 11.40 – 17.02 | 9.93 | <0.001 |
|
AW Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.60 | 0.550 |
| AW Score c * C1 | -0.04 | 0.03 | -0.11 – 0.03 | -1.20 | 0.231 |
| AW Score c * C2 | 0.12 | 0.05 | 0.02 – 0.22 | 2.43 | 0.015 |
| AW Score c * C3 | -0.19 | 0.05 | -0.29 – -0.09 | -3.71 | <0.001 |
| AW Score c * C4 | 0.08 | 0.06 | -0.03 – 0.19 | 1.38 | 0.167 |
| AW Score c * C5 | 0.13 | 0.06 | 0.01 – 0.24 | 2.17 | 0.030 |
| Random Effects | |||||
| σ2 | 414.83 | ||||
| τ00 id | 249.16 | ||||
| ICC | 0.38 | ||||
| N id | 1001 | ||||
| Observations | 2998 | ||||
| Marginal R2 / Conditional R2 | 0.162 / 0.477 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict risk perception, over and above burger contrasts?
modA.863 <- lmer(Risk ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.863)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c * C1 +
## CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c * C4 +
## CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30884.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2817 -0.5411 0.0160 0.5032 3.3230
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 328.8 18.13
## Residual 450.6 21.23
## Number of obs: 3315, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.42216 0.68581 1013.46468 64.773 < 2e-16 ***
## CNS_Score.c -0.15938 0.05531 1023.07715 -2.882 0.004039 **
## C1 6.06443 0.80851 2634.35075 7.501 8.62e-14 ***
## C2 9.57307 1.19336 2611.53667 8.022 1.56e-15 ***
## C3 -7.72192 1.26825 2586.41534 -6.089 1.31e-09 ***
## C4 8.20473 1.43434 2602.09959 5.720 1.19e-08 ***
## C5 2.44594 1.22421 2404.24223 1.998 0.045832 *
## CNS_Score.c:C1 -0.08770 0.06542 2624.73743 -1.341 0.180185
## CNS_Score.c:C2 0.31326 0.09983 2617.47173 3.138 0.001721 **
## CNS_Score.c:C3 -0.33996 0.10195 2585.30315 -3.335 0.000866 ***
## CNS_Score.c:C4 0.21239 0.11544 2616.68419 1.840 0.065900 .
## CNS_Score.c:C5 0.05031 0.09839 2421.61438 0.511 0.609129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c 0.000
## C1 -0.043 0.004
## C2 0.032 0.017 0.051
## C3 0.013 0.003 -0.039 0.005
## C4 0.005 -0.008 -0.001 0.004 -0.016
## C5 -0.065 0.006 -0.134 0.128 0.008 -0.007
## CNS_Scr.:C1 0.004 -0.031 -0.002 0.032 -0.009 0.015 0.014
## CNS_Scr.:C2 0.017 0.054 0.030 0.045 -0.003 0.002 -0.007 0.105
## CNS_Scr.:C3 0.003 0.016 -0.010 -0.003 -0.006 0.017 0.002 -0.040
## CNS_Scr.:C4 -0.008 -0.009 0.015 0.002 0.017 -0.012 0.004 0.036
## CNS_Scr.:C5 0.006 -0.075 0.015 -0.007 0.002 0.003 -0.025 -0.151
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 -0.003
## CNS_Scr.:C4 0.004 0.006
## CNS_Scr.:C5 0.145 0.002 -0.010tab_model(modA.863,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.69 | 43.08 – 45.77 | 64.77 | <0.001 |
| CNS Score c | -0.16 | 0.06 | -0.27 – -0.05 | -2.88 | 0.004 |
| C1 | 6.06 | 0.81 | 4.48 – 7.65 | 7.50 | <0.001 |
| C2 | 9.57 | 1.19 | 7.23 – 11.91 | 8.02 | <0.001 |
| C3 | -7.72 | 1.27 | -10.21 – -5.24 | -6.09 | <0.001 |
| C4 | 8.20 | 1.43 | 5.39 – 11.02 | 5.72 | <0.001 |
| C5 | 2.45 | 1.22 | 0.05 – 4.85 | 2.00 | 0.046 |
| CNS Score c * C1 | -0.09 | 0.07 | -0.22 – 0.04 | -1.34 | 0.180 |
| CNS Score c * C2 | 0.31 | 0.10 | 0.12 – 0.51 | 3.14 | 0.002 |
| CNS Score c * C3 | -0.34 | 0.10 | -0.54 – -0.14 | -3.33 | 0.001 |
| CNS Score c * C4 | 0.21 | 0.12 | -0.01 – 0.44 | 1.84 | 0.066 |
| CNS Score c * C5 | 0.05 | 0.10 | -0.14 – 0.24 | 0.51 | 0.609 |
| Random Effects | |||||
| σ2 | 450.60 | ||||
| τ00 id | 328.81 | ||||
| ICC | 0.42 | ||||
| N id | 1002 | ||||
| Observations | 3315 | ||||
| Marginal R2 / Conditional R2 | 0.046 / 0.449 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8638 <- lmer(Risk ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8638)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27637.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9517 -0.5474 0.0270 0.5260 3.4939
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 248.1 15.75
## Residual 415.1 20.37
## Number of obs: 3000, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.651e+01 6.265e-01 1.012e+03 74.234 < 2e-16 ***
## CNS_Score.c -1.130e-01 5.079e-02 1.038e+03 -2.224 0.02633 *
## Naturalness.c -4.330e-01 2.011e-02 2.623e+03 -21.535 < 2e-16 ***
## C1 3.286e+00 8.165e-01 2.339e+03 4.024 5.90e-05 ***
## C2 6.655e-01 1.234e+00 2.320e+03 0.539 0.58976
## C3 -9.650e+00 1.221e+00 2.310e+03 -7.901 4.25e-15 ***
## C4 2.746e+00 1.398e+00 2.324e+03 1.964 0.04967 *
## C5 1.392e+01 1.430e+00 2.316e+03 9.731 < 2e-16 ***
## CNS_Score.c:Naturalness.c -2.073e-03 1.344e-03 2.583e+03 -1.542 0.12310
## CNS_Score.c:C1 -2.036e-01 6.806e-02 2.338e+03 -2.991 0.00281 **
## CNS_Score.c:C2 1.412e-01 1.032e-01 2.343e+03 1.369 0.17124
## CNS_Score.c:C3 -3.198e-01 9.837e-02 2.299e+03 -3.251 0.00117 **
## CNS_Score.c:C4 3.900e-02 1.138e-01 2.338e+03 0.343 0.73174
## CNS_Score.c:C5 1.463e-01 1.109e-01 2.299e+03 1.320 0.18704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8638,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.51 | 0.63 | 45.28 – 47.74 | 74.23 | <0.001 |
| CNS Score c | -0.11 | 0.05 | -0.21 – -0.01 | -2.22 | 0.026 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.39 | -21.53 | <0.001 |
| C1 | 3.29 | 0.82 | 1.68 – 4.89 | 4.02 | <0.001 |
| C2 | 0.67 | 1.23 | -1.75 – 3.09 | 0.54 | 0.590 |
| C3 | -9.65 | 1.22 | -12.04 – -7.26 | -7.90 | <0.001 |
| C4 | 2.75 | 1.40 | 0.00 – 5.49 | 1.96 | 0.050 |
| C5 | 13.92 | 1.43 | 11.11 – 16.72 | 9.73 | <0.001 |
|
CNS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.54 | 0.123 |
| CNS Score c * C1 | -0.20 | 0.07 | -0.34 – -0.07 | -2.99 | 0.003 |
| CNS Score c * C2 | 0.14 | 0.10 | -0.06 – 0.34 | 1.37 | 0.171 |
| CNS Score c * C3 | -0.32 | 0.10 | -0.51 – -0.13 | -3.25 | 0.001 |
| CNS Score c * C4 | 0.04 | 0.11 | -0.18 – 0.26 | 0.34 | 0.732 |
| CNS Score c * C5 | 0.15 | 0.11 | -0.07 – 0.36 | 1.32 | 0.187 |
| Random Effects | |||||
| σ2 | 415.06 | ||||
| τ00 id | 248.12 | ||||
| ICC | 0.37 | ||||
| N id | 1002 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.164 / 0.477 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict risk perception, over and above burger contrasts?
modA.864 <- lmer(Risk ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.864)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30807.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2261 -0.5556 0.0210 0.5014 3.2556
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 328.5 18.12
## Residual 441.0 21.00
## Number of obs: 3312, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.439e+01 6.837e-01 1.012e+03 64.923 < 2e-16 ***
## CCBelief_Score.c -1.636e-02 2.937e-02 1.015e+03 -0.557 0.5777
## C1 5.894e+00 8.004e-01 2.627e+03 7.365 2.37e-13 ***
## C2 9.656e+00 1.181e+00 2.605e+03 8.179 4.43e-16 ***
## C3 -7.700e+00 1.255e+00 2.580e+03 -6.134 9.92e-10 ***
## C4 8.527e+00 1.420e+00 2.595e+03 6.004 2.20e-09 ***
## C5 2.650e+00 1.212e+00 2.401e+03 2.186 0.0289 *
## CCBelief_Score.c:C1 -1.648e-01 3.405e-02 2.575e+03 -4.841 1.37e-06 ***
## CCBelief_Score.c:C2 2.447e-01 5.169e-02 2.616e+03 4.733 2.33e-06 ***
## CCBelief_Score.c:C3 -3.259e-01 5.493e-02 2.597e+03 -5.932 3.39e-09 ***
## CCBelief_Score.c:C4 3.612e-02 6.067e-02 2.603e+03 0.595 0.5517
## CCBelief_Score.c:C5 -6.172e-03 5.084e-02 2.399e+03 -0.121 0.9034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c 0.000
## C1 -0.043 0.009
## C2 0.031 -0.010 0.050
## C3 0.013 0.000 -0.039 0.006
## C4 0.004 0.010 0.000 0.004 -0.014
## C5 -0.065 -0.009 -0.134 0.129 0.008 -0.007
## CCBlf_S.:C1 0.009 -0.047 0.000 -0.019 -0.002 -0.012 -0.015
## CCBlf_S.:C2 -0.010 0.051 -0.019 0.000 -0.001 0.001 0.017 0.094
## CCBlf_S.:C3 0.000 0.027 -0.002 0.000 -0.013 -0.014 0.005 -0.063
## CCBlf_S.:C4 0.010 0.001 -0.013 0.001 -0.014 -0.018 -0.003 0.007
## CCBlf_S.:C5 -0.009 -0.063 -0.015 0.017 0.005 -0.003 0.040 -0.128
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 -0.001
## CCBlf_S.:C4 0.009 -0.009
## CCBlf_S.:C5 0.126 0.002 0.000tab_model(modA.864,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.39 | 0.68 | 43.05 – 45.73 | 64.92 | <0.001 |
| CCBelief Score c | -0.02 | 0.03 | -0.07 – 0.04 | -0.56 | 0.578 |
| C1 | 5.89 | 0.80 | 4.33 – 7.46 | 7.36 | <0.001 |
| C2 | 9.66 | 1.18 | 7.34 – 11.97 | 8.18 | <0.001 |
| C3 | -7.70 | 1.26 | -10.16 – -5.24 | -6.13 | <0.001 |
| C4 | 8.53 | 1.42 | 5.74 – 11.31 | 6.00 | <0.001 |
| C5 | 2.65 | 1.21 | 0.27 – 5.03 | 2.19 | 0.029 |
| CCBelief Score c * C1 | -0.16 | 0.03 | -0.23 – -0.10 | -4.84 | <0.001 |
| CCBelief Score c * C2 | 0.24 | 0.05 | 0.14 – 0.35 | 4.73 | <0.001 |
| CCBelief Score c * C3 | -0.33 | 0.05 | -0.43 – -0.22 | -5.93 | <0.001 |
| CCBelief Score c * C4 | 0.04 | 0.06 | -0.08 – 0.16 | 0.60 | 0.552 |
| CCBelief Score c * C5 | -0.01 | 0.05 | -0.11 – 0.09 | -0.12 | 0.903 |
| Random Effects | |||||
| σ2 | 441.01 | ||||
| τ00 id | 328.48 | ||||
| ICC | 0.43 | ||||
| N id | 1001 | ||||
| Observations | 3312 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.458 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8649 <- lmer(Risk ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8649)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27587.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9109 -0.5415 0.0271 0.5172 3.4343
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 247.6 15.74
## Residual 409.9 20.25
## Number of obs: 2997, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.644e+01 6.250e-01 1.008e+03 74.296
## CCBelief_Score.c -2.981e-03 2.689e-02 1.015e+03 -0.111
## Naturalness.c -4.229e-01 2.007e-02 2.620e+03 -21.066
## C1 3.353e+00 8.129e-01 2.335e+03 4.124
## C2 9.081e-01 1.227e+00 2.310e+03 0.740
## C3 -9.435e+00 1.218e+00 2.308e+03 -7.746
## C4 3.214e+00 1.392e+00 2.322e+03 2.310
## C5 1.431e+01 1.425e+00 2.316e+03 10.047
## CCBelief_Score.c:Naturalness.c -1.415e-03 7.971e-04 2.724e+03 -1.775
## CCBelief_Score.c:C1 -1.811e-01 3.558e-02 2.309e+03 -5.089
## CCBelief_Score.c:C2 1.193e-01 5.299e-02 2.311e+03 2.252
## CCBelief_Score.c:C3 -2.634e-01 5.385e-02 2.334e+03 -4.890
## CCBelief_Score.c:C4 -4.478e-03 6.010e-02 2.363e+03 -0.075
## CCBelief_Score.c:C5 1.274e-01 5.916e-02 2.278e+03 2.154
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 0.9118
## Naturalness.c < 2e-16 ***
## C1 3.85e-05 ***
## C2 0.4592
## C3 1.41e-14 ***
## C4 0.0210 *
## C5 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.0760 .
## CCBelief_Score.c:C1 3.90e-07 ***
## CCBelief_Score.c:C2 0.0244 *
## CCBelief_Score.c:C3 1.07e-06 ***
## CCBelief_Score.c:C4 0.9406
## CCBelief_Score.c:C5 0.0314 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8649,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.44 | 0.63 | 45.21 – 47.66 | 74.30 | <0.001 |
| CCBelief Score c | -0.00 | 0.03 | -0.06 – 0.05 | -0.11 | 0.912 |
| Naturalness c | -0.42 | 0.02 | -0.46 – -0.38 | -21.07 | <0.001 |
| C1 | 3.35 | 0.81 | 1.76 – 4.95 | 4.12 | <0.001 |
| C2 | 0.91 | 1.23 | -1.50 – 3.31 | 0.74 | 0.459 |
| C3 | -9.43 | 1.22 | -11.82 – -7.05 | -7.75 | <0.001 |
| C4 | 3.21 | 1.39 | 0.49 – 5.94 | 2.31 | 0.021 |
| C5 | 14.31 | 1.42 | 11.52 – 17.11 | 10.05 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.78 | 0.076 |
| CCBelief Score c * C1 | -0.18 | 0.04 | -0.25 – -0.11 | -5.09 | <0.001 |
| CCBelief Score c * C2 | 0.12 | 0.05 | 0.02 – 0.22 | 2.25 | 0.024 |
| CCBelief Score c * C3 | -0.26 | 0.05 | -0.37 – -0.16 | -4.89 | <0.001 |
| CCBelief Score c * C4 | -0.00 | 0.06 | -0.12 – 0.11 | -0.07 | 0.941 |
| CCBelief Score c * C5 | 0.13 | 0.06 | 0.01 – 0.24 | 2.15 | 0.031 |
| Random Effects | |||||
| σ2 | 409.88 | ||||
| τ00 id | 247.65 | ||||
| ICC | 0.38 | ||||
| N id | 1001 | ||||
| Observations | 2997 | ||||
| Marginal R2 / Conditional R2 | 0.167 / 0.481 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict risk perception, over and above burger contrasts?
modA.865 <- lmer(Risk ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.865)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c * C1 +
## DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 + DS_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30892.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.04758 -0.54972 0.01577 0.51622 3.02806
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 331.2 18.20
## Residual 453.3 21.29
## Number of obs: 3313, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.41245 0.68853 1013.17980 64.503 < 2e-16 ***
## DS_Score.c 0.05685 0.03314 1019.82005 1.716 0.08654 .
## C1 5.92965 0.81146 2634.17348 7.307 3.59e-13 ***
## C2 9.61266 1.19559 2609.59757 8.040 1.35e-15 ***
## C3 -7.80754 1.27293 2585.06206 -6.134 9.91e-10 ***
## C4 8.42217 1.44054 2601.74020 5.847 5.65e-09 ***
## C5 2.53109 1.22870 2403.36176 2.060 0.03951 *
## DS_Score.c:C1 0.09303 0.03935 2656.57393 2.364 0.01814 *
## DS_Score.c:C2 0.15252 0.05735 2619.29058 2.660 0.00787 **
## DS_Score.c:C3 0.04134 0.06112 2590.33436 0.676 0.49882
## DS_Score.c:C4 -0.04983 0.06890 2601.33672 -0.723 0.46959
## DS_Score.c:C5 0.07555 0.06125 2418.88339 1.233 0.21752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c 0.001
## C1 -0.044 0.004
## C2 0.031 0.002 0.049
## C3 0.013 -0.011 -0.038 0.006
## C4 0.004 -0.018 0.000 0.004 -0.014
## C5 -0.065 -0.007 -0.133 0.129 0.007 -0.008
## DS_Scr.c:C1 0.004 -0.034 -0.005 0.013 0.017 0.042 -0.013
## DS_Scr.c:C2 0.002 0.010 0.013 0.014 -0.007 -0.004 0.014 0.008
## DS_Scr.c:C3 -0.011 0.014 0.016 -0.006 -0.019 0.031 0.006 -0.040 0.011
## DS_Scr.c:C4 -0.019 0.017 0.041 -0.003 0.031 0.015 -0.005 -0.015 0.002
## DS_Scr.c:C5 -0.007 -0.078 -0.012 0.014 0.005 -0.006 0.007 -0.154 0.154
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 -0.046
## DS_Scr.c:C5 0.006 -0.005tab_model(modA.865,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.41 | 0.69 | 43.06 – 45.76 | 64.50 | <0.001 |
| DS Score c | 0.06 | 0.03 | -0.01 – 0.12 | 1.72 | 0.086 |
| C1 | 5.93 | 0.81 | 4.34 – 7.52 | 7.31 | <0.001 |
| C2 | 9.61 | 1.20 | 7.27 – 11.96 | 8.04 | <0.001 |
| C3 | -7.81 | 1.27 | -10.30 – -5.31 | -6.13 | <0.001 |
| C4 | 8.42 | 1.44 | 5.60 – 11.25 | 5.85 | <0.001 |
| C5 | 2.53 | 1.23 | 0.12 – 4.94 | 2.06 | 0.039 |
| DS Score c * C1 | 0.09 | 0.04 | 0.02 – 0.17 | 2.36 | 0.018 |
| DS Score c * C2 | 0.15 | 0.06 | 0.04 – 0.26 | 2.66 | 0.008 |
| DS Score c * C3 | 0.04 | 0.06 | -0.08 – 0.16 | 0.68 | 0.499 |
| DS Score c * C4 | -0.05 | 0.07 | -0.18 – 0.09 | -0.72 | 0.470 |
| DS Score c * C5 | 0.08 | 0.06 | -0.04 – 0.20 | 1.23 | 0.217 |
| Random Effects | |||||
| σ2 | 453.29 | ||||
| τ00 id | 331.25 | ||||
| ICC | 0.42 | ||||
| N id | 1001 | ||||
| Observations | 3313 | ||||
| Marginal R2 / Conditional R2 | 0.040 / 0.445 | ||||
Q.2 (DISGUST SENSITIVITY) Does sensitivity to disgust depend on perception of naturalness in predicting risk perception, over and above burger contrasts?
modA.8651 <- lmer(Risk ~ DS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8651)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ DS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## DS_Score.c * C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c *
## C4 + DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27634.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0201 -0.5428 0.0180 0.5261 3.4138
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 249.4 15.79
## Residual 416.0 20.40
## Number of obs: 2998, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.650e+01 6.282e-01 1.012e+03 74.026 < 2e-16 ***
## DS_Score.c 6.202e-02 3.035e-02 1.031e+03 2.044 0.04123 *
## Naturalness.c -4.409e-01 1.978e-02 2.593e+03 -22.295 < 2e-16 ***
## C1 3.195e+00 8.187e-01 2.340e+03 3.902 9.80e-05 ***
## C2 7.120e-01 1.234e+00 2.319e+03 0.577 0.56415
## C3 -9.720e+00 1.224e+00 2.307e+03 -7.943 3.05e-15 ***
## C4 3.011e+00 1.401e+00 2.323e+03 2.149 0.03173 *
## C5 1.415e+01 1.433e+00 2.315e+03 9.879 < 2e-16 ***
## DS_Score.c:Naturalness.c -7.659e-04 8.512e-04 2.583e+03 -0.900 0.36829
## DS_Score.c:C1 1.258e-01 4.118e-02 2.383e+03 3.055 0.00227 **
## DS_Score.c:C2 3.912e-02 5.859e-02 2.294e+03 0.668 0.50437
## DS_Score.c:C3 7.700e-02 5.871e-02 2.310e+03 1.311 0.18982
## DS_Score.c:C4 -5.816e-02 6.723e-02 2.318e+03 -0.865 0.38706
## DS_Score.c:C5 1.450e-01 6.960e-02 2.306e+03 2.083 0.03735 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8651,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.50 | 0.63 | 45.27 – 47.73 | 74.03 | <0.001 |
| DS Score c | 0.06 | 0.03 | 0.00 – 0.12 | 2.04 | 0.041 |
| Naturalness c | -0.44 | 0.02 | -0.48 – -0.40 | -22.29 | <0.001 |
| C1 | 3.19 | 0.82 | 1.59 – 4.80 | 3.90 | <0.001 |
| C2 | 0.71 | 1.23 | -1.71 – 3.13 | 0.58 | 0.564 |
| C3 | -9.72 | 1.22 | -12.12 – -7.32 | -7.94 | <0.001 |
| C4 | 3.01 | 1.40 | 0.26 – 5.76 | 2.15 | 0.032 |
| C5 | 14.15 | 1.43 | 11.34 – 16.96 | 9.88 | <0.001 |
|
DS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.90 | 0.368 |
| DS Score c * C1 | 0.13 | 0.04 | 0.05 – 0.21 | 3.06 | 0.002 |
| DS Score c * C2 | 0.04 | 0.06 | -0.08 – 0.15 | 0.67 | 0.504 |
| DS Score c * C3 | 0.08 | 0.06 | -0.04 – 0.19 | 1.31 | 0.190 |
| DS Score c * C4 | -0.06 | 0.07 | -0.19 – 0.07 | -0.87 | 0.387 |
| DS Score c * C5 | 0.14 | 0.07 | 0.01 – 0.28 | 2.08 | 0.037 |
| Random Effects | |||||
| σ2 | 415.97 | ||||
| τ00 id | 249.42 | ||||
| ICC | 0.37 | ||||
| N id | 1001 | ||||
| Observations | 2998 | ||||
| Marginal R2 / Conditional R2 | 0.162 / 0.476 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict risk perception, over and above burger contrasts?
modA.866 <- lmer(Risk ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)
summary(modA.866)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Risk ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c *
## C1 + Collectivism_Score.c * C2 + Collectivism_Score.c * C3 +
## Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30921.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.08291 -0.56179 0.02075 0.51989 3.09096
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 326.1 18.06
## Residual 453.8 21.30
## Number of obs: 3317, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.442e+01 6.842e-01 1.014e+03 64.914 < 2e-16 ***
## Collectivism_Score.c 1.148e-01 3.353e-02 1.020e+03 3.425 0.000639 ***
## C1 5.991e+00 8.106e-01 2.640e+03 7.390 1.95e-13 ***
## C2 9.458e+00 1.195e+00 2.615e+03 7.911 3.73e-15 ***
## C3 -7.729e+00 1.271e+00 2.590e+03 -6.081 1.37e-09 ***
## C4 8.322e+00 1.438e+00 2.607e+03 5.787 8.00e-09 ***
## C5 2.451e+00 1.229e+00 2.406e+03 1.995 0.046136 *
## Collectivism_Score.c:C1 9.381e-02 3.963e-02 2.614e+03 2.367 0.018010 *
## Collectivism_Score.c:C2 2.495e-03 5.998e-02 2.637e+03 0.042 0.966819
## Collectivism_Score.c:C3 5.950e-02 6.351e-02 2.612e+03 0.937 0.348940
## Collectivism_Score.c:C4 -1.318e-01 6.856e-02 2.598e+03 -1.923 0.054585 .
## Collectivism_Score.c:C5 1.707e-02 6.052e-02 2.415e+03 0.282 0.777870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. 0.000
## C1 -0.043 0.007
## C2 0.031 -0.008 0.049
## C3 0.013 -0.005 -0.039 0.006
## C4 0.005 0.007 -0.001 0.004 -0.016
## C5 -0.066 -0.009 -0.134 0.129 0.007 -0.007
## Cllct_S.:C1 0.007 -0.034 0.011 -0.008 0.007 -0.014 -0.018
## Cllct_S.:C2 -0.008 0.040 -0.008 -0.004 -0.003 0.003 0.015 0.065
## Cllct_S.:C3 -0.005 0.037 0.006 -0.003 -0.015 -0.010 0.000 -0.081 0.007
## Cllct_S.:C4 0.007 -0.008 -0.014 0.003 -0.010 -0.003 0.001 0.029 -0.002
## Cllct_S.:C5 -0.009 -0.071 -0.018 0.015 0.000 0.001 0.032 -0.142 0.140
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.008
## Cllct_S.:C5 0.005 -0.007tab_model(modA.866,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.68 | 43.07 – 45.76 | 64.91 | <0.001 |
| Collectivism Score c | 0.11 | 0.03 | 0.05 – 0.18 | 3.43 | 0.001 |
| C1 | 5.99 | 0.81 | 4.40 – 7.58 | 7.39 | <0.001 |
| C2 | 9.46 | 1.20 | 7.11 – 11.80 | 7.91 | <0.001 |
| C3 | -7.73 | 1.27 | -10.22 – -5.24 | -6.08 | <0.001 |
| C4 | 8.32 | 1.44 | 5.50 – 11.14 | 5.79 | <0.001 |
| C5 | 2.45 | 1.23 | 0.04 – 4.86 | 2.00 | 0.046 |
| Collectivism Score c * C1 | 0.09 | 0.04 | 0.02 – 0.17 | 2.37 | 0.018 |
| Collectivism Score c * C2 | 0.00 | 0.06 | -0.12 – 0.12 | 0.04 | 0.967 |
| Collectivism Score c * C3 | 0.06 | 0.06 | -0.07 – 0.18 | 0.94 | 0.349 |
| Collectivism Score c * C4 | -0.13 | 0.07 | -0.27 – 0.00 | -1.92 | 0.055 |
| Collectivism Score c * C5 | 0.02 | 0.06 | -0.10 – 0.14 | 0.28 | 0.778 |
| Random Effects | |||||
| σ2 | 453.76 | ||||
| τ00 id | 326.13 | ||||
| ICC | 0.42 | ||||
| N id | 1003 | ||||
| Observations | 3317 | ||||
| Marginal R2 / Conditional R2 | 0.045 / 0.444 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8665 <- lmer(Risk ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8665)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27661.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9935 -0.5366 0.0233 0.5294 3.3950
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 243.5 15.60
## Residual 417.0 20.42
## Number of obs: 3002, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.651e+01 6.232e-01 1.012e+03 74.623
## Collectivism_Score.c 1.080e-01 3.040e-02 1.002e+03 3.553
## Naturalness.c -4.418e-01 1.992e-02 2.602e+03 -22.180
## C1 3.289e+00 8.200e-01 2.352e+03 4.011
## C2 5.552e-01 1.233e+00 2.322e+03 0.450
## C3 -9.687e+00 1.222e+00 2.314e+03 -7.927
## C4 2.883e+00 1.401e+00 2.332e+03 2.058
## C5 1.421e+01 1.433e+00 2.323e+03 9.912
## Collectivism_Score.c:Naturalness.c -1.515e-03 8.533e-04 2.551e+03 -1.776
## Collectivism_Score.c:C1 1.214e-01 4.017e-02 2.332e+03 3.022
## Collectivism_Score.c:C2 -7.177e-02 6.175e-02 2.314e+03 -1.162
## Collectivism_Score.c:C3 3.496e-02 6.088e-02 2.334e+03 0.574
## Collectivism_Score.c:C4 -1.215e-01 6.697e-02 2.315e+03 -1.814
## Collectivism_Score.c:C5 1.327e-01 6.974e-02 2.321e+03 1.903
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 0.000398 ***
## Naturalness.c < 2e-16 ***
## C1 6.25e-05 ***
## C2 0.652601
## C3 3.45e-15 ***
## C4 0.039720 *
## C5 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.075853 .
## Collectivism_Score.c:C1 0.002536 **
## Collectivism_Score.c:C2 0.245257
## Collectivism_Score.c:C3 0.565856
## Collectivism_Score.c:C4 0.069769 .
## Collectivism_Score.c:C5 0.057164 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8665,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.51 | 0.62 | 45.28 – 47.73 | 74.62 | <0.001 |
| Collectivism Score c | 0.11 | 0.03 | 0.05 – 0.17 | 3.55 | <0.001 |
| Naturalness c | -0.44 | 0.02 | -0.48 – -0.40 | -22.18 | <0.001 |
| C1 | 3.29 | 0.82 | 1.68 – 4.90 | 4.01 | <0.001 |
| C2 | 0.56 | 1.23 | -1.86 – 2.97 | 0.45 | 0.653 |
| C3 | -9.69 | 1.22 | -12.08 – -7.29 | -7.93 | <0.001 |
| C4 | 2.88 | 1.40 | 0.14 – 5.63 | 2.06 | 0.040 |
| C5 | 14.21 | 1.43 | 11.40 – 17.02 | 9.91 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.78 | 0.076 |
| Collectivism Score c * C1 | 0.12 | 0.04 | 0.04 – 0.20 | 3.02 | 0.003 |
| Collectivism Score c * C2 | -0.07 | 0.06 | -0.19 – 0.05 | -1.16 | 0.245 |
| Collectivism Score c * C3 | 0.03 | 0.06 | -0.08 – 0.15 | 0.57 | 0.566 |
| Collectivism Score c * C4 | -0.12 | 0.07 | -0.25 – 0.01 | -1.81 | 0.070 |
| Collectivism Score c * C5 | 0.13 | 0.07 | -0.00 – 0.27 | 1.90 | 0.057 |
| Random Effects | |||||
| σ2 | 417.04 | ||||
| τ00 id | 243.48 | ||||
| ICC | 0.37 | ||||
| N id | 1003 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.167 / 0.474 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict risk perception, over and above burger contrasts?
modA.867 <- lmer(Risk ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.867)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Risk ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c *
## C1 + Individualism_Score.c * C2 + Individualism_Score.c *
## C3 + Individualism_Score.c * C4 + Individualism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30916.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.96662 -0.55503 0.01134 0.49778 3.05033
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 329.9 18.16
## Residual 455.0 21.33
## Number of obs: 3315, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.41824 0.68750 1012.84723 64.608 < 2e-16 ***
## Individualism_Score.c -0.04287 0.03831 1015.61177 -1.119 0.2633
## C1 5.95221 0.81194 2636.10129 7.331 3.03e-13 ***
## C2 9.54947 1.19752 2611.74584 7.974 2.27e-15 ***
## C3 -7.77330 1.27305 2587.53634 -6.106 1.17e-09 ***
## C4 8.38242 1.44036 2604.31961 5.820 6.62e-09 ***
## C5 2.52256 1.23107 2404.58221 2.049 0.0406 *
## Individualism_Score.c:C1 0.12425 0.04509 2613.65702 2.756 0.0059 **
## Individualism_Score.c:C2 0.06890 0.06680 2640.41000 1.032 0.3024
## Individualism_Score.c:C3 -0.01473 0.07203 2607.69724 -0.204 0.8380
## Individualism_Score.c:C4 0.04321 0.07856 2598.17481 0.550 0.5823
## Individualism_Score.c:C5 0.03081 0.07022 2411.50131 0.439 0.6609
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. 0.002
## C1 -0.043 0.004
## C2 0.030 0.000 0.050
## C3 0.013 0.004 -0.039 0.006
## C4 0.005 -0.004 -0.001 0.004 -0.016
## C5 -0.066 -0.007 -0.134 0.129 0.008 -0.007
## Indvd_S.:C1 0.004 -0.036 0.012 0.006 -0.009 0.010 -0.014
## Indvd_S.:C2 0.000 0.016 0.005 0.009 -0.008 0.001 0.014 0.015
## Indvd_S.:C3 0.004 0.031 -0.009 -0.008 0.006 0.014 -0.001 -0.074 0.007
## Indvd_S.:C4 -0.005 0.003 0.010 0.002 0.014 -0.012 0.002 0.005 0.000
## Indvd_S.:C5 -0.007 -0.072 -0.013 0.014 -0.002 0.003 0.017 -0.142 0.145
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 -0.014
## Indvd_S.:C5 0.007 -0.005tab_model(modA.867,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.42 | 0.69 | 43.07 – 45.77 | 64.61 | <0.001 |
| Individualism Score c | -0.04 | 0.04 | -0.12 – 0.03 | -1.12 | 0.263 |
| C1 | 5.95 | 0.81 | 4.36 – 7.54 | 7.33 | <0.001 |
| C2 | 9.55 | 1.20 | 7.20 – 11.90 | 7.97 | <0.001 |
| C3 | -7.77 | 1.27 | -10.27 – -5.28 | -6.11 | <0.001 |
| C4 | 8.38 | 1.44 | 5.56 – 11.21 | 5.82 | <0.001 |
| C5 | 2.52 | 1.23 | 0.11 – 4.94 | 2.05 | 0.041 |
|
Individualism Score c * C1 |
0.12 | 0.05 | 0.04 – 0.21 | 2.76 | 0.006 |
|
Individualism Score c * C2 |
0.07 | 0.07 | -0.06 – 0.20 | 1.03 | 0.302 |
|
Individualism Score c * C3 |
-0.01 | 0.07 | -0.16 – 0.13 | -0.20 | 0.838 |
|
Individualism Score c * C4 |
0.04 | 0.08 | -0.11 – 0.20 | 0.55 | 0.582 |
|
Individualism Score c * C5 |
0.03 | 0.07 | -0.11 – 0.17 | 0.44 | 0.661 |
| Random Effects | |||||
| σ2 | 455.03 | ||||
| τ00 id | 329.93 | ||||
| ICC | 0.42 | ||||
| N id | 1002 | ||||
| Observations | 3315 | ||||
| Marginal R2 / Conditional R2 | 0.038 / 0.442 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
modA.8672 <- lmer(Risk ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8672)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27657
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8947 -0.5383 0.0168 0.5308 3.5116
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 244.8 15.65
## Residual 419.2 20.48
## Number of obs: 3000, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.647e+01 6.253e-01 1.011e+03 74.321
## Individualism_Score.c -2.839e-02 3.486e-02 1.015e+03 -0.814
## Naturalness.c -4.316e-01 2.092e-02 2.651e+03 -20.624
## C1 3.145e+00 8.207e-01 2.348e+03 3.832
## C2 6.520e-01 1.238e+00 2.319e+03 0.527
## C3 -9.642e+00 1.226e+00 2.313e+03 -7.867
## C4 2.950e+00 1.404e+00 2.331e+03 2.101
## C5 1.427e+01 1.438e+00 2.320e+03 9.923
## Individualism_Score.c:Naturalness.c -2.441e-03 1.067e-03 2.687e+03 -2.288
## Individualism_Score.c:C1 6.253e-02 4.543e-02 2.334e+03 1.376
## Individualism_Score.c:C2 -9.387e-02 6.785e-02 2.314e+03 -1.383
## Individualism_Score.c:C3 -1.666e-02 6.923e-02 2.332e+03 -0.241
## Individualism_Score.c:C4 -3.840e-02 7.643e-02 2.314e+03 -0.502
## Individualism_Score.c:C5 2.531e-01 7.999e-02 2.303e+03 3.165
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 0.41556
## Naturalness.c < 2e-16 ***
## C1 0.00013 ***
## C2 0.59833
## C3 5.55e-15 ***
## C4 0.03578 *
## C5 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.02219 *
## Individualism_Score.c:C1 0.16883
## Individualism_Score.c:C2 0.16665
## Individualism_Score.c:C3 0.80987
## Individualism_Score.c:C4 0.61541
## Individualism_Score.c:C5 0.00157 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8672,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 46.47 | 0.63 | 45.25 – 47.70 | 74.32 | <0.001 |
| Individualism Score c | -0.03 | 0.03 | -0.10 – 0.04 | -0.81 | 0.415 |
| Naturalness c | -0.43 | 0.02 | -0.47 – -0.39 | -20.62 | <0.001 |
| C1 | 3.15 | 0.82 | 1.54 – 4.75 | 3.83 | <0.001 |
| C2 | 0.65 | 1.24 | -1.77 – 3.08 | 0.53 | 0.598 |
| C3 | -9.64 | 1.23 | -12.04 – -7.24 | -7.87 | <0.001 |
| C4 | 2.95 | 1.40 | 0.20 – 5.70 | 2.10 | 0.036 |
| C5 | 14.27 | 1.44 | 11.45 – 17.09 | 9.92 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -2.29 | 0.022 |
|
Individualism Score c * C1 |
0.06 | 0.05 | -0.03 – 0.15 | 1.38 | 0.169 |
|
Individualism Score c * C2 |
-0.09 | 0.07 | -0.23 – 0.04 | -1.38 | 0.167 |
|
Individualism Score c * C3 |
-0.02 | 0.07 | -0.15 – 0.12 | -0.24 | 0.810 |
|
Individualism Score c * C4 |
-0.04 | 0.08 | -0.19 – 0.11 | -0.50 | 0.615 |
|
Individualism Score c * C5 |
0.25 | 0.08 | 0.10 – 0.41 | 3.16 | 0.002 |
| Random Effects | |||||
| σ2 | 419.25 | ||||
| τ00 id | 244.81 | ||||
| ICC | 0.37 | ||||
| N id | 1002 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.160 / 0.470 | ||||
Poltical Ideology
Q.1 (POLITICAL IDEOLOGY) How does ideology predict risk perception, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.868 <- lmer(Risk ~ Ideology.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.868)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c * C1 +
## Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 + Ideology.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30882.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9482 -0.5595 0.0105 0.5087 3.0211
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 329.5 18.15
## Residual 455.0 21.33
## Number of obs: 3315, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 42.6633 1.0114 1014.0100 42.182 < 2e-16 ***
## Ideology.c -1.4921 0.6331 1013.4054 -2.357 0.0186 *
## C1 7.0961 1.1995 2648.2067 5.916 3.72e-09 ***
## C2 9.3195 1.7206 2587.9694 5.416 6.64e-08 ***
## C3 -8.6900 1.8965 2591.2096 -4.582 4.82e-06 ***
## C4 10.7389 2.1422 2612.6326 5.013 5.72e-07 ***
## C5 3.4771 1.8154 2409.4216 1.915 0.0556 .
## Ideology.c:C1 1.0001 0.7492 2640.7091 1.335 0.1820
## Ideology.c:C2 -0.1763 1.0800 2590.4255 -0.163 0.8703
## Ideology.c:C3 -0.7612 1.2021 2600.9249 -0.633 0.5266
## Ideology.c:C4 2.0588 1.3398 2603.2195 1.537 0.1245
## Ideology.c:C5 0.7955 1.1101 2398.8010 0.717 0.4737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.734
## C1 -0.053 -0.046
## C2 0.021 0.016 0.031
## C3 0.014 0.012 -0.049 0.013
## C4 0.005 0.001 -0.004 0.005 -0.017
## C5 -0.069 -0.048 -0.142 0.137 0.012 -0.004
## Idelgy.c:C1 -0.046 -0.063 0.736 0.024 -0.044 0.002 -0.099
## Idelgy.c:C2 0.016 0.033 0.025 0.718 0.012 0.002 0.096 0.053
## Idelgy.c:C3 0.012 0.018 -0.044 0.012 0.741 -0.006 0.013 -0.062 0.014
## Idelgy.c:C4 0.001 -0.004 0.002 0.003 -0.006 0.740 -0.003 0.013 0.000
## Idelgy.c:C5 -0.049 -0.059 -0.101 0.098 0.013 -0.003 0.736 -0.123 0.123
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.003
## Idelgy.c:C5 0.019 -0.005tab_model(modA.868,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 42.66 | 1.01 | 40.68 – 44.65 | 42.18 | <0.001 |
| Ideology c | -1.49 | 0.63 | -2.73 – -0.25 | -2.36 | 0.018 |
| C1 | 7.10 | 1.20 | 4.74 – 9.45 | 5.92 | <0.001 |
| C2 | 9.32 | 1.72 | 5.95 – 12.69 | 5.42 | <0.001 |
| C3 | -8.69 | 1.90 | -12.41 – -4.97 | -4.58 | <0.001 |
| C4 | 10.74 | 2.14 | 6.54 – 14.94 | 5.01 | <0.001 |
| C5 | 3.48 | 1.82 | -0.08 – 7.04 | 1.92 | 0.056 |
| Ideology c * C1 | 1.00 | 0.75 | -0.47 – 2.47 | 1.33 | 0.182 |
| Ideology c * C2 | -0.18 | 1.08 | -2.29 – 1.94 | -0.16 | 0.870 |
| Ideology c * C3 | -0.76 | 1.20 | -3.12 – 1.60 | -0.63 | 0.527 |
| Ideology c * C4 | 2.06 | 1.34 | -0.57 – 4.69 | 1.54 | 0.124 |
| Ideology c * C5 | 0.80 | 1.11 | -1.38 – 2.97 | 0.72 | 0.474 |
| Random Effects | |||||
| σ2 | 455.05 | ||||
| τ00 id | 329.55 | ||||
| ICC | 0.42 | ||||
| N id | 1002 | ||||
| Observations | 3315 | ||||
| Marginal R2 / Conditional R2 | 0.039 / 0.443 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting risk perception, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8683 <- lmer(Risk ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.8683)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Risk ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27625
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8739 -0.5324 0.0169 0.5291 3.3675
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 247.4 15.73
## Residual 419.4 20.48
## Number of obs: 3000, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 45.27451 0.92177 1008.43183 49.117 < 2e-16 ***
## Ideology.c -1.06131 0.57487 995.94491 -1.846 0.06516 .
## Naturalness.c -0.47584 0.02916 2587.19234 -16.316 < 2e-16 ***
## C1 3.42849 1.20218 2344.03357 2.852 0.00438 **
## C2 0.02094 1.77443 2303.52650 0.012 0.99059
## C3 -11.09105 1.82378 2313.37410 -6.081 1.39e-09 ***
## C4 4.17562 2.08360 2343.65391 2.004 0.04518 *
## C5 15.61290 2.10467 2322.53769 7.418 1.66e-13 ***
## Ideology.c:Naturalness.c -0.03557 0.02048 2594.69487 -1.737 0.08252 .
## Ideology.c:C1 0.23569 0.74313 2336.06580 0.317 0.75115
## Ideology.c:C2 -0.65203 1.12170 2316.29023 -0.581 0.56110
## Ideology.c:C3 -1.14920 1.15489 2327.37221 -0.995 0.31981
## Ideology.c:C4 1.08960 1.29837 2329.84989 0.839 0.40144
## Ideology.c:C5 1.36637 1.32663 2330.62028 1.030 0.30314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8683,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 45.27 | 0.92 | 43.47 – 47.08 | 49.12 | <0.001 |
| Ideology c | -1.06 | 0.57 | -2.19 – 0.07 | -1.85 | 0.065 |
| Naturalness c | -0.48 | 0.03 | -0.53 – -0.42 | -16.32 | <0.001 |
| C1 | 3.43 | 1.20 | 1.07 – 5.79 | 2.85 | 0.004 |
| C2 | 0.02 | 1.77 | -3.46 – 3.50 | 0.01 | 0.991 |
| C3 | -11.09 | 1.82 | -14.67 – -7.52 | -6.08 | <0.001 |
| C4 | 4.18 | 2.08 | 0.09 – 8.26 | 2.00 | 0.045 |
| C5 | 15.61 | 2.10 | 11.49 – 19.74 | 7.42 | <0.001 |
|
Ideology c * Naturalness c |
-0.04 | 0.02 | -0.08 – 0.00 | -1.74 | 0.083 |
| Ideology c * C1 | 0.24 | 0.74 | -1.22 – 1.69 | 0.32 | 0.751 |
| Ideology c * C2 | -0.65 | 1.12 | -2.85 – 1.55 | -0.58 | 0.561 |
| Ideology c * C3 | -1.15 | 1.15 | -3.41 – 1.12 | -1.00 | 0.320 |
| Ideology c * C4 | 1.09 | 1.30 | -1.46 – 3.64 | 0.84 | 0.401 |
| Ideology c * C5 | 1.37 | 1.33 | -1.23 – 3.97 | 1.03 | 0.303 |
| Random Effects | |||||
| σ2 | 419.38 | ||||
| τ00 id | 247.44 | ||||
| ICC | 0.37 | ||||
| N id | 1002 | ||||
| Observations | 3000 | ||||
| Marginal R2 / Conditional R2 | 0.159 / 0.471 | ||||
Benefit
Q.1 How do burger contrasts predict benefit perception?
modA.870 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.870)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27808
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3345 -0.4773 0.0739 0.5582 2.6462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 238.7 15.45
## Residual 463.2 21.52
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.3899 0.6270 999.7844 96.314 < 2e-16 ***
## C1 -6.6494 0.8415 2369.0189 -7.901 4.18e-15 ***
## C2 -7.1582 1.2462 2352.7388 -5.744 1.04e-08 ***
## C3 6.3360 1.2802 2351.7235 4.949 7.98e-07 ***
## C4 -10.4003 1.4478 2368.4324 -7.184 9.05e-13 ***
## C5 -4.9473 1.4516 2347.8427 -3.408 0.000665 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.007
## C2 -0.006 -0.014
## C3 0.016 -0.033 0.002
## C4 0.006 -0.004 0.005 -0.017
## C5 0.016 0.023 -0.032 0.010 -0.008tab_model(modA.870,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.39 | 0.63 | 59.16 – 61.62 | 96.31 | <0.001 |
| C1 | -6.65 | 0.84 | -8.30 – -5.00 | -7.90 | <0.001 |
| C2 | -7.16 | 1.25 | -9.60 – -4.71 | -5.74 | <0.001 |
| C3 | 6.34 | 1.28 | 3.83 – 8.85 | 4.95 | <0.001 |
| C4 | -10.40 | 1.45 | -13.24 – -7.56 | -7.18 | <0.001 |
| C5 | -4.95 | 1.45 | -7.79 – -2.10 | -3.41 | 0.001 |
| Random Effects | |||||
| σ2 | 463.15 | ||||
| τ00 id | 238.71 | ||||
| ICC | 0.34 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.044 / 0.369 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict benefit perception, over and above burger contrasts?
modA.871 <- lmer(Ben ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.871)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c *
## C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c *
## C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27764.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4916 -0.4548 0.0585 0.5531 2.7820
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 242.1 15.56
## Residual 451.8 21.26
## Number of obs: 2995, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.41959 0.62707 996.96682 96.352 < 2e-16 ***
## ATNS_Score.c 0.08527 0.03613 997.50909 2.360 0.018474 *
## C1 -6.68979 0.83298 2354.50789 -8.031 1.51e-15 ***
## C2 -7.38690 1.23360 2340.50035 -5.988 2.45e-09 ***
## C3 6.24779 1.26622 2337.08652 4.934 8.61e-07 ***
## C4 -10.51941 1.43259 2352.30909 -7.343 2.87e-13 ***
## C5 -4.91757 1.43796 2333.42405 -3.420 0.000637 ***
## ATNS_Score.c:C1 -0.27120 0.04794 2346.60486 -5.657 1.73e-08 ***
## ATNS_Score.c:C2 -0.26238 0.07232 2346.50049 -3.628 0.000292 ***
## ATNS_Score.c:C3 0.05283 0.07314 2323.84905 0.722 0.470228
## ATNS_Score.c:C4 -0.12979 0.08115 2361.24732 -1.599 0.109878
## ATNS_Score.c:C5 -0.16402 0.08285 2345.54996 -1.980 0.047854 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c -0.001
## C1 -0.007 -0.002
## C2 -0.007 0.008 -0.016
## C3 0.015 -0.003 -0.032 0.002
## C4 0.006 -0.009 -0.004 0.005 -0.017
## C5 0.017 -0.023 0.025 -0.034 0.010 -0.008
## ATNS_Sc.:C1 -0.002 0.002 0.002 0.021 0.005 0.013 -0.034
## ATNS_Sc.:C2 0.008 0.005 0.021 0.001 -0.001 -0.003 0.042 0.009
## ATNS_Sc.:C3 -0.004 0.030 0.005 -0.001 -0.008 0.019 -0.001 -0.066
## ATNS_Sc.:C4 -0.009 0.020 0.014 -0.003 0.019 0.006 -0.005 -0.027
## ATNS_Sc.:C5 -0.023 0.010 -0.035 0.042 -0.002 -0.005 -0.018 0.015
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.001
## ATNS_Sc.:C4 0.005 -0.044
## ATNS_Sc.:C5 -0.016 0.016 -0.009tab_model(modA.871,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.42 | 0.63 | 59.19 – 61.65 | 96.35 | <0.001 |
| ATNS Score c | 0.09 | 0.04 | 0.01 – 0.16 | 2.36 | 0.018 |
| C1 | -6.69 | 0.83 | -8.32 – -5.06 | -8.03 | <0.001 |
| C2 | -7.39 | 1.23 | -9.81 – -4.97 | -5.99 | <0.001 |
| C3 | 6.25 | 1.27 | 3.77 – 8.73 | 4.93 | <0.001 |
| C4 | -10.52 | 1.43 | -13.33 – -7.71 | -7.34 | <0.001 |
| C5 | -4.92 | 1.44 | -7.74 – -2.10 | -3.42 | 0.001 |
| ATNS Score c * C1 | -0.27 | 0.05 | -0.37 – -0.18 | -5.66 | <0.001 |
| ATNS Score c * C2 | -0.26 | 0.07 | -0.40 – -0.12 | -3.63 | <0.001 |
| ATNS Score c * C3 | 0.05 | 0.07 | -0.09 – 0.20 | 0.72 | 0.470 |
| ATNS Score c * C4 | -0.13 | 0.08 | -0.29 – 0.03 | -1.60 | 0.110 |
| ATNS Score c * C5 | -0.16 | 0.08 | -0.33 – -0.00 | -1.98 | 0.048 |
| Random Effects | |||||
| σ2 | 451.84 | ||||
| τ00 id | 242.09 | ||||
| ICC | 0.35 | ||||
| N id | 1002 | ||||
| Observations | 2995 | ||||
| Marginal R2 / Conditional R2 | 0.059 / 0.387 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8715 <- lmer(Ben ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8715)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27484.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7987 -0.4419 0.0565 0.5626 2.8521
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 256.5 16.01
## Residual 395.1 19.88
## Number of obs: 2993, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.913e+01 6.283e-01 1.022e+03 94.104 < 2e-16
## ATNS_Score.c 8.229e-02 3.637e-02 1.038e+03 2.263 0.023860
## Naturalness.c 3.300e-01 1.985e-02 2.577e+03 16.623 < 2e-16
## C1 -3.901e+00 8.007e-01 2.326e+03 -4.872 1.18e-06
## C2 -1.705e+00 1.209e+00 2.304e+03 -1.411 0.158416
## C3 7.921e+00 1.195e+00 2.300e+03 6.630 4.16e-11
## C4 -6.357e+00 1.368e+00 2.310e+03 -4.646 3.57e-06
## C5 -1.149e+01 1.404e+00 2.305e+03 -8.183 4.51e-16
## ATNS_Score.c:Naturalness.c 1.306e-03 9.767e-04 2.561e+03 1.337 0.181278
## ATNS_Score.c:C1 -2.359e-01 4.745e-02 2.329e+03 -4.972 7.11e-07
## ATNS_Score.c:C2 -9.792e-02 7.135e-02 2.308e+03 -1.372 0.170058
## ATNS_Score.c:C3 3.530e-02 6.893e-02 2.286e+03 0.512 0.608603
## ATNS_Score.c:C4 -7.323e-02 7.790e-02 2.310e+03 -0.940 0.347320
## ATNS_Score.c:C5 -2.999e-01 7.956e-02 2.311e+03 -3.770 0.000167
##
## (Intercept) ***
## ATNS_Score.c *
## Naturalness.c ***
## C1 ***
## C2
## C3 ***
## C4 ***
## C5 ***
## ATNS_Score.c:Naturalness.c
## ATNS_Score.c:C1 ***
## ATNS_Score.c:C2
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8715,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.13 | 0.63 | 57.89 – 60.36 | 94.10 | <0.001 |
| ATNS Score c | 0.08 | 0.04 | 0.01 – 0.15 | 2.26 | 0.024 |
| Naturalness c | 0.33 | 0.02 | 0.29 – 0.37 | 16.62 | <0.001 |
| C1 | -3.90 | 0.80 | -5.47 – -2.33 | -4.87 | <0.001 |
| C2 | -1.71 | 1.21 | -4.08 – 0.66 | -1.41 | 0.158 |
| C3 | 7.92 | 1.19 | 5.58 – 10.26 | 6.63 | <0.001 |
| C4 | -6.36 | 1.37 | -9.04 – -3.67 | -4.65 | <0.001 |
| C5 | -11.49 | 1.40 | -14.24 – -8.73 | -8.18 | <0.001 |
|
ATNS Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.34 | 0.181 |
| ATNS Score c * C1 | -0.24 | 0.05 | -0.33 – -0.14 | -4.97 | <0.001 |
| ATNS Score c * C2 | -0.10 | 0.07 | -0.24 – 0.04 | -1.37 | 0.170 |
| ATNS Score c * C3 | 0.04 | 0.07 | -0.10 – 0.17 | 0.51 | 0.609 |
| ATNS Score c * C4 | -0.07 | 0.08 | -0.23 – 0.08 | -0.94 | 0.347 |
| ATNS Score c * C5 | -0.30 | 0.08 | -0.46 – -0.14 | -3.77 | <0.001 |
| Random Effects | |||||
| σ2 | 395.12 | ||||
| τ00 id | 256.45 | ||||
| ICC | 0.39 | ||||
| N id | 1002 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.128 / 0.471 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict benefit perception, over and above burger contrasts?
modA.872 <- lmer(Ben ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.872)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c * C1 +
## AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 + AW_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27692.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5825 -0.4604 0.0842 0.5545 2.7977
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 210.7 14.51
## Residual 454.8 21.33
## Number of obs: 2994, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.47189 0.60252 997.62684 100.365 < 2e-16 ***
## AW_Score.c 0.22074 0.02458 1001.19976 8.982 < 2e-16 ***
## C1 -6.63963 0.83224 2384.08392 -7.978 2.29e-15 ***
## C2 -7.31359 1.23221 2369.35255 -5.935 3.36e-09 ***
## C3 6.14752 1.26610 2368.56315 4.855 1.28e-06 ***
## C4 -10.26514 1.43312 2383.24318 -7.163 1.05e-12 ***
## C5 -5.29376 1.43605 2362.36081 -3.686 0.000233 ***
## AW_Score.c:C1 0.07963 0.03386 2361.88687 2.352 0.018771 *
## AW_Score.c:C2 -0.13032 0.05070 2389.97391 -2.571 0.010213 *
## AW_Score.c:C3 0.29175 0.05217 2389.86979 5.592 2.49e-08 ***
## AW_Score.c:C4 -0.10907 0.05818 2387.73367 -1.875 0.060939 .
## AW_Score.c:C5 -0.08246 0.05831 2354.48057 -1.414 0.157453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c -0.002
## C1 -0.008 0.015
## C2 -0.007 -0.001 -0.014
## C3 0.015 0.007 -0.031 0.002
## C4 0.007 -0.018 -0.006 0.004 -0.018
## C5 0.016 -0.012 0.022 -0.030 0.009 -0.007
## AW_Scr.c:C1 0.015 -0.007 -0.006 -0.001 -0.016 0.027 -0.017
## AW_Scr.c:C2 -0.001 -0.001 -0.001 0.021 -0.004 0.004 0.025 -0.007
## AW_Scr.c:C3 0.007 0.022 -0.016 -0.004 -0.020 0.030 0.000 -0.045 -0.002
## AW_Scr.c:C4 -0.018 0.035 0.028 0.004 0.030 -0.036 -0.001 -0.044 0.003
## AW_Scr.c:C5 -0.012 0.013 -0.017 0.025 0.000 -0.002 0.023 0.018 -0.027
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 -0.069
## AW_Scr.c:C5 0.005 -0.014tab_model(modA.872,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.47 | 0.60 | 59.29 – 61.65 | 100.37 | <0.001 |
| AW Score c | 0.22 | 0.02 | 0.17 – 0.27 | 8.98 | <0.001 |
| C1 | -6.64 | 0.83 | -8.27 – -5.01 | -7.98 | <0.001 |
| C2 | -7.31 | 1.23 | -9.73 – -4.90 | -5.94 | <0.001 |
| C3 | 6.15 | 1.27 | 3.67 – 8.63 | 4.86 | <0.001 |
| C4 | -10.27 | 1.43 | -13.08 – -7.46 | -7.16 | <0.001 |
| C5 | -5.29 | 1.44 | -8.11 – -2.48 | -3.69 | <0.001 |
| AW Score c * C1 | 0.08 | 0.03 | 0.01 – 0.15 | 2.35 | 0.019 |
| AW Score c * C2 | -0.13 | 0.05 | -0.23 – -0.03 | -2.57 | 0.010 |
| AW Score c * C3 | 0.29 | 0.05 | 0.19 – 0.39 | 5.59 | <0.001 |
| AW Score c * C4 | -0.11 | 0.06 | -0.22 – 0.00 | -1.87 | 0.061 |
| AW Score c * C5 | -0.08 | 0.06 | -0.20 – 0.03 | -1.41 | 0.157 |
| Random Effects | |||||
| σ2 | 454.84 | ||||
| τ00 id | 210.68 | ||||
| ICC | 0.32 | ||||
| N id | 1001 | ||||
| Observations | 2994 | ||||
| Marginal R2 / Conditional R2 | 0.094 / 0.381 | ||||
Q.2 (ANIMAL WELFARE) How does concern for animal welfare depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8724 <- lmer(Ben ~ AW_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8724)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Ben ~ AW_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c *
## C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 +
## AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27429.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8667 -0.4451 0.0681 0.5506 3.2147
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 225.7 15.02
## Residual 400.5 20.01
## Number of obs: 2992, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.916e+01 6.049e-01 1.020e+03 97.797 < 2e-16 ***
## AW_Score.c 2.135e-01 2.472e-02 1.027e+03 8.638 < 2e-16 ***
## Naturalness.c 3.250e-01 1.966e-02 2.635e+03 16.532 < 2e-16 ***
## C1 -3.980e+00 8.038e-01 2.353e+03 -4.952 7.88e-07 ***
## C2 -1.766e+00 1.212e+00 2.327e+03 -1.456 0.1454
## C3 7.816e+00 1.202e+00 2.329e+03 6.505 9.50e-11 ***
## C4 -6.234e+00 1.373e+00 2.336e+03 -4.541 5.88e-06 ***
## C5 -1.170e+01 1.407e+00 2.331e+03 -8.312 < 2e-16 ***
## AW_Score.c:Naturalness.c 3.468e-05 7.320e-04 2.722e+03 0.047 0.9622
## AW_Score.c:C1 5.960e-02 3.354e-02 2.352e+03 1.777 0.0757 .
## AW_Score.c:C2 -5.311e-02 4.944e-02 2.345e+03 -1.074 0.2828
## AW_Score.c:C3 2.384e-01 4.995e-02 2.351e+03 4.772 1.93e-06 ***
## AW_Score.c:C4 -9.570e-02 5.609e-02 2.344e+03 -1.706 0.0881 .
## AW_Score.c:C5 -1.264e-01 5.691e-02 2.318e+03 -2.220 0.0265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8724,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.16 | 0.60 | 57.98 – 60.35 | 97.80 | <0.001 |
| AW Score c | 0.21 | 0.02 | 0.17 – 0.26 | 8.64 | <0.001 |
| Naturalness c | 0.33 | 0.02 | 0.29 – 0.36 | 16.53 | <0.001 |
| C1 | -3.98 | 0.80 | -5.56 – -2.40 | -4.95 | <0.001 |
| C2 | -1.77 | 1.21 | -4.14 – 0.61 | -1.46 | 0.145 |
| C3 | 7.82 | 1.20 | 5.46 – 10.17 | 6.50 | <0.001 |
| C4 | -6.23 | 1.37 | -8.93 – -3.54 | -4.54 | <0.001 |
| C5 | -11.70 | 1.41 | -14.46 – -8.94 | -8.31 | <0.001 |
|
AW Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.05 | 0.962 |
| AW Score c * C1 | 0.06 | 0.03 | -0.01 – 0.13 | 1.78 | 0.076 |
| AW Score c * C2 | -0.05 | 0.05 | -0.15 – 0.04 | -1.07 | 0.283 |
| AW Score c * C3 | 0.24 | 0.05 | 0.14 – 0.34 | 4.77 | <0.001 |
| AW Score c * C4 | -0.10 | 0.06 | -0.21 – 0.01 | -1.71 | 0.088 |
| AW Score c * C5 | -0.13 | 0.06 | -0.24 – -0.01 | -2.22 | 0.026 |
| Random Effects | |||||
| σ2 | 400.47 | ||||
| τ00 id | 225.66 | ||||
| ICC | 0.36 | ||||
| N id | 1001 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.158 / 0.462 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict benefit perception, over and above burger contrasts?
modA.873 <- lmer(Ben ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.873)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c * C1 +
## CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c * C4 +
## CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27784.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4974 -0.4622 0.0834 0.5589 2.9600
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 235.1 15.33
## Residual 459.9 21.44
## Number of obs: 2995, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.39991 0.62356 997.18765 96.863 < 2e-16 ***
## CNS_Score.c 0.12900 0.05015 996.21347 2.572 0.010242 *
## C1 -6.76397 0.83897 2364.31950 -8.062 1.18e-15 ***
## C2 -7.21498 1.24333 2348.83694 -5.803 7.40e-09 ***
## C3 6.24361 1.27579 2347.07994 4.894 1.06e-06 ***
## C4 -10.30120 1.44337 2362.13514 -7.137 1.26e-12 ***
## C5 -4.87976 1.44664 2343.68917 -3.373 0.000755 ***
## CNS_Score.c:C1 0.05523 0.06726 2346.90663 0.821 0.411651
## CNS_Score.c:C2 -0.25117 0.10285 2354.74746 -2.442 0.014671 *
## CNS_Score.c:C3 0.29437 0.10254 2340.80817 2.871 0.004130 **
## CNS_Score.c:C4 -0.38651 0.11605 2375.20341 -3.330 0.000880 ***
## CNS_Score.c:C5 0.01463 0.11327 2343.13273 0.129 0.897207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c 0.000
## C1 -0.007 0.006
## C2 -0.005 0.018 -0.013
## C3 0.015 0.004 -0.032 0.001
## C4 0.006 -0.009 -0.004 0.005 -0.017
## C5 0.016 0.006 0.023 -0.032 0.010 -0.007
## CNS_Scr.:C1 0.006 -0.006 0.001 0.029 -0.010 0.015 0.013
## CNS_Scr.:C2 0.017 0.030 0.027 0.044 -0.003 0.002 -0.008 0.054
## CNS_Scr.:C3 0.004 0.017 -0.010 -0.003 -0.006 0.017 0.003 -0.038
## CNS_Scr.:C4 -0.009 -0.011 0.015 0.002 0.017 -0.012 0.003 0.031
## CNS_Scr.:C5 0.006 -0.009 0.013 -0.007 0.002 0.002 -0.022 -0.020
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 -0.002
## CNS_Scr.:C4 0.006 0.006
## CNS_Scr.:C5 0.011 -0.001 -0.012tab_model(modA.873,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.40 | 0.62 | 59.18 – 61.62 | 96.86 | <0.001 |
| CNS Score c | 0.13 | 0.05 | 0.03 – 0.23 | 2.57 | 0.010 |
| C1 | -6.76 | 0.84 | -8.41 – -5.12 | -8.06 | <0.001 |
| C2 | -7.21 | 1.24 | -9.65 – -4.78 | -5.80 | <0.001 |
| C3 | 6.24 | 1.28 | 3.74 – 8.75 | 4.89 | <0.001 |
| C4 | -10.30 | 1.44 | -13.13 – -7.47 | -7.14 | <0.001 |
| C5 | -4.88 | 1.45 | -7.72 – -2.04 | -3.37 | 0.001 |
| CNS Score c * C1 | 0.06 | 0.07 | -0.08 – 0.19 | 0.82 | 0.412 |
| CNS Score c * C2 | -0.25 | 0.10 | -0.45 – -0.05 | -2.44 | 0.015 |
| CNS Score c * C3 | 0.29 | 0.10 | 0.09 – 0.50 | 2.87 | 0.004 |
| CNS Score c * C4 | -0.39 | 0.12 | -0.61 – -0.16 | -3.33 | 0.001 |
| CNS Score c * C5 | 0.01 | 0.11 | -0.21 – 0.24 | 0.13 | 0.897 |
| Random Effects | |||||
| σ2 | 459.85 | ||||
| τ00 id | 235.10 | ||||
| ICC | 0.34 | ||||
| N id | 1002 | ||||
| Observations | 2995 | ||||
| Marginal R2 / Conditional R2 | 0.054 / 0.374 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8736 <- lmer(Ben ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8736)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27505.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6696 -0.4515 0.0581 0.5623 2.9372
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 249.7 15.80
## Residual 402.4 20.06
## Number of obs: 2993, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.907e+01 6.247e-01 1.022e+03 94.553 < 2e-16 ***
## CNS_Score.c 8.547e-02 5.061e-02 1.047e+03 1.689 0.09153 .
## Naturalness.c 3.245e-01 1.985e-02 2.607e+03 16.345 < 2e-16 ***
## C1 -4.015e+00 8.059e-01 2.333e+03 -4.982 6.75e-07 ***
## C2 -1.538e+00 1.219e+00 2.312e+03 -1.262 0.20718
## C3 7.872e+00 1.204e+00 2.309e+03 6.538 7.63e-11 ***
## C4 -6.117e+00 1.379e+00 2.318e+03 -4.437 9.55e-06 ***
## C5 -1.136e+01 1.415e+00 2.316e+03 -8.033 1.50e-15 ***
## CNS_Score.c:Naturalness.c 2.932e-03 1.326e-03 2.570e+03 2.211 0.02713 *
## CNS_Score.c:C1 1.616e-01 6.710e-02 2.331e+03 2.408 0.01612 *
## CNS_Score.c:C2 -9.911e-02 1.017e-01 2.336e+03 -0.974 0.33007
## CNS_Score.c:C3 3.068e-01 9.694e-02 2.294e+03 3.165 0.00157 **
## CNS_Score.c:C4 -2.338e-01 1.121e-01 2.331e+03 -2.085 0.03721 *
## CNS_Score.c:C5 -7.311e-02 1.094e-01 2.295e+03 -0.669 0.50387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8736,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.07 | 0.62 | 57.85 – 60.30 | 94.55 | <0.001 |
| CNS Score c | 0.09 | 0.05 | -0.01 – 0.18 | 1.69 | 0.091 |
| Naturalness c | 0.32 | 0.02 | 0.29 – 0.36 | 16.35 | <0.001 |
| C1 | -4.01 | 0.81 | -5.60 – -2.43 | -4.98 | <0.001 |
| C2 | -1.54 | 1.22 | -3.93 – 0.85 | -1.26 | 0.207 |
| C3 | 7.87 | 1.20 | 5.51 – 10.23 | 6.54 | <0.001 |
| C4 | -6.12 | 1.38 | -8.82 – -3.41 | -4.44 | <0.001 |
| C5 | -11.36 | 1.41 | -14.14 – -8.59 | -8.03 | <0.001 |
|
CNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 2.21 | 0.027 |
| CNS Score c * C1 | 0.16 | 0.07 | 0.03 – 0.29 | 2.41 | 0.016 |
| CNS Score c * C2 | -0.10 | 0.10 | -0.30 – 0.10 | -0.97 | 0.330 |
| CNS Score c * C3 | 0.31 | 0.10 | 0.12 – 0.50 | 3.17 | 0.002 |
| CNS Score c * C4 | -0.23 | 0.11 | -0.45 – -0.01 | -2.08 | 0.037 |
| CNS Score c * C5 | -0.07 | 0.11 | -0.29 – 0.14 | -0.67 | 0.504 |
| Random Effects | |||||
| σ2 | 402.43 | ||||
| τ00 id | 249.73 | ||||
| ICC | 0.38 | ||||
| N id | 1002 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.122 / 0.459 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict benefit perception, over and above burger contrasts?
modA.874 <- lmer(Ben ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.874)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27606.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7649 -0.4646 0.0874 0.5765 2.9377
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 200.1 14.14
## Residual 448.0 21.17
## Number of obs: 2992, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.041e+01 5.918e-01 9.974e+02 102.082 < 2e-16 ***
## CCBelief_Score.c 2.658e-01 2.541e-02 9.991e+02 10.458 < 2e-16 ***
## C1 -6.593e+00 8.251e-01 2.391e+03 -7.990 2.07e-15 ***
## C2 -7.351e+00 1.222e+00 2.374e+03 -6.014 2.09e-09 ***
## C3 6.217e+00 1.255e+00 2.372e+03 4.955 7.73e-07 ***
## C4 -1.059e+01 1.420e+00 2.390e+03 -7.455 1.25e-13 ***
## C5 -5.267e+00 1.424e+00 2.370e+03 -3.699 0.000222 ***
## CCBelief_Score.c:C1 1.406e-01 3.517e-02 2.335e+03 3.999 6.55e-05 ***
## CCBelief_Score.c:C2 -1.683e-01 5.338e-02 2.392e+03 -3.152 0.001642 **
## CCBelief_Score.c:C3 4.280e-01 5.484e-02 2.396e+03 7.804 8.86e-15 ***
## CCBelief_Score.c:C4 -5.265e-03 6.071e-02 2.402e+03 -0.087 0.930907
## CCBelief_Score.c:C5 1.891e-02 5.991e-02 2.373e+03 0.316 0.752265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c -0.001
## C1 -0.007 0.009
## C2 -0.007 -0.009 -0.014
## C3 0.016 0.002 -0.031 0.002
## C4 0.005 0.009 -0.003 0.004 -0.015
## C5 0.017 -0.012 0.023 -0.031 0.009 -0.007
## CCBlf_S.:C1 0.009 -0.013 -0.003 -0.013 -0.003 -0.015 -0.017
## CCBlf_S.:C2 -0.009 0.018 -0.013 -0.003 0.000 0.003 0.019 0.029
## CCBlf_S.:C3 0.002 0.030 -0.003 0.001 -0.011 -0.014 0.005 -0.055
## CCBlf_S.:C4 0.009 0.002 -0.016 0.003 -0.015 -0.019 -0.007 0.002
## CCBlf_S.:C5 -0.012 0.020 -0.018 0.019 0.005 -0.007 0.029 0.031
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 -0.003
## CCBlf_S.:C4 0.009 -0.011
## CCBlf_S.:C5 -0.034 0.002 -0.001tab_model(modA.874,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.41 | 0.59 | 59.25 – 61.57 | 102.08 | <0.001 |
| CCBelief Score c | 0.27 | 0.03 | 0.22 – 0.32 | 10.46 | <0.001 |
| C1 | -6.59 | 0.83 | -8.21 – -4.98 | -7.99 | <0.001 |
| C2 | -7.35 | 1.22 | -9.75 – -4.95 | -6.01 | <0.001 |
| C3 | 6.22 | 1.25 | 3.76 – 8.68 | 4.96 | <0.001 |
| C4 | -10.59 | 1.42 | -13.37 – -7.80 | -7.46 | <0.001 |
| C5 | -5.27 | 1.42 | -8.06 – -2.48 | -3.70 | <0.001 |
| CCBelief Score c * C1 | 0.14 | 0.04 | 0.07 – 0.21 | 4.00 | <0.001 |
| CCBelief Score c * C2 | -0.17 | 0.05 | -0.27 – -0.06 | -3.15 | 0.002 |
| CCBelief Score c * C3 | 0.43 | 0.05 | 0.32 – 0.54 | 7.80 | <0.001 |
| CCBelief Score c * C4 | -0.01 | 0.06 | -0.12 – 0.11 | -0.09 | 0.931 |
| CCBelief Score c * C5 | 0.02 | 0.06 | -0.10 – 0.14 | 0.32 | 0.752 |
| Random Effects | |||||
| σ2 | 447.96 | ||||
| τ00 id | 200.08 | ||||
| ICC | 0.31 | ||||
| N id | 1001 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.116 / 0.389 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8746 <- lmer(Ben ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8746)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27345.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9313 -0.4362 0.0817 0.5550 3.5396
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 214.2 14.64
## Residual 395.0 19.87
## Number of obs: 2990, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.913e+01 5.937e-01 1.023e+03 99.587
## CCBelief_Score.c 2.658e-01 2.555e-02 1.030e+03 10.405
## Naturalness.c 3.264e-01 1.961e-02 2.650e+03 16.649
## C1 -3.906e+00 7.965e-01 2.361e+03 -4.904
## C2 -2.029e+00 1.203e+00 2.334e+03 -1.687
## C3 8.027e+00 1.192e+00 2.338e+03 6.734
## C4 -6.520e+00 1.362e+00 2.348e+03 -4.786
## C5 -1.150e+01 1.399e+00 2.348e+03 -8.218
## CCBelief_Score.c:Naturalness.c -1.572e-03 7.778e-04 2.750e+03 -2.021
## CCBelief_Score.c:C1 1.126e-01 3.486e-02 2.333e+03 3.230
## CCBelief_Score.c:C2 -1.098e-01 5.193e-02 2.335e+03 -2.115
## CCBelief_Score.c:C3 3.475e-01 5.267e-02 2.364e+03 6.597
## CCBelief_Score.c:C4 -1.344e-02 5.885e-02 2.390e+03 -0.228
## CCBelief_Score.c:C5 -4.451e-02 5.805e-02 2.307e+03 -0.767
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 1.00e-06 ***
## C2 0.09173 .
## C3 2.08e-11 ***
## C4 1.81e-06 ***
## C5 3.37e-16 ***
## CCBelief_Score.c:Naturalness.c 0.04333 *
## CCBelief_Score.c:C1 0.00125 **
## CCBelief_Score.c:C2 0.03455 *
## CCBelief_Score.c:C3 5.15e-11 ***
## CCBelief_Score.c:C4 0.81935
## CCBelief_Score.c:C5 0.44337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8746,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.13 | 0.59 | 57.96 – 60.29 | 99.59 | <0.001 |
| CCBelief Score c | 0.27 | 0.03 | 0.22 – 0.32 | 10.41 | <0.001 |
| Naturalness c | 0.33 | 0.02 | 0.29 – 0.36 | 16.65 | <0.001 |
| C1 | -3.91 | 0.80 | -5.47 – -2.34 | -4.90 | <0.001 |
| C2 | -2.03 | 1.20 | -4.39 – 0.33 | -1.69 | 0.092 |
| C3 | 8.03 | 1.19 | 5.69 – 10.36 | 6.73 | <0.001 |
| C4 | -6.52 | 1.36 | -9.19 – -3.85 | -4.79 | <0.001 |
| C5 | -11.50 | 1.40 | -14.24 – -8.75 | -8.22 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – -0.00 | -2.02 | 0.043 |
| CCBelief Score c * C1 | 0.11 | 0.03 | 0.04 – 0.18 | 3.23 | 0.001 |
| CCBelief Score c * C2 | -0.11 | 0.05 | -0.21 – -0.01 | -2.11 | 0.035 |
| CCBelief Score c * C3 | 0.35 | 0.05 | 0.24 – 0.45 | 6.60 | <0.001 |
| CCBelief Score c * C4 | -0.01 | 0.06 | -0.13 – 0.10 | -0.23 | 0.819 |
| CCBelief Score c * C5 | -0.04 | 0.06 | -0.16 – 0.07 | -0.77 | 0.443 |
| Random Effects | |||||
| σ2 | 394.99 | ||||
| τ00 id | 214.19 | ||||
| ICC | 0.35 | ||||
| N id | 1001 | ||||
| Observations | 2990 | ||||
| Marginal R2 / Conditional R2 | 0.178 / 0.467 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict the benefit, above and beyond burger contrasts?
modA.875 <- lmer(Ben ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.875)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c * C1 +
## DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 + DS_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27790.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4031 -0.4708 0.0721 0.5597 2.6545
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 239.0 15.46
## Residual 459.7 21.44
## Number of obs: 2994, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.40699 0.62691 997.23860 96.357 < 2e-16 ***
## DS_Score.c 0.07289 0.03012 997.85028 2.420 0.015713 *
## C1 -6.69731 0.83987 2363.19959 -7.974 2.36e-15 ***
## C2 -7.25453 1.24252 2345.33171 -5.839 6.00e-09 ***
## C3 6.19198 1.27703 2343.97924 4.849 1.32e-06 ***
## C4 -10.53288 1.44566 2359.50731 -7.286 4.34e-13 ***
## C5 -5.02249 1.44765 2338.99864 -3.469 0.000531 ***
## DS_Score.c:C1 -0.06535 0.04046 2377.35491 -1.615 0.106457
## DS_Score.c:C2 -0.19345 0.05906 2334.31980 -3.275 0.001072 **
## DS_Score.c:C3 0.09101 0.06128 2343.33498 1.485 0.137623
## DS_Score.c:C4 -0.10491 0.06910 2351.46348 -1.518 0.129112
## DS_Score.c:C5 -0.07978 0.07066 2347.35017 -1.129 0.258932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c 0.000
## C1 -0.008 0.001
## C2 -0.007 0.006 -0.015
## C3 0.015 -0.009 -0.032 0.002
## C4 0.005 -0.021 -0.003 0.005 -0.016
## C5 0.016 -0.011 0.023 -0.032 0.009 -0.008
## DS_Scr.c:C1 0.001 -0.004 -0.009 0.018 0.018 0.040 -0.017
## DS_Scr.c:C2 0.006 -0.022 0.018 0.007 -0.009 -0.003 0.020 -0.043
## DS_Scr.c:C3 -0.009 0.016 0.018 -0.009 -0.018 0.030 0.011 -0.034 0.007
## DS_Scr.c:C4 -0.021 0.018 0.039 -0.002 0.031 0.015 -0.005 -0.019 0.005
## DS_Scr.c:C5 -0.011 -0.008 -0.016 0.019 0.010 -0.006 0.000 -0.011 0.012
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 -0.045
## DS_Scr.c:C5 0.006 -0.008tab_model(modA.875,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.41 | 0.63 | 59.18 – 61.64 | 96.36 | <0.001 |
| DS Score c | 0.07 | 0.03 | 0.01 – 0.13 | 2.42 | 0.016 |
| C1 | -6.70 | 0.84 | -8.34 – -5.05 | -7.97 | <0.001 |
| C2 | -7.25 | 1.24 | -9.69 – -4.82 | -5.84 | <0.001 |
| C3 | 6.19 | 1.28 | 3.69 – 8.70 | 4.85 | <0.001 |
| C4 | -10.53 | 1.45 | -13.37 – -7.70 | -7.29 | <0.001 |
| C5 | -5.02 | 1.45 | -7.86 – -2.18 | -3.47 | 0.001 |
| DS Score c * C1 | -0.07 | 0.04 | -0.14 – 0.01 | -1.61 | 0.106 |
| DS Score c * C2 | -0.19 | 0.06 | -0.31 – -0.08 | -3.28 | 0.001 |
| DS Score c * C3 | 0.09 | 0.06 | -0.03 – 0.21 | 1.49 | 0.138 |
| DS Score c * C4 | -0.10 | 0.07 | -0.24 – 0.03 | -1.52 | 0.129 |
| DS Score c * C5 | -0.08 | 0.07 | -0.22 – 0.06 | -1.13 | 0.259 |
| Random Effects | |||||
| σ2 | 459.68 | ||||
| τ00 id | 239.05 | ||||
| ICC | 0.34 | ||||
| N id | 1001 | ||||
| Observations | 2994 | ||||
| Marginal R2 / Conditional R2 | 0.051 / 0.376 | ||||
Q.2 (DISGUST SENSITIVITY) How does sensitivity to disgust depend on naturalness perception in predicting the benefit, above and beyond burger contrasts?
modA.8758 <- lmer(Ben ~ DS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8758)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Ben ~ DS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c *
## C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 +
## DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27507.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6125 -0.4567 0.0661 0.5576 2.7724
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 250.8 15.84
## Residual 402.8 20.07
## Number of obs: 2992, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.905e+01 6.260e-01 1.023e+03 94.336 < 2e-16 ***
## DS_Score.c 8.667e-02 3.023e-02 1.042e+03 2.867 0.00423 **
## Naturalness.c 3.371e-01 1.951e-02 2.579e+03 17.282 < 2e-16 ***
## C1 -3.885e+00 8.074e-01 2.336e+03 -4.811 1.60e-06 ***
## C2 -1.673e+00 1.218e+00 2.314e+03 -1.373 0.16979
## C3 7.926e+00 1.205e+00 2.307e+03 6.576 5.95e-11 ***
## C4 -6.410e+00 1.380e+00 2.319e+03 -4.643 3.62e-06 ***
## C5 -1.169e+01 1.415e+00 2.314e+03 -8.261 2.40e-16 ***
## DS_Score.c:Naturalness.c -1.606e-03 8.395e-04 2.571e+03 -1.913 0.05587 .
## DS_Score.c:C1 -1.156e-01 4.062e-02 2.376e+03 -2.847 0.00445 **
## DS_Score.c:C2 -1.696e-01 5.774e-02 2.288e+03 -2.938 0.00334 **
## DS_Score.c:C3 5.949e-02 5.782e-02 2.307e+03 1.029 0.30361
## DS_Score.c:C4 -1.315e-01 6.626e-02 2.314e+03 -1.984 0.04737 *
## DS_Score.c:C5 -4.998e-02 6.885e-02 2.310e+03 -0.726 0.46795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8758,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.05 | 0.63 | 57.83 – 60.28 | 94.34 | <0.001 |
| DS Score c | 0.09 | 0.03 | 0.03 – 0.15 | 2.87 | 0.004 |
| Naturalness c | 0.34 | 0.02 | 0.30 – 0.38 | 17.28 | <0.001 |
| C1 | -3.88 | 0.81 | -5.47 – -2.30 | -4.81 | <0.001 |
| C2 | -1.67 | 1.22 | -4.06 – 0.72 | -1.37 | 0.170 |
| C3 | 7.93 | 1.21 | 5.56 – 10.29 | 6.58 | <0.001 |
| C4 | -6.41 | 1.38 | -9.12 – -3.70 | -4.64 | <0.001 |
| C5 | -11.69 | 1.41 | -14.46 – -8.91 | -8.26 | <0.001 |
|
DS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.91 | 0.056 |
| DS Score c * C1 | -0.12 | 0.04 | -0.20 – -0.04 | -2.85 | 0.004 |
| DS Score c * C2 | -0.17 | 0.06 | -0.28 – -0.06 | -2.94 | 0.003 |
| DS Score c * C3 | 0.06 | 0.06 | -0.05 – 0.17 | 1.03 | 0.304 |
| DS Score c * C4 | -0.13 | 0.07 | -0.26 – -0.00 | -1.98 | 0.047 |
| DS Score c * C5 | -0.05 | 0.07 | -0.18 – 0.09 | -0.73 | 0.468 |
| Random Effects | |||||
| σ2 | 402.82 | ||||
| τ00 id | 250.81 | ||||
| ICC | 0.38 | ||||
| N id | 1001 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.121 / 0.458 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict benefit perception, over and above burger contrasts?
modA.876 <- lmer(Ben ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)
summary(modA.876)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Ben ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c *
## C1 + Collectivism_Score.c * C2 + Collectivism_Score.c * C3 +
## Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27687
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7070 -0.4587 0.0853 0.5647 2.9400
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 193.8 13.92
## Residual 459.7 21.44
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.38912 0.58933 997.56178 102.471 < 2e-16 ***
## Collectivism_Score.c 0.32184 0.02885 999.33139 11.155 < 2e-16 ***
## C1 -6.63438 0.83387 2405.63492 -7.956 2.71e-15 ***
## C2 -7.16120 1.23507 2387.93954 -5.798 7.59e-09 ***
## C3 6.22686 1.26859 2386.15504 4.908 9.80e-07 ***
## C4 -10.24401 1.43435 2404.33788 -7.142 1.21e-12 ***
## C5 -5.02506 1.43922 2382.04675 -3.492 0.000489 ***
## Collectivism_Score.c:C1 -0.20336 0.04063 2367.11277 -5.005 5.98e-07 ***
## Collectivism_Score.c:C2 -0.11486 0.06158 2414.56542 -1.865 0.062277 .
## Collectivism_Score.c:C3 0.01649 0.06337 2415.19514 0.260 0.794751
## Collectivism_Score.c:C4 -0.01728 0.06843 2386.84394 -0.252 0.800689
## Collectivism_Score.c:C5 -0.03298 0.06997 2381.96147 -0.471 0.637457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. -0.001
## C1 -0.007 0.003
## C2 -0.007 -0.004 -0.014
## C3 0.017 -0.003 -0.032 0.002
## C4 0.007 0.008 -0.005 0.004 -0.016
## C5 0.017 -0.013 0.023 -0.031 0.009 -0.007
## Cllct_S.:C1 0.003 -0.003 0.005 -0.002 0.006 -0.013 -0.021
## Cllct_S.:C2 -0.004 0.008 -0.001 -0.009 -0.002 0.002 0.021 0.009
## Cllct_S.:C3 -0.003 0.044 0.006 -0.002 -0.011 -0.010 0.004 -0.073 0.004
## Cllct_S.:C4 0.008 -0.009 -0.013 0.002 -0.010 -0.002 -0.001 0.021 0.001
## Cllct_S.:C5 -0.014 0.005 -0.021 0.021 0.003 0.000 0.012 0.003 -0.009
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.008
## Cllct_S.:C5 0.005 -0.009tab_model(modA.876,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.39 | 0.59 | 59.23 – 61.54 | 102.47 | <0.001 |
| Collectivism Score c | 0.32 | 0.03 | 0.27 – 0.38 | 11.16 | <0.001 |
| C1 | -6.63 | 0.83 | -8.27 – -5.00 | -7.96 | <0.001 |
| C2 | -7.16 | 1.24 | -9.58 – -4.74 | -5.80 | <0.001 |
| C3 | 6.23 | 1.27 | 3.74 – 8.71 | 4.91 | <0.001 |
| C4 | -10.24 | 1.43 | -13.06 – -7.43 | -7.14 | <0.001 |
| C5 | -5.03 | 1.44 | -7.85 – -2.20 | -3.49 | <0.001 |
| Collectivism Score c * C1 | -0.20 | 0.04 | -0.28 – -0.12 | -5.01 | <0.001 |
| Collectivism Score c * C2 | -0.11 | 0.06 | -0.24 – 0.01 | -1.87 | 0.062 |
| Collectivism Score c * C3 | 0.02 | 0.06 | -0.11 – 0.14 | 0.26 | 0.795 |
| Collectivism Score c * C4 | -0.02 | 0.07 | -0.15 – 0.12 | -0.25 | 0.801 |
| Collectivism Score c * C5 | -0.03 | 0.07 | -0.17 – 0.10 | -0.47 | 0.637 |
| Random Effects | |||||
| σ2 | 459.69 | ||||
| τ00 id | 193.84 | ||||
| ICC | 0.30 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.110 / 0.374 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8766 <- lmer(Ben ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8766)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + Collectivism_Score.c * C1 + Collectivism_Score.c * C2 +
## Collectivism_Score.c * C3 + Collectivism_Score.c * C4 + Collectivism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27385.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9277 -0.4466 0.0832 0.5439 2.8752
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 207.0 14.39
## Residual 399.3 19.98
## Number of obs: 2994, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.901e+01 5.886e-01 1.025e+03 100.262
## Collectivism_Score.c 3.270e-01 2.870e-02 1.014e+03 11.393
## Naturalness.c 3.457e-01 1.939e-02 2.634e+03 17.827
## C1 -3.796e+00 8.005e-01 2.378e+03 -4.742
## C2 -1.325e+00 1.205e+00 2.345e+03 -1.100
## C3 7.958e+00 1.193e+00 2.344e+03 6.671
## C4 -5.919e+00 1.367e+00 2.359e+03 -4.331
## C5 -1.186e+01 1.402e+00 2.354e+03 -8.457
## Collectivism_Score.c:Naturalness.c -6.315e-05 8.310e-04 2.581e+03 -0.076
## Collectivism_Score.c:C1 -2.434e-01 3.920e-02 2.355e+03 -6.208
## Collectivism_Score.c:C2 -7.185e-02 6.029e-02 2.338e+03 -1.192
## Collectivism_Score.c:C3 3.479e-02 5.945e-02 2.367e+03 0.585
## Collectivism_Score.c:C4 -4.090e-02 6.539e-02 2.341e+03 -0.626
## Collectivism_Score.c:C5 -1.187e-01 6.810e-02 2.348e+03 -1.743
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 2.24e-06 ***
## C2 0.2715
## C3 3.15e-11 ***
## C4 1.55e-05 ***
## C5 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.9394
## Collectivism_Score.c:C1 6.31e-10 ***
## Collectivism_Score.c:C2 0.2335
## Collectivism_Score.c:C3 0.5585
## Collectivism_Score.c:C4 0.5317
## Collectivism_Score.c:C5 0.0815 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8766,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.01 | 0.59 | 57.86 – 60.16 | 100.26 | <0.001 |
| Collectivism Score c | 0.33 | 0.03 | 0.27 – 0.38 | 11.39 | <0.001 |
| Naturalness c | 0.35 | 0.02 | 0.31 – 0.38 | 17.83 | <0.001 |
| C1 | -3.80 | 0.80 | -5.37 – -2.23 | -4.74 | <0.001 |
| C2 | -1.33 | 1.21 | -3.69 – 1.04 | -1.10 | 0.271 |
| C3 | 7.96 | 1.19 | 5.62 – 10.30 | 6.67 | <0.001 |
| C4 | -5.92 | 1.37 | -8.60 – -3.24 | -4.33 | <0.001 |
| C5 | -11.86 | 1.40 | -14.61 – -9.11 | -8.46 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.08 | 0.939 |
| Collectivism Score c * C1 | -0.24 | 0.04 | -0.32 – -0.17 | -6.21 | <0.001 |
| Collectivism Score c * C2 | -0.07 | 0.06 | -0.19 – 0.05 | -1.19 | 0.233 |
| Collectivism Score c * C3 | 0.03 | 0.06 | -0.08 – 0.15 | 0.59 | 0.559 |
| Collectivism Score c * C4 | -0.04 | 0.07 | -0.17 – 0.09 | -0.63 | 0.532 |
| Collectivism Score c * C5 | -0.12 | 0.07 | -0.25 – 0.01 | -1.74 | 0.081 |
| Random Effects | |||||
| σ2 | 399.34 | ||||
| τ00 id | 207.04 | ||||
| ICC | 0.34 | ||||
| N id | 1003 | ||||
| Observations | 2994 | ||||
| Marginal R2 / Conditional R2 | 0.183 / 0.462 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict benefit perception, over and above burger contrasts?
modA.877 <- lmer(Ben ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.877)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Ben ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c *
## C1 + Individualism_Score.c * C2 + Individualism_Score.c *
## C3 + Individualism_Score.c * C4 + Individualism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27728.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6951 -0.4442 0.0853 0.5665 2.4734
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 216.1 14.7
## Residual 457.8 21.4
## Number of obs: 2995, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.41508 0.60753 997.05620 99.443 < 2e-16 ***
## Individualism_Score.c 0.26256 0.03384 998.74798 7.758 2.12e-14 ***
## C1 -6.69590 0.83503 2381.53816 -8.019 1.66e-15 ***
## C2 -7.22262 1.23660 2364.47386 -5.841 5.91e-09 ***
## C3 6.41410 1.27002 2363.58376 5.050 4.75e-07 ***
## C4 -10.49467 1.43611 2380.81287 -7.308 3.69e-13 ***
## C5 -4.94602 1.44111 2357.66851 -3.432 0.000609 ***
## Individualism_Score.c:C1 -0.23644 0.04634 2352.92423 -5.103 3.62e-07 ***
## Individualism_Score.c:C2 -0.08781 0.06872 2384.97820 -1.278 0.201444
## Individualism_Score.c:C3 0.14086 0.07181 2385.63347 1.961 0.049941 *
## Individualism_Score.c:C4 -0.19132 0.07834 2368.11359 -2.442 0.014667 *
## Individualism_Score.c:C5 -0.04716 0.08143 2360.21083 -0.579 0.562546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. 0.000
## C1 -0.007 -0.001
## C2 -0.007 0.005 -0.014
## C3 0.016 0.006 -0.033 0.002
## C4 0.007 -0.006 -0.005 0.004 -0.017
## C5 0.016 -0.013 0.023 -0.032 0.009 -0.007
## Indvd_S.:C1 -0.001 -0.002 0.004 0.013 -0.009 0.008 -0.023
## Indvd_S.:C2 0.005 -0.021 0.012 0.003 -0.006 0.002 0.021 -0.042
## Indvd_S.:C3 0.006 0.036 -0.009 -0.006 0.009 0.014 0.002 -0.065 0.002
## Indvd_S.:C4 -0.006 0.004 0.009 0.002 0.014 -0.010 -0.002 0.000 0.002
## Indvd_S.:C5 -0.013 0.006 -0.022 0.021 0.001 -0.002 -0.006 0.010 -0.010
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 -0.016
## Indvd_S.:C5 0.010 -0.006tab_model(modA.877,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.42 | 0.61 | 59.22 – 61.61 | 99.44 | <0.001 |
| Individualism Score c | 0.26 | 0.03 | 0.20 – 0.33 | 7.76 | <0.001 |
| C1 | -6.70 | 0.84 | -8.33 – -5.06 | -8.02 | <0.001 |
| C2 | -7.22 | 1.24 | -9.65 – -4.80 | -5.84 | <0.001 |
| C3 | 6.41 | 1.27 | 3.92 – 8.90 | 5.05 | <0.001 |
| C4 | -10.49 | 1.44 | -13.31 – -7.68 | -7.31 | <0.001 |
| C5 | -4.95 | 1.44 | -7.77 – -2.12 | -3.43 | 0.001 |
|
Individualism Score c * C1 |
-0.24 | 0.05 | -0.33 – -0.15 | -5.10 | <0.001 |
|
Individualism Score c * C2 |
-0.09 | 0.07 | -0.22 – 0.05 | -1.28 | 0.201 |
|
Individualism Score c * C3 |
0.14 | 0.07 | 0.00 – 0.28 | 1.96 | 0.050 |
|
Individualism Score c * C4 |
-0.19 | 0.08 | -0.34 – -0.04 | -2.44 | 0.015 |
|
Individualism Score c * C5 |
-0.05 | 0.08 | -0.21 – 0.11 | -0.58 | 0.563 |
| Random Effects | |||||
| σ2 | 457.82 | ||||
| τ00 id | 216.11 | ||||
| ICC | 0.32 | ||||
| N id | 1002 | ||||
| Observations | 2995 | ||||
| Marginal R2 / Conditional R2 | 0.082 / 0.376 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
modA.8775 <- lmer(Ben ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.8775)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27439
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7583 -0.4387 0.0788 0.5444 2.9155
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 231.4 15.21
## Residual 398.6 19.97
## Number of obs: 2993, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.916e+01 6.090e-01 1.025e+03 97.144
## Individualism_Score.c 2.423e-01 3.395e-02 1.029e+03 7.137
## Naturalness.c 3.144e-01 2.041e-02 2.650e+03 15.407
## C1 -3.989e+00 8.012e-01 2.353e+03 -4.978
## C2 -1.582e+00 1.209e+00 2.323e+03 -1.309
## C3 7.978e+00 1.196e+00 2.323e+03 6.670
## C4 -6.445e+00 1.370e+00 2.337e+03 -4.705
## C5 -1.162e+01 1.405e+00 2.329e+03 -8.268
## Individualism_Score.c:Naturalness.c 3.473e-03 1.040e-03 2.684e+03 3.339
## Individualism_Score.c:C1 -1.794e-01 4.434e-02 2.337e+03 -4.047
## Individualism_Score.c:C2 5.588e-02 6.625e-02 2.318e+03 0.843
## Individualism_Score.c:C3 1.504e-01 6.758e-02 2.345e+03 2.225
## Individualism_Score.c:C4 -1.218e-01 7.456e-02 2.320e+03 -1.634
## Individualism_Score.c:C5 -2.283e-01 7.812e-02 2.311e+03 -2.923
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 1.80e-12 ***
## Naturalness.c < 2e-16 ***
## C1 6.87e-07 ***
## C2 0.190790
## C3 3.18e-11 ***
## C4 2.69e-06 ***
## C5 2.26e-16 ***
## Individualism_Score.c:Naturalness.c 0.000854 ***
## Individualism_Score.c:C1 5.36e-05 ***
## Individualism_Score.c:C2 0.399115
## Individualism_Score.c:C3 0.026154 *
## Individualism_Score.c:C4 0.102471
## Individualism_Score.c:C5 0.003503 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.8775,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 59.16 | 0.61 | 57.97 – 60.35 | 97.14 | <0.001 |
| Individualism Score c | 0.24 | 0.03 | 0.18 – 0.31 | 7.14 | <0.001 |
| Naturalness c | 0.31 | 0.02 | 0.27 – 0.35 | 15.41 | <0.001 |
| C1 | -3.99 | 0.80 | -5.56 – -2.42 | -4.98 | <0.001 |
| C2 | -1.58 | 1.21 | -3.95 – 0.79 | -1.31 | 0.191 |
| C3 | 7.98 | 1.20 | 5.63 – 10.32 | 6.67 | <0.001 |
| C4 | -6.44 | 1.37 | -9.13 – -3.76 | -4.70 | <0.001 |
| C5 | -11.62 | 1.41 | -14.37 – -8.86 | -8.27 | <0.001 |
|
Individualism Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 3.34 | 0.001 |
|
Individualism Score c * C1 |
-0.18 | 0.04 | -0.27 – -0.09 | -4.05 | <0.001 |
|
Individualism Score c * C2 |
0.06 | 0.07 | -0.07 – 0.19 | 0.84 | 0.399 |
|
Individualism Score c * C3 |
0.15 | 0.07 | 0.02 – 0.28 | 2.23 | 0.026 |
|
Individualism Score c * C4 |
-0.12 | 0.07 | -0.27 – 0.02 | -1.63 | 0.102 |
|
Individualism Score c * C5 |
-0.23 | 0.08 | -0.38 – -0.08 | -2.92 | 0.003 |
| Random Effects | |||||
| σ2 | 398.65 | ||||
| τ00 id | 231.38 | ||||
| ICC | 0.37 | ||||
| N id | 1002 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.153 / 0.464 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does political ideology predict benefit perception, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.878 <- lmer(Ben ~ Ideology.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.878)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Ben ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c * C1 +
## Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 + Ideology.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27775.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3456 -0.4785 0.0749 0.5611 2.6320
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 236.7 15.39
## Residual 462.9 21.52
## Number of obs: 2995, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.7202 0.9202 995.9511 63.813 < 2e-16 ***
## Ideology.c -1.4407 0.5770 1000.2443 -2.497 0.01268 *
## C1 -7.0673 1.2380 2365.1005 -5.708 1.28e-08 ***
## C2 -8.1529 1.7902 2321.7554 -4.554 5.53e-06 ***
## C3 6.0454 1.9047 2347.5783 3.174 0.00152 **
## C4 -13.4940 2.1528 2375.5809 -6.268 4.33e-10 ***
## C5 -7.2923 2.1332 2352.3859 -3.418 0.00064 ***
## Ideology.c:C1 -0.4056 0.7772 2366.7235 -0.522 0.60178
## Ideology.c:C2 -0.8828 1.1286 2329.1417 -0.782 0.43415
## Ideology.c:C3 -0.2757 1.2075 2361.0886 -0.228 0.81940
## Ideology.c:C4 -2.5809 1.3466 2370.4806 -1.917 0.05540 .
## Ideology.c:C5 -2.0179 1.3265 2354.2689 -1.521 0.12835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.733
## C1 -0.022 -0.022
## C2 -0.017 -0.013 -0.031
## C3 0.018 0.017 -0.039 0.004
## C4 0.007 0.001 -0.005 0.005 -0.018
## C5 0.012 0.014 0.010 -0.027 0.021 -0.004
## Idelgy.c:C1 -0.022 -0.026 0.733 -0.023 -0.034 0.002 0.014
## Idelgy.c:C2 -0.013 -0.009 -0.023 0.718 0.002 0.002 -0.027 -0.018
## Idelgy.c:C3 0.017 0.026 -0.034 0.003 0.741 -0.006 0.024 -0.048 0.000
## Idelgy.c:C4 0.001 -0.005 0.002 0.002 -0.006 0.740 -0.002 0.011 0.002
## Idelgy.c:C5 0.014 0.030 0.014 -0.027 0.024 -0.002 0.733 0.039 -0.051
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.003
## Idelgy.c:C5 0.035 -0.005tab_model(modA.878,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 58.72 | 0.92 | 56.92 – 60.52 | 63.81 | <0.001 |
| Ideology c | -1.44 | 0.58 | -2.57 – -0.31 | -2.50 | 0.013 |
| C1 | -7.07 | 1.24 | -9.49 – -4.64 | -5.71 | <0.001 |
| C2 | -8.15 | 1.79 | -11.66 – -4.64 | -4.55 | <0.001 |
| C3 | 6.05 | 1.90 | 2.31 – 9.78 | 3.17 | 0.002 |
| C4 | -13.49 | 2.15 | -17.72 – -9.27 | -6.27 | <0.001 |
| C5 | -7.29 | 2.13 | -11.48 – -3.11 | -3.42 | 0.001 |
| Ideology c * C1 | -0.41 | 0.78 | -1.93 – 1.12 | -0.52 | 0.602 |
| Ideology c * C2 | -0.88 | 1.13 | -3.10 – 1.33 | -0.78 | 0.434 |
| Ideology c * C3 | -0.28 | 1.21 | -2.64 – 2.09 | -0.23 | 0.819 |
| Ideology c * C4 | -2.58 | 1.35 | -5.22 – 0.06 | -1.92 | 0.055 |
| Ideology c * C5 | -2.02 | 1.33 | -4.62 – 0.58 | -1.52 | 0.128 |
| Random Effects | |||||
| σ2 | 462.91 | ||||
| τ00 id | 236.74 | ||||
| ICC | 0.34 | ||||
| N id | 1002 | ||||
| Observations | 2995 | ||||
| Marginal R2 / Conditional R2 | 0.049 / 0.371 | ||||
Q.2 (POLITICAL ORIENTATION) How does political ideology depend on naturalness perception in predicting benefit perception, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.8784 <- lmer(Ben ~ Ideology.c*Naturalness + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.8784)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Ben ~ Ideology.c * Naturalness + C1 + C2 + C3 + C4 + C5 + Ideology.c *
## C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 +
## Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27487.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5697 -0.4507 0.0668 0.5653 2.8882
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 248.7 15.77
## Residual 405.9 20.15
## Number of obs: 2993, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.36047 1.70309 2936.24820 23.698 < 2e-16 ***
## Ideology.c -2.71144 1.13374 2966.33366 -2.392 0.01684 *
## Naturalness 0.35907 0.02876 2573.00013 12.483 < 2e-16 ***
## C1 -3.50076 1.18452 2336.39896 -2.955 0.00315 **
## C2 -2.14200 1.74825 2296.78430 -1.225 0.22062
## C3 8.03991 1.79598 2310.16778 4.477 7.95e-06 ***
## C4 -8.67029 2.05280 2337.39411 -4.224 2.50e-05 ***
## C5 -13.80107 2.07748 2321.76188 -6.643 3.81e-11 ***
## Ideology.c:Naturalness 0.02133 0.02022 2582.70457 1.055 0.29152
## Ideology.c:C1 0.27317 0.73269 2327.28150 0.373 0.70931
## Ideology.c:C2 -0.58816 1.10656 2304.49042 -0.532 0.59511
## Ideology.c:C3 -0.02312 1.13740 2322.74213 -0.020 0.98378
## Ideology.c:C4 -2.02994 1.27870 2324.39575 -1.587 0.11254
## Ideology.c:C5 -1.91926 1.31765 2341.39970 -1.457 0.14537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.8784,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 40.36 | 1.70 | 37.02 – 43.70 | 23.70 | <0.001 |
| Ideology c | -2.71 | 1.13 | -4.93 – -0.49 | -2.39 | 0.017 |
| Naturalness | 0.36 | 0.03 | 0.30 – 0.42 | 12.48 | <0.001 |
| C1 | -3.50 | 1.18 | -5.82 – -1.18 | -2.96 | 0.003 |
| C2 | -2.14 | 1.75 | -5.57 – 1.29 | -1.23 | 0.221 |
| C3 | 8.04 | 1.80 | 4.52 – 11.56 | 4.48 | <0.001 |
| C4 | -8.67 | 2.05 | -12.70 – -4.65 | -4.22 | <0.001 |
| C5 | -13.80 | 2.08 | -17.87 – -9.73 | -6.64 | <0.001 |
| Ideology c * Naturalness | 0.02 | 0.02 | -0.02 – 0.06 | 1.06 | 0.292 |
| Ideology c * C1 | 0.27 | 0.73 | -1.16 – 1.71 | 0.37 | 0.709 |
| Ideology c * C2 | -0.59 | 1.11 | -2.76 – 1.58 | -0.53 | 0.595 |
| Ideology c * C3 | -0.02 | 1.14 | -2.25 – 2.21 | -0.02 | 0.984 |
| Ideology c * C4 | -2.03 | 1.28 | -4.54 – 0.48 | -1.59 | 0.113 |
| Ideology c * C5 | -1.92 | 1.32 | -4.50 – 0.66 | -1.46 | 0.145 |
| Random Effects | |||||
| σ2 | 405.87 | ||||
| τ00 id | 248.70 | ||||
| ICC | 0.38 | ||||
| N id | 1002 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.119 / 0.454 | ||||
Difference Benefit - Risk
Q.1 How do burger contrasts predict benefit perception?
modA.910 <- lmer(BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.910)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30825.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3908 -0.5246 -0.0378 0.5578 2.8665
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 367.7 19.18
## Residual 1452.6 38.11
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.6984 0.9238 1000.3810 16.993 < 2e-16 ***
## C1 -13.5195 1.4614 2515.2585 -9.251 < 2e-16 ***
## C2 -15.5764 2.1646 2491.4027 -7.196 8.17e-13 ***
## C3 13.8468 2.2252 2488.0575 6.223 5.72e-10 ***
## C4 -19.4601 2.5134 2511.0443 -7.742 1.40e-14 ***
## C5 -10.2906 2.5228 2484.1148 -4.079 4.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.009
## C2 -0.008 -0.013
## C3 0.019 -0.030 0.001
## C4 0.007 -0.006 0.003 -0.014
## C5 0.019 0.023 -0.029 0.007 -0.005tab_model(modA.910,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.70 | 0.92 | 13.89 – 17.51 | 16.99 | <0.001 |
| C1 | -13.52 | 1.46 | -16.38 – -10.65 | -9.25 | <0.001 |
| C2 | -15.58 | 2.16 | -19.82 – -11.33 | -7.20 | <0.001 |
| C3 | 13.85 | 2.23 | 9.48 – 18.21 | 6.22 | <0.001 |
| C4 | -19.46 | 2.51 | -24.39 – -14.53 | -7.74 | <0.001 |
| C5 | -10.29 | 2.52 | -15.24 – -5.34 | -4.08 | <0.001 |
| Random Effects | |||||
| σ2 | 1452.65 | ||||
| τ00 id | 367.70 | ||||
| ICC | 0.20 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.068 / 0.257 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict the benefit-risk difference, over and above burger contrasts?
modA.911 <- lmer(BRDiff ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.911)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c *
## C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c *
## C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30759.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5491 -0.5115 -0.0423 0.5478 2.8317
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 381.2 19.52
## Residual 1406.9 37.51
## Number of obs: 2992, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.76734 0.92349 998.60462 17.074 < 2e-16 ***
## ATNS_Score.c -0.09794 0.05321 999.12063 -1.841 0.0660 .
## C1 -13.54603 1.44244 2496.47810 -9.391 < 2e-16 ***
## C2 -16.09451 2.13633 2475.13561 -7.534 6.87e-14 ***
## C3 13.71578 2.19471 2469.20631 6.249 4.84e-10 ***
## C4 -19.59827 2.48025 2490.64747 -7.902 4.09e-15 ***
## C5 -9.89615 2.49189 2465.28120 -3.971 7.35e-05 ***
## ATNS_Score.c:C1 -0.55524 0.08303 2484.85376 -6.687 2.80e-11 ***
## ATNS_Score.c:C2 -0.50208 0.12520 2483.03677 -4.010 6.24e-05 ***
## ATNS_Score.c:C3 0.02337 0.12682 2451.55100 0.184 0.8538
## ATNS_Score.c:C4 -0.25608 0.14040 2502.04622 -1.824 0.0683 .
## ATNS_Score.c:C5 -0.36076 0.14346 2480.76123 -2.515 0.0120 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c -0.002
## C1 -0.008 -0.003
## C2 -0.009 0.010 -0.015
## C3 0.018 -0.005 -0.030 0.001
## C4 0.006 -0.011 -0.005 0.003 -0.014
## C5 0.020 -0.026 0.024 -0.031 0.007 -0.006
## ATNS_Sc.:C1 -0.003 0.002 0.000 0.019 0.006 0.014 -0.033
## ATNS_Sc.:C2 0.010 0.006 0.019 0.002 -0.001 -0.002 0.039 0.008
## ATNS_Sc.:C3 -0.005 0.035 0.006 -0.001 -0.008 0.018 -0.001 -0.061
## ATNS_Sc.:C4 -0.011 0.024 0.014 -0.002 0.018 0.007 -0.004 -0.028
## ATNS_Sc.:C5 -0.026 0.012 -0.034 0.040 -0.001 -0.004 -0.019 0.014
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.001
## ATNS_Sc.:C4 0.004 -0.040
## ATNS_Sc.:C5 -0.015 0.012 -0.006tab_model(modA.911,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.77 | 0.92 | 13.96 – 17.58 | 17.07 | <0.001 |
| ATNS Score c | -0.10 | 0.05 | -0.20 – 0.01 | -1.84 | 0.066 |
| C1 | -13.55 | 1.44 | -16.37 – -10.72 | -9.39 | <0.001 |
| C2 | -16.09 | 2.14 | -20.28 – -11.91 | -7.53 | <0.001 |
| C3 | 13.72 | 2.19 | 9.41 – 18.02 | 6.25 | <0.001 |
| C4 | -19.60 | 2.48 | -24.46 – -14.74 | -7.90 | <0.001 |
| C5 | -9.90 | 2.49 | -14.78 – -5.01 | -3.97 | <0.001 |
| ATNS Score c * C1 | -0.56 | 0.08 | -0.72 – -0.39 | -6.69 | <0.001 |
| ATNS Score c * C2 | -0.50 | 0.13 | -0.75 – -0.26 | -4.01 | <0.001 |
| ATNS Score c * C3 | 0.02 | 0.13 | -0.23 – 0.27 | 0.18 | 0.854 |
| ATNS Score c * C4 | -0.26 | 0.14 | -0.53 – 0.02 | -1.82 | 0.068 |
| ATNS Score c * C5 | -0.36 | 0.14 | -0.64 – -0.08 | -2.51 | 0.012 |
| Random Effects | |||||
| σ2 | 1406.93 | ||||
| τ00 id | 381.20 | ||||
| ICC | 0.21 | ||||
| N id | 1002 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.088 / 0.283 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9114 <- lmer(BRDiff ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9114)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30205.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0587 -0.5054 -0.0215 0.5335 3.3643
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 327.3 18.09
## Residual 1159.4 34.05
## Number of obs: 2991, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.276e+01 8.560e-01 1.033e+03 14.902 < 2e-16
## ATNS_Score.c -1.109e-01 4.964e-02 1.054e+03 -2.235 0.02564
## Naturalness.c 7.722e-01 3.259e-02 2.805e+03 23.696 < 2e-16
## C1 -6.989e+00 1.339e+00 2.502e+03 -5.221 1.92e-07
## C2 -2.142e+00 2.023e+00 2.471e+03 -1.059 0.28966
## C3 1.746e+01 2.001e+00 2.464e+03 8.724 < 2e-16
## C4 -9.725e+00 2.289e+00 2.481e+03 -4.248 2.24e-05
## C5 -2.538e+01 2.349e+00 2.471e+03 -10.806 < 2e-16
## ATNS_Score.c:Naturalness.c 4.507e-03 1.605e-03 2.789e+03 2.808 0.00503
## ATNS_Score.c:C1 -4.349e-01 7.931e-02 2.504e+03 -5.484 4.58e-08
## ATNS_Score.c:C2 -8.877e-02 1.194e-01 2.478e+03 -0.744 0.45714
## ATNS_Score.c:C3 -2.052e-03 1.156e-01 2.444e+03 -0.018 0.98583
## ATNS_Score.c:C4 -9.923e-02 1.303e-01 2.481e+03 -0.761 0.44655
## ATNS_Score.c:C5 -6.898e-01 1.331e-01 2.481e+03 -5.183 2.35e-07
##
## (Intercept) ***
## ATNS_Score.c *
## Naturalness.c ***
## C1 ***
## C2
## C3 ***
## C4 ***
## C5 ***
## ATNS_Score.c:Naturalness.c **
## ATNS_Score.c:C1 ***
## ATNS_Score.c:C2
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9114,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.76 | 0.86 | 11.08 – 14.43 | 14.90 | <0.001 |
| ATNS Score c | -0.11 | 0.05 | -0.21 – -0.01 | -2.23 | 0.026 |
| Naturalness c | 0.77 | 0.03 | 0.71 – 0.84 | 23.70 | <0.001 |
| C1 | -6.99 | 1.34 | -9.61 – -4.36 | -5.22 | <0.001 |
| C2 | -2.14 | 2.02 | -6.11 – 1.82 | -1.06 | 0.290 |
| C3 | 17.46 | 2.00 | 13.54 – 21.39 | 8.72 | <0.001 |
| C4 | -9.72 | 2.29 | -14.21 – -5.24 | -4.25 | <0.001 |
| C5 | -25.38 | 2.35 | -29.99 – -20.78 | -10.81 | <0.001 |
|
ATNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 2.81 | 0.005 |
| ATNS Score c * C1 | -0.43 | 0.08 | -0.59 – -0.28 | -5.48 | <0.001 |
| ATNS Score c * C2 | -0.09 | 0.12 | -0.32 – 0.15 | -0.74 | 0.457 |
| ATNS Score c * C3 | -0.00 | 0.12 | -0.23 – 0.22 | -0.02 | 0.986 |
| ATNS Score c * C4 | -0.10 | 0.13 | -0.35 – 0.16 | -0.76 | 0.447 |
| ATNS Score c * C5 | -0.69 | 0.13 | -0.95 – -0.43 | -5.18 | <0.001 |
| Random Effects | |||||
| σ2 | 1159.36 | ||||
| τ00 id | 327.31 | ||||
| ICC | 0.22 | ||||
| N id | 1002 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.236 / 0.404 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict the benefit-risk difference, over and above burger contrasts?
modA.912 <- lmer(BRDiff ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.912)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c * C1 +
## AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 + AW_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30739.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5623 -0.5111 -0.0228 0.5420 3.0899
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 354.7 18.83
## Residual 1416.6 37.64
## Number of obs: 2991, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.83982 0.91097 997.71608 17.388 < 2e-16 ***
## AW_Score.c 0.17995 0.03717 1002.44064 4.841 1.49e-06 ***
## C1 -13.56709 1.44412 2509.18013 -9.395 < 2e-16 ***
## C2 -15.90971 2.13819 2487.74090 -7.441 1.37e-13 ***
## C3 13.49389 2.19860 2484.84917 6.137 9.73e-10 ***
## C4 -19.00409 2.48611 2504.84985 -7.644 2.98e-14 ***
## C5 -10.89712 2.49335 2478.68714 -4.370 1.29e-05 ***
## AW_Score.c:C1 0.13167 0.05882 2481.85284 2.238 0.0253 *
## AW_Score.c:C2 -0.36088 0.08790 2512.85662 -4.105 4.16e-05 ***
## AW_Score.c:C3 0.55372 0.09049 2510.39716 6.119 1.09e-09 ***
## AW_Score.c:C4 -0.21038 0.10089 2508.93036 -2.085 0.0372 *
## AW_Score.c:C5 -0.15320 0.10131 2468.61811 -1.512 0.1306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c -0.003
## C1 -0.009 0.017
## C2 -0.008 -0.001 -0.014
## C3 0.018 0.008 -0.029 0.001
## C4 0.008 -0.020 -0.007 0.003 -0.016
## C5 0.018 -0.014 0.022 -0.028 0.007 -0.005
## AW_Scr.c:C1 0.017 -0.008 -0.007 -0.001 -0.014 0.026 -0.016
## AW_Scr.c:C2 -0.001 -0.002 -0.001 0.019 -0.003 0.002 0.022 -0.006
## AW_Scr.c:C3 0.008 0.026 -0.014 -0.003 -0.020 0.028 0.000 -0.042 -0.001
## AW_Scr.c:C4 -0.020 0.040 0.027 0.002 0.028 -0.037 -0.001 -0.046 0.002
## AW_Scr.c:C5 -0.014 0.014 -0.016 0.022 0.000 -0.001 0.021 0.017 -0.024
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 -0.063
## AW_Scr.c:C5 0.004 -0.009tab_model(modA.912,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.84 | 0.91 | 14.05 – 17.63 | 17.39 | <0.001 |
| AW Score c | 0.18 | 0.04 | 0.11 – 0.25 | 4.84 | <0.001 |
| C1 | -13.57 | 1.44 | -16.40 – -10.74 | -9.39 | <0.001 |
| C2 | -15.91 | 2.14 | -20.10 – -11.72 | -7.44 | <0.001 |
| C3 | 13.49 | 2.20 | 9.18 – 17.80 | 6.14 | <0.001 |
| C4 | -19.00 | 2.49 | -23.88 – -14.13 | -7.64 | <0.001 |
| C5 | -10.90 | 2.49 | -15.79 – -6.01 | -4.37 | <0.001 |
| AW Score c * C1 | 0.13 | 0.06 | 0.02 – 0.25 | 2.24 | 0.025 |
| AW Score c * C2 | -0.36 | 0.09 | -0.53 – -0.19 | -4.11 | <0.001 |
| AW Score c * C3 | 0.55 | 0.09 | 0.38 – 0.73 | 6.12 | <0.001 |
| AW Score c * C4 | -0.21 | 0.10 | -0.41 – -0.01 | -2.09 | 0.037 |
| AW Score c * C5 | -0.15 | 0.10 | -0.35 – 0.05 | -1.51 | 0.131 |
| Random Effects | |||||
| σ2 | 1416.56 | ||||
| τ00 id | 354.66 | ||||
| ICC | 0.20 | ||||
| N id | 1001 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.277 | ||||
Q.2 (ANIMAL WELFARE) How does concern for animal welfare depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9124 <- lmer(BRDiff ~ AW_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9124)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ AW_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## AW_Score.c * C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c *
## C4 + AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30214.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3557 -0.4939 -0.0173 0.5245 3.4187
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 298.8 17.29
## Residual 1184.5 34.42
## Number of obs: 2990, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.273e+01 8.444e-01 1.031e+03 15.080 < 2e-16 ***
## AW_Score.c 1.640e-01 3.453e-02 1.041e+03 4.751 2.31e-06 ***
## Naturalness.c 7.741e-01 3.242e-02 2.841e+03 23.877 < 2e-16 ***
## C1 -7.186e+00 1.350e+00 2.523e+03 -5.322 1.11e-07 ***
## C2 -2.266e+00 2.039e+00 2.489e+03 -1.111 0.26647
## C3 1.732e+01 2.022e+00 2.488e+03 8.566 < 2e-16 ***
## C4 -9.443e+00 2.308e+00 2.501e+03 -4.091 4.43e-05 ***
## C5 -2.611e+01 2.366e+00 2.493e+03 -11.036 < 2e-16 ***
## AW_Score.c:Naturalness.c 1.481e-04 1.200e-03 2.912e+03 0.123 0.90174
## AW_Score.c:C1 8.466e-02 5.634e-02 2.516e+03 1.503 0.13306
## AW_Score.c:C2 -1.777e-01 8.304e-02 2.513e+03 -2.140 0.03242 *
## AW_Score.c:C3 4.289e-01 8.393e-02 2.516e+03 5.110 3.46e-07 ***
## AW_Score.c:C4 -1.727e-01 9.426e-02 2.509e+03 -1.832 0.06709 .
## AW_Score.c:C5 -2.524e-01 9.577e-02 2.475e+03 -2.636 0.00845 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9124,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.73 | 0.84 | 11.08 – 14.39 | 15.08 | <0.001 |
| AW Score c | 0.16 | 0.03 | 0.10 – 0.23 | 4.75 | <0.001 |
| Naturalness c | 0.77 | 0.03 | 0.71 – 0.84 | 23.88 | <0.001 |
| C1 | -7.19 | 1.35 | -9.83 – -4.54 | -5.32 | <0.001 |
| C2 | -2.27 | 2.04 | -6.26 – 1.73 | -1.11 | 0.266 |
| C3 | 17.32 | 2.02 | 13.36 – 21.29 | 8.57 | <0.001 |
| C4 | -9.44 | 2.31 | -13.97 – -4.92 | -4.09 | <0.001 |
| C5 | -26.11 | 2.37 | -30.75 – -21.47 | -11.04 | <0.001 |
|
AW Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.12 | 0.902 |
| AW Score c * C1 | 0.08 | 0.06 | -0.03 – 0.20 | 1.50 | 0.133 |
| AW Score c * C2 | -0.18 | 0.08 | -0.34 – -0.01 | -2.14 | 0.032 |
| AW Score c * C3 | 0.43 | 0.08 | 0.26 – 0.59 | 5.11 | <0.001 |
| AW Score c * C4 | -0.17 | 0.09 | -0.36 – 0.01 | -1.83 | 0.067 |
| AW Score c * C5 | -0.25 | 0.10 | -0.44 – -0.06 | -2.64 | 0.008 |
| Random Effects | |||||
| σ2 | 1184.51 | ||||
| τ00 id | 298.80 | ||||
| ICC | 0.20 | ||||
| N id | 1001 | ||||
| Observations | 2990 | ||||
| Marginal R2 / Conditional R2 | 0.237 / 0.390 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict the benefit-risk difference, over and above burger contrasts?
modA.913 <- lmer(BRDiff ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.913)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c *
## C1 + CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c *
## C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30781.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5145 -0.5055 -0.0332 0.5712 3.2165
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 359 18.95
## Residual 1436 37.89
## Number of obs: 2992, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.69653 0.91658 998.73371 17.125 < 2e-16 ***
## CNS_Score.c 0.28427 0.07369 997.34423 3.858 0.000122 ***
## C1 -13.72584 1.45352 2511.79196 -9.443 < 2e-16 ***
## C2 -15.60058 2.15446 2488.54373 -7.241 5.91e-13 ***
## C3 13.70912 2.21236 2484.47629 6.197 6.73e-10 ***
## C4 -19.31740 2.50007 2505.89854 -7.727 1.58e-14 ***
## C5 -10.08105 2.50831 2481.05607 -4.019 6.02e-05 ***
## CNS_Score.c:C1 0.15261 0.11659 2487.37216 1.309 0.190653
## CNS_Score.c:C2 -0.54043 0.17815 2496.54441 -3.034 0.002441 **
## CNS_Score.c:C3 0.58600 0.17778 2478.04343 3.296 0.000994 ***
## CNS_Score.c:C4 -0.60806 0.20079 2522.15290 -3.028 0.002484 **
## CNS_Score.c:C5 -0.06281 0.19638 2481.37202 -0.320 0.749131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c 0.000
## C1 -0.009 0.007
## C2 -0.007 0.021 -0.012
## C3 0.018 0.004 -0.030 0.001
## C4 0.007 -0.011 -0.005 0.003 -0.014
## C5 0.019 0.008 0.022 -0.029 0.007 -0.005
## CNS_Scr.:C1 0.007 -0.007 0.000 0.028 -0.008 0.015 0.012
## CNS_Scr.:C2 0.021 0.035 0.027 0.041 -0.002 0.001 -0.008 0.051
## CNS_Scr.:C3 0.004 0.020 -0.008 -0.002 -0.005 0.016 0.002 -0.034
## CNS_Scr.:C4 -0.011 -0.013 0.015 0.001 0.016 -0.013 0.002 0.027
## CNS_Scr.:C5 0.008 -0.011 0.013 -0.008 0.001 0.001 -0.021 -0.018
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 -0.001
## CNS_Scr.:C4 0.004 0.010
## CNS_Scr.:C5 0.012 -0.001 -0.008tab_model(modA.913,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.70 | 0.92 | 13.90 – 17.49 | 17.13 | <0.001 |
| CNS Score c | 0.28 | 0.07 | 0.14 – 0.43 | 3.86 | <0.001 |
| C1 | -13.73 | 1.45 | -16.58 – -10.88 | -9.44 | <0.001 |
| C2 | -15.60 | 2.15 | -19.82 – -11.38 | -7.24 | <0.001 |
| C3 | 13.71 | 2.21 | 9.37 – 18.05 | 6.20 | <0.001 |
| C4 | -19.32 | 2.50 | -24.22 – -14.42 | -7.73 | <0.001 |
| C5 | -10.08 | 2.51 | -15.00 – -5.16 | -4.02 | <0.001 |
| CNS Score c * C1 | 0.15 | 0.12 | -0.08 – 0.38 | 1.31 | 0.191 |
| CNS Score c * C2 | -0.54 | 0.18 | -0.89 – -0.19 | -3.03 | 0.002 |
| CNS Score c * C3 | 0.59 | 0.18 | 0.24 – 0.93 | 3.30 | 0.001 |
| CNS Score c * C4 | -0.61 | 0.20 | -1.00 – -0.21 | -3.03 | 0.002 |
| CNS Score c * C5 | -0.06 | 0.20 | -0.45 – 0.32 | -0.32 | 0.749 |
| Random Effects | |||||
| σ2 | 1435.88 | ||||
| τ00 id | 359.02 | ||||
| ICC | 0.20 | ||||
| N id | 1002 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.084 / 0.267 | ||||
Q.2 (CONNECTEDNESS TO NATURE) How does connectedness to nature depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9135 <- lmer(BRDiff ~ CNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9135)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## CNS_Score.c * C1 + CNS_Score.c * C2 + CNS_Score.c * C3 +
## CNS_Score.c * C4 + CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30233.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0811 -0.4919 -0.0188 0.5059 3.2210
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 305.8 17.49
## Residual 1188.6 34.48
## Number of obs: 2991, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.257e+01 8.488e-01 1.033e+03 14.807 < 2e-16 ***
## CNS_Score.c 1.952e-01 6.897e-02 1.066e+03 2.830 0.004744 **
## Naturalness.c 7.735e-01 3.259e-02 2.839e+03 23.739 < 2e-16 ***
## C1 -7.216e+00 1.350e+00 2.518e+03 -5.345 9.83e-08 ***
## C2 -1.711e+00 2.043e+00 2.489e+03 -0.837 0.402571
## C3 1.745e+01 2.021e+00 2.481e+03 8.633 < 2e-16 ***
## C4 -9.360e+00 2.312e+00 2.498e+03 -4.049 5.30e-05 ***
## C5 -2.543e+01 2.372e+00 2.492e+03 -10.723 < 2e-16 ***
## CNS_Score.c:Naturalness.c 5.008e-03 2.183e-03 2.802e+03 2.295 0.021825 *
## CNS_Score.c:C1 3.764e-01 1.124e-01 2.513e+03 3.349 0.000823 ***
## CNS_Score.c:C2 -2.343e-01 1.703e-01 2.520e+03 -1.376 0.169059
## CNS_Score.c:C3 6.039e-01 1.628e-01 2.463e+03 3.709 0.000213 ***
## CNS_Score.c:C4 -2.957e-01 1.878e-01 2.515e+03 -1.575 0.115452
## CNS_Score.c:C5 -2.214e-01 1.836e-01 2.465e+03 -1.206 0.228032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9135,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.57 | 0.85 | 10.90 – 14.23 | 14.81 | <0.001 |
| CNS Score c | 0.20 | 0.07 | 0.06 – 0.33 | 2.83 | 0.005 |
| Naturalness c | 0.77 | 0.03 | 0.71 – 0.84 | 23.74 | <0.001 |
| C1 | -7.22 | 1.35 | -9.86 – -4.57 | -5.35 | <0.001 |
| C2 | -1.71 | 2.04 | -5.72 – 2.30 | -0.84 | 0.403 |
| C3 | 17.45 | 2.02 | 13.48 – 21.41 | 8.63 | <0.001 |
| C4 | -9.36 | 2.31 | -13.89 – -4.83 | -4.05 | <0.001 |
| C5 | -25.43 | 2.37 | -30.08 – -20.78 | -10.72 | <0.001 |
|
CNS Score c * Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 2.29 | 0.022 |
| CNS Score c * C1 | 0.38 | 0.11 | 0.16 – 0.60 | 3.35 | 0.001 |
| CNS Score c * C2 | -0.23 | 0.17 | -0.57 – 0.10 | -1.38 | 0.169 |
| CNS Score c * C3 | 0.60 | 0.16 | 0.28 – 0.92 | 3.71 | <0.001 |
| CNS Score c * C4 | -0.30 | 0.19 | -0.66 – 0.07 | -1.57 | 0.115 |
| CNS Score c * C5 | -0.22 | 0.18 | -0.58 – 0.14 | -1.21 | 0.228 |
| Random Effects | |||||
| σ2 | 1188.63 | ||||
| τ00 id | 305.82 | ||||
| ICC | 0.20 | ||||
| N id | 1002 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.231 / 0.388 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict the benefit-risk difference, over and above burger contrasts?
modA.914 <- lmer(BRDiff ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.914)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## BRDiff ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30631.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6183 -0.5192 -0.0118 0.5367 3.0809
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 322.5 17.96
## Residual 1390.6 37.29
## Number of obs: 2989, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.572e+01 8.883e-01 9.967e+02 17.695 < 2e-16 ***
## CCBelief_Score.c 2.822e-01 3.816e-02 9.989e+02 7.395 2.98e-13 ***
## C1 -1.342e+01 1.428e+00 2.522e+03 -9.399 < 2e-16 ***
## C2 -1.598e+01 2.115e+00 2.499e+03 -7.552 5.97e-14 ***
## C3 1.364e+01 2.173e+00 2.494e+03 6.275 4.11e-10 ***
## C4 -1.980e+01 2.456e+00 2.517e+03 -8.059 1.18e-15 ***
## C5 -1.101e+01 2.466e+00 2.492e+03 -4.465 8.36e-06 ***
## CCBelief_Score.c:C1 3.082e-01 6.102e-02 2.453e+03 5.051 4.73e-07 ***
## CCBelief_Score.c:C2 -4.209e-01 9.232e-02 2.521e+03 -4.560 5.37e-06 ***
## CCBelief_Score.c:C3 7.815e-01 9.497e-02 2.522e+03 8.229 2.98e-16 ***
## CCBelief_Score.c:C4 -6.820e-02 1.050e-01 2.533e+03 -0.650 0.516
## CCBelief_Score.c:C5 7.337e-03 1.037e-01 2.497e+03 0.071 0.944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c -0.002
## C1 -0.009 0.010
## C2 -0.008 -0.010 -0.013
## C3 0.019 0.001 -0.029 0.001
## C4 0.006 0.010 -0.004 0.003 -0.012
## C5 0.019 -0.013 0.022 -0.029 0.007 -0.005
## CCBlf_S.:C1 0.010 -0.016 -0.003 -0.013 -0.001 -0.014 -0.016
## CCBlf_S.:C2 -0.010 0.020 -0.013 -0.003 0.000 0.002 0.018 0.027
## CCBlf_S.:C3 0.001 0.036 -0.001 0.000 -0.012 -0.014 0.003 -0.053
## CCBlf_S.:C4 0.010 0.003 -0.015 0.002 -0.014 -0.018 -0.005 0.001
## CCBlf_S.:C5 -0.013 0.023 -0.017 0.018 0.003 -0.005 0.027 0.029
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 -0.002
## CCBlf_S.:C4 0.006 -0.008
## CCBlf_S.:C5 -0.032 0.002 -0.001tab_model(modA.914,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.72 | 0.89 | 13.98 – 17.46 | 17.69 | <0.001 |
| CCBelief Score c | 0.28 | 0.04 | 0.21 – 0.36 | 7.40 | <0.001 |
| C1 | -13.42 | 1.43 | -16.22 – -10.62 | -9.40 | <0.001 |
| C2 | -15.98 | 2.12 | -20.12 – -11.83 | -7.55 | <0.001 |
| C3 | 13.64 | 2.17 | 9.38 – 17.90 | 6.28 | <0.001 |
| C4 | -19.80 | 2.46 | -24.61 – -14.98 | -8.06 | <0.001 |
| C5 | -11.01 | 2.47 | -15.85 – -6.18 | -4.47 | <0.001 |
| CCBelief Score c * C1 | 0.31 | 0.06 | 0.19 – 0.43 | 5.05 | <0.001 |
| CCBelief Score c * C2 | -0.42 | 0.09 | -0.60 – -0.24 | -4.56 | <0.001 |
| CCBelief Score c * C3 | 0.78 | 0.09 | 0.60 – 0.97 | 8.23 | <0.001 |
| CCBelief Score c * C4 | -0.07 | 0.10 | -0.27 – 0.14 | -0.65 | 0.516 |
| CCBelief Score c * C5 | 0.01 | 0.10 | -0.20 – 0.21 | 0.07 | 0.944 |
| Random Effects | |||||
| σ2 | 1390.59 | ||||
| τ00 id | 322.52 | ||||
| ICC | 0.19 | ||||
| N id | 1001 | ||||
| Observations | 2989 | ||||
| Marginal R2 / Conditional R2 | 0.122 / 0.287 | ||||
Q.2 (CLIMATE CHANGE BELIEF) How does climate change belief depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9145 <- lmer(BRDiff ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9145)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30105.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1440 -0.4937 -0.0093 0.5201 3.8154
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 271.2 16.47
## Residual 1162.9 34.10
## Number of obs: 2988, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.268e+01 8.231e-01 1.031e+03 15.411
## CCBelief_Score.c 2.660e-01 3.545e-02 1.040e+03 7.503
## Naturalness.c 7.670e-01 3.219e-02 2.856e+03 23.826
## C1 -7.137e+00 1.333e+00 2.535e+03 -5.353
## C2 -2.539e+00 2.016e+00 2.499e+03 -1.260
## C3 1.735e+01 1.999e+00 2.501e+03 8.676
## C4 -1.013e+01 2.282e+00 2.518e+03 -4.439
## C5 -2.619e+01 2.343e+00 2.515e+03 -11.178
## CCBelief_Score.c:Naturalness.c 1.297e-04 1.268e-03 2.929e+03 0.102
## CCBelief_Score.c:C1 2.961e-01 5.849e-02 2.491e+03 5.064
## CCBelief_Score.c:C2 -2.224e-01 8.703e-02 2.501e+03 -2.556
## CCBelief_Score.c:C3 6.411e-01 8.828e-02 2.535e+03 7.262
## CCBelief_Score.c:C4 -2.018e-02 9.832e-02 2.569e+03 -0.205
## CCBelief_Score.c:C5 -1.948e-01 9.749e-02 2.461e+03 -1.998
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 1.34e-13 ***
## Naturalness.c < 2e-16 ***
## C1 9.45e-08 ***
## C2 0.2079
## C3 < 2e-16 ***
## C4 9.41e-06 ***
## C5 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.9185
## CCBelief_Score.c:C1 4.42e-07 ***
## CCBelief_Score.c:C2 0.0107 *
## CCBelief_Score.c:C3 5.05e-13 ***
## CCBelief_Score.c:C4 0.8374
## CCBelief_Score.c:C5 0.0458 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9145,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.68 | 0.82 | 11.07 – 14.30 | 15.41 | <0.001 |
| CCBelief Score c | 0.27 | 0.04 | 0.20 – 0.34 | 7.50 | <0.001 |
| Naturalness c | 0.77 | 0.03 | 0.70 – 0.83 | 23.83 | <0.001 |
| C1 | -7.14 | 1.33 | -9.75 – -4.52 | -5.35 | <0.001 |
| C2 | -2.54 | 2.02 | -6.49 – 1.41 | -1.26 | 0.208 |
| C3 | 17.35 | 2.00 | 13.43 – 21.27 | 8.68 | <0.001 |
| C4 | -10.13 | 2.28 | -14.61 – -5.66 | -4.44 | <0.001 |
| C5 | -26.19 | 2.34 | -30.78 – -21.60 | -11.18 | <0.001 |
|
CCBelief Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.10 | 0.919 |
| CCBelief Score c * C1 | 0.30 | 0.06 | 0.18 – 0.41 | 5.06 | <0.001 |
| CCBelief Score c * C2 | -0.22 | 0.09 | -0.39 – -0.05 | -2.56 | 0.011 |
| CCBelief Score c * C3 | 0.64 | 0.09 | 0.47 – 0.81 | 7.26 | <0.001 |
| CCBelief Score c * C4 | -0.02 | 0.10 | -0.21 – 0.17 | -0.21 | 0.837 |
| CCBelief Score c * C5 | -0.19 | 0.10 | -0.39 – -0.00 | -2.00 | 0.046 |
| Random Effects | |||||
| σ2 | 1162.95 | ||||
| τ00 id | 271.21 | ||||
| ICC | 0.19 | ||||
| N id | 1001 | ||||
| Observations | 2988 | ||||
| Marginal R2 / Conditional R2 | 0.260 / 0.400 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict the benefit-risk difference, above and beyond burger contrasts?
modA.915 <- lmer(BRDiff ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.915)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c * C1 +
## DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 + DS_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30806.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4851 -0.5220 -0.0399 0.5488 2.8901
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 374.9 19.36
## Residual 1441.9 37.97
## Number of obs: 2991, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.572e+01 9.266e-01 9.982e+02 16.962 < 2e-16 ***
## DS_Score.c 9.471e-03 4.454e-02 9.999e+02 0.213 0.83163
## C1 -1.354e+01 1.459e+00 2.506e+03 -9.280 < 2e-16 ***
## C2 -1.579e+01 2.159e+00 2.480e+03 -7.314 3.48e-13 ***
## C3 1.370e+01 2.221e+00 2.477e+03 6.169 7.98e-10 ***
## C4 -1.965e+01 2.511e+00 2.499e+03 -7.826 7.38e-15 ***
## C5 -1.036e+01 2.517e+00 2.472e+03 -4.116 3.98e-05 ***
## DS_Score.c:C1 -1.603e-01 7.028e-02 2.528e+03 -2.280 0.02268 *
## DS_Score.c:C2 -3.129e-01 1.028e-01 2.466e+03 -3.045 0.00235 **
## DS_Score.c:C3 4.689e-02 1.066e-01 2.477e+03 0.440 0.65995
## DS_Score.c:C4 -6.730e-02 1.200e-01 2.488e+03 -0.561 0.57506
## DS_Score.c:C5 -2.065e-01 1.230e-01 2.481e+03 -1.679 0.09324 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c 0.000
## C1 -0.010 0.003
## C2 -0.008 0.007 -0.014
## C3 0.018 -0.012 -0.030 0.002
## C4 0.006 -0.025 -0.004 0.003 -0.013
## C5 0.018 -0.014 0.022 -0.029 0.006 -0.005
## DS_Scr.c:C1 0.003 -0.004 -0.006 0.014 0.018 0.037 -0.018
## DS_Scr.c:C2 0.007 -0.027 0.014 0.009 -0.006 -0.002 0.020 -0.042
## DS_Scr.c:C3 -0.012 0.019 0.018 -0.006 -0.019 0.031 0.008 -0.032 0.005
## DS_Scr.c:C4 -0.025 0.021 0.037 -0.001 0.031 0.014 -0.003 -0.022 0.004
## DS_Scr.c:C5 -0.014 -0.011 -0.017 0.019 0.007 -0.004 -0.001 -0.014 0.014
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 -0.039
## DS_Scr.c:C5 0.004 -0.005tab_model(modA.915,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.72 | 0.93 | 13.90 – 17.53 | 16.96 | <0.001 |
| DS Score c | 0.01 | 0.04 | -0.08 – 0.10 | 0.21 | 0.832 |
| C1 | -13.54 | 1.46 | -16.40 – -10.68 | -9.28 | <0.001 |
| C2 | -15.79 | 2.16 | -20.03 – -11.56 | -7.31 | <0.001 |
| C3 | 13.70 | 2.22 | 9.35 – 18.06 | 6.17 | <0.001 |
| C4 | -19.65 | 2.51 | -24.58 – -14.73 | -7.83 | <0.001 |
| C5 | -10.36 | 2.52 | -15.30 – -5.43 | -4.12 | <0.001 |
| DS Score c * C1 | -0.16 | 0.07 | -0.30 – -0.02 | -2.28 | 0.023 |
| DS Score c * C2 | -0.31 | 0.10 | -0.51 – -0.11 | -3.05 | 0.002 |
| DS Score c * C3 | 0.05 | 0.11 | -0.16 – 0.26 | 0.44 | 0.660 |
| DS Score c * C4 | -0.07 | 0.12 | -0.30 – 0.17 | -0.56 | 0.575 |
| DS Score c * C5 | -0.21 | 0.12 | -0.45 – 0.03 | -1.68 | 0.093 |
| Random Effects | |||||
| σ2 | 1441.89 | ||||
| τ00 id | 374.88 | ||||
| ICC | 0.21 | ||||
| N id | 1001 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.073 / 0.265 | ||||
Q.2 (DISGUST SENSITIVITY) How does sensitivity to disgust depend on naturalness perception in predicting the benefit-risk difference, above and beyond burger contrasts?
modA.9155 <- lmer(BRDiff ~ DS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9155)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ DS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## DS_Score.c * C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c *
## C4 + DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30258.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0514 -0.5083 -0.0068 0.5377 3.1462
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 315 17.75
## Residual 1196 34.58
## Number of obs: 2990, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.256e+01 8.560e-01 1.032e+03 14.669 < 2e-16 ***
## DS_Score.c 2.492e-02 4.144e-02 1.058e+03 0.601 0.547690
## Naturalness.c 7.945e-01 3.220e-02 2.806e+03 24.679 < 2e-16 ***
## C1 -6.998e+00 1.356e+00 2.516e+03 -5.159 2.68e-07 ***
## C2 -1.941e+00 2.048e+00 2.484e+03 -0.948 0.343389
## C3 1.757e+01 2.030e+00 2.474e+03 8.654 < 2e-16 ***
## C4 -9.855e+00 2.322e+00 2.493e+03 -4.245 2.27e-05 ***
## C5 -2.599e+01 2.379e+00 2.485e+03 -10.927 < 2e-16 ***
## DS_Score.c:Naturalness.c -9.384e-04 1.386e-03 2.798e+03 -0.677 0.498479
## DS_Score.c:C1 -2.307e-01 6.803e-02 2.566e+03 -3.391 0.000707 ***
## DS_Score.c:C2 -2.011e-01 9.727e-02 2.448e+03 -2.067 0.038802 *
## DS_Score.c:C3 -2.194e-02 9.733e-02 2.474e+03 -0.225 0.821676
## DS_Score.c:C4 -7.849e-02 1.114e-01 2.485e+03 -0.704 0.481208
## DS_Score.c:C5 -1.960e-01 1.158e-01 2.479e+03 -1.692 0.090694 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9155,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.56 | 0.86 | 10.88 – 14.24 | 14.67 | <0.001 |
| DS Score c | 0.02 | 0.04 | -0.06 – 0.11 | 0.60 | 0.548 |
| Naturalness c | 0.79 | 0.03 | 0.73 – 0.86 | 24.68 | <0.001 |
| C1 | -7.00 | 1.36 | -9.66 – -4.34 | -5.16 | <0.001 |
| C2 | -1.94 | 2.05 | -5.96 – 2.08 | -0.95 | 0.343 |
| C3 | 17.57 | 2.03 | 13.59 – 21.54 | 8.65 | <0.001 |
| C4 | -9.85 | 2.32 | -14.41 – -5.30 | -4.24 | <0.001 |
| C5 | -25.99 | 2.38 | -30.66 – -21.33 | -10.93 | <0.001 |
|
DS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.68 | 0.498 |
| DS Score c * C1 | -0.23 | 0.07 | -0.36 – -0.10 | -3.39 | 0.001 |
| DS Score c * C2 | -0.20 | 0.10 | -0.39 – -0.01 | -2.07 | 0.039 |
| DS Score c * C3 | -0.02 | 0.10 | -0.21 – 0.17 | -0.23 | 0.822 |
| DS Score c * C4 | -0.08 | 0.11 | -0.30 – 0.14 | -0.70 | 0.481 |
| DS Score c * C5 | -0.20 | 0.12 | -0.42 – 0.03 | -1.69 | 0.091 |
| Random Effects | |||||
| σ2 | 1195.54 | ||||
| τ00 id | 314.95 | ||||
| ICC | 0.21 | ||||
| N id | 1001 | ||||
| Observations | 2990 | ||||
| Marginal R2 / Conditional R2 | 0.222 / 0.385 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict the benefit-risk difference, over and above burger contrasts?
modA.916 <- lmer(BRDiff ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)
summary(modA.916)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## BRDiff ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c *
## C1 + Collectivism_Score.c * C2 + Collectivism_Score.c * C3 +
## Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30800.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4299 -0.5203 -0.0352 0.5493 3.1563
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 345.5 18.59
## Residual 1446.6 38.03
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.69882 0.91080 996.97428 17.236 < 2e-16 ***
## Collectivism_Score.c 0.21055 0.04459 998.93058 4.722 2.67e-06 ***
## C1 -13.51401 1.45644 2519.96210 -9.279 < 2e-16 ***
## C2 -15.59453 2.15728 2495.81637 -7.229 6.45e-13 ***
## C3 13.72719 2.21753 2491.92310 6.190 7.00e-10 ***
## C4 -19.26356 2.50460 2515.13329 -7.691 2.08e-14 ***
## C5 -10.32821 2.51521 2488.05807 -4.106 4.15e-05 ***
## Collectivism_Score.c:C1 -0.30111 0.07105 2472.33296 -4.238 2.34e-05 ***
## Collectivism_Score.c:C2 -0.15560 0.10744 2527.89812 -1.448 0.148
## Collectivism_Score.c:C3 -0.02447 0.11071 2525.79297 -0.221 0.825
## Collectivism_Score.c:C4 0.08439 0.11954 2493.76729 0.706 0.480
## Collectivism_Score.c:C5 -0.03670 0.12224 2489.37835 -0.300 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. -0.001
## C1 -0.009 0.004
## C2 -0.008 -0.005 -0.013
## C3 0.019 -0.005 -0.030 0.001
## C4 0.007 0.009 -0.006 0.003 -0.014
## C5 0.019 -0.015 0.022 -0.029 0.007 -0.005
## Cllct_S.:C1 0.004 -0.004 0.002 -0.003 0.007 -0.013 -0.020
## Cllct_S.:C2 -0.005 0.009 -0.003 -0.007 -0.002 0.002 0.021 0.010
## Cllct_S.:C3 -0.004 0.050 0.006 -0.002 -0.012 -0.010 0.003 -0.071 0.003
## Cllct_S.:C4 0.009 -0.011 -0.013 0.002 -0.011 -0.002 -0.001 0.019 0.000
## Cllct_S.:C5 -0.015 0.005 -0.020 0.020 0.002 0.000 0.011 0.003 -0.008
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.010
## Cllct_S.:C5 0.004 -0.007tab_model(modA.916,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.70 | 0.91 | 13.91 – 17.48 | 17.24 | <0.001 |
| Collectivism Score c | 0.21 | 0.04 | 0.12 – 0.30 | 4.72 | <0.001 |
| C1 | -13.51 | 1.46 | -16.37 – -10.66 | -9.28 | <0.001 |
| C2 | -15.59 | 2.16 | -19.82 – -11.36 | -7.23 | <0.001 |
| C3 | 13.73 | 2.22 | 9.38 – 18.08 | 6.19 | <0.001 |
| C4 | -19.26 | 2.50 | -24.17 – -14.35 | -7.69 | <0.001 |
| C5 | -10.33 | 2.52 | -15.26 – -5.40 | -4.11 | <0.001 |
| Collectivism Score c * C1 | -0.30 | 0.07 | -0.44 – -0.16 | -4.24 | <0.001 |
| Collectivism Score c * C2 | -0.16 | 0.11 | -0.37 – 0.06 | -1.45 | 0.148 |
| Collectivism Score c * C3 | -0.02 | 0.11 | -0.24 – 0.19 | -0.22 | 0.825 |
| Collectivism Score c * C4 | 0.08 | 0.12 | -0.15 – 0.32 | 0.71 | 0.480 |
| Collectivism Score c * C5 | -0.04 | 0.12 | -0.28 – 0.20 | -0.30 | 0.764 |
| Random Effects | |||||
| σ2 | 1446.56 | ||||
| τ00 id | 345.48 | ||||
| ICC | 0.19 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.084 / 0.260 | ||||
Q.2 (COLLECTIVISM) How does collectivism depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9166 <- lmer(BRDiff ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9166)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Collectivism_Score.c * C1 + Collectivism_Score.c *
## C2 + Collectivism_Score.c * C3 + Collectivism_Score.c * C4 +
## Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30226
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1241 -0.5078 -0.0200 0.5374 3.2846
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 287.5 16.95
## Residual 1189.3 34.49
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.251e+01 8.380e-01 1.035e+03 14.923
## Collectivism_Score.c 2.194e-01 4.082e-02 1.020e+03 5.375
## Naturalness.c 8.062e-01 3.222e-02 2.824e+03 25.021
## C1 -6.945e+00 1.351e+00 2.538e+03 -5.142
## C2 -1.455e+00 2.036e+00 2.496e+03 -0.714
## C3 1.762e+01 2.017e+00 2.492e+03 8.736
## C4 -9.175e+00 2.308e+00 2.514e+03 -3.975
## C5 -2.636e+01 2.368e+00 2.505e+03 -11.133
## Collectivism_Score.c:Naturalness.c 1.370e-03 1.385e-03 2.770e+03 0.989
## Collectivism_Score.c:C1 -3.733e-01 6.624e-02 2.506e+03 -5.636
## Collectivism_Score.c:C2 -8.319e-03 1.019e-01 2.488e+03 -0.082
## Collectivism_Score.c:C3 2.362e-02 1.005e-01 2.521e+03 0.235
## Collectivism_Score.c:C4 5.748e-02 1.105e-01 2.490e+03 0.520
## Collectivism_Score.c:C5 -2.671e-01 1.150e-01 2.500e+03 -2.322
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 9.51e-08 ***
## Naturalness.c < 2e-16 ***
## C1 2.93e-07 ***
## C2 0.4750
## C3 < 2e-16 ***
## C4 7.24e-05 ***
## C5 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.3227
## Collectivism_Score.c:C1 1.94e-08 ***
## Collectivism_Score.c:C2 0.9349
## Collectivism_Score.c:C3 0.8142
## Collectivism_Score.c:C4 0.6031
## Collectivism_Score.c:C5 0.0203 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9166,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.51 | 0.84 | 10.86 – 14.15 | 14.92 | <0.001 |
| Collectivism Score c | 0.22 | 0.04 | 0.14 – 0.30 | 5.37 | <0.001 |
| Naturalness c | 0.81 | 0.03 | 0.74 – 0.87 | 25.02 | <0.001 |
| C1 | -6.94 | 1.35 | -9.59 – -4.30 | -5.14 | <0.001 |
| C2 | -1.45 | 2.04 | -5.45 – 2.54 | -0.71 | 0.475 |
| C3 | 17.62 | 2.02 | 13.67 – 21.58 | 8.74 | <0.001 |
| C4 | -9.18 | 2.31 | -13.70 – -4.65 | -3.97 | <0.001 |
| C5 | -26.36 | 2.37 | -31.01 – -21.72 | -11.13 | <0.001 |
|
Collectivism Score c * Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.99 | 0.323 |
| Collectivism Score c * C1 | -0.37 | 0.07 | -0.50 – -0.24 | -5.64 | <0.001 |
| Collectivism Score c * C2 | -0.01 | 0.10 | -0.21 – 0.19 | -0.08 | 0.935 |
| Collectivism Score c * C3 | 0.02 | 0.10 | -0.17 – 0.22 | 0.24 | 0.814 |
| Collectivism Score c * C4 | 0.06 | 0.11 | -0.16 – 0.27 | 0.52 | 0.603 |
| Collectivism Score c * C5 | -0.27 | 0.12 | -0.49 – -0.04 | -2.32 | 0.020 |
| Random Effects | |||||
| σ2 | 1189.31 | ||||
| τ00 id | 287.46 | ||||
| ICC | 0.19 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.240 / 0.388 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict the benefit-risk difference, over and above burger contrasts?
modA.917 <- lmer(BRDiff ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.917)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## BRDiff ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c *
## C1 + Individualism_Score.c * C2 + Individualism_Score.c *
## C3 + Individualism_Score.c * C4 + Individualism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30769.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5361 -0.4914 -0.0216 0.5536 2.6985
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 333.3 18.26
## Residual 1444.0 38.00
## Number of obs: 2992, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.72736 0.90382 996.65600 17.401 < 2e-16 ***
## Individualism_Score.c 0.30598 0.05035 998.77588 6.077 1.74e-09 ***
## C1 -13.55268 1.45399 2525.81178 -9.321 < 2e-16 ***
## C2 -15.70434 2.15380 2501.36831 -7.291 4.09e-13 ***
## C3 13.97182 2.21357 2498.06910 6.312 3.25e-10 ***
## C4 -19.67679 2.50001 2521.38729 -7.871 5.19e-15 ***
## C5 -10.33908 2.51161 2492.23260 -4.117 3.97e-05 ***
## Individualism_Score.c:C1 -0.36919 0.08078 2488.36334 -4.570 5.11e-06 ***
## Individualism_Score.c:C2 -0.17455 0.11955 2527.06019 -1.460 0.1444
## Individualism_Score.c:C3 0.16723 0.12501 2525.55441 1.338 0.1811
## Individualism_Score.c:C4 -0.23410 0.13646 2505.34366 -1.716 0.0864 .
## Individualism_Score.c:C5 -0.12239 0.14185 2496.38130 -0.863 0.3883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. 0.000
## C1 -0.008 0.000
## C2 -0.008 0.006 -0.013
## C3 0.019 0.006 -0.030 0.001
## C4 0.007 -0.007 -0.006 0.003 -0.014
## C5 0.019 -0.015 0.022 -0.029 0.006 -0.005
## Indvd_S.:C1 0.000 -0.003 0.001 0.011 -0.008 0.008 -0.021
## Indvd_S.:C2 0.006 -0.024 0.011 0.004 -0.004 0.001 0.020 -0.038
## Indvd_S.:C3 0.006 0.043 -0.008 -0.004 0.007 0.012 0.001 -0.061 0.002
## Indvd_S.:C4 -0.007 0.005 0.008 0.001 0.012 -0.010 -0.001 -0.001 0.002
## Indvd_S.:C5 -0.015 0.007 -0.021 0.020 0.001 -0.001 -0.007 0.009 -0.010
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 -0.012
## Indvd_S.:C5 0.007 -0.004tab_model(modA.917,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.73 | 0.90 | 13.96 – 17.50 | 17.40 | <0.001 |
| Individualism Score c | 0.31 | 0.05 | 0.21 – 0.40 | 6.08 | <0.001 |
| C1 | -13.55 | 1.45 | -16.40 – -10.70 | -9.32 | <0.001 |
| C2 | -15.70 | 2.15 | -19.93 – -11.48 | -7.29 | <0.001 |
| C3 | 13.97 | 2.21 | 9.63 – 18.31 | 6.31 | <0.001 |
| C4 | -19.68 | 2.50 | -24.58 – -14.77 | -7.87 | <0.001 |
| C5 | -10.34 | 2.51 | -15.26 – -5.41 | -4.12 | <0.001 |
|
Individualism Score c * C1 |
-0.37 | 0.08 | -0.53 – -0.21 | -4.57 | <0.001 |
|
Individualism Score c * C2 |
-0.17 | 0.12 | -0.41 – 0.06 | -1.46 | 0.144 |
|
Individualism Score c * C3 |
0.17 | 0.13 | -0.08 – 0.41 | 1.34 | 0.181 |
|
Individualism Score c * C4 |
-0.23 | 0.14 | -0.50 – 0.03 | -1.72 | 0.086 |
|
Individualism Score c * C5 |
-0.12 | 0.14 | -0.40 – 0.16 | -0.86 | 0.388 |
| Random Effects | |||||
| σ2 | 1443.98 | ||||
| τ00 id | 333.35 | ||||
| ICC | 0.19 | ||||
| N id | 1002 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.091 / 0.262 | ||||
Q.2 (INDIVIDUALISM) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
modA.9177 <- lmer(BRDiff ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9177)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 +
## C4 + C5 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30202.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3002 -0.4722 -0.0081 0.5288 3.3519
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 275.6 16.60
## Residual 1192.1 34.53
## Number of obs: 2991, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.270e+01 8.318e-01 1.033e+03 15.274
## Individualism_Score.c 2.695e-01 4.640e-02 1.039e+03 5.807
## Naturalness.c 7.628e-01 3.359e-02 2.875e+03 22.711
## C1 -7.020e+00 1.348e+00 2.542e+03 -5.206
## C2 -1.748e+00 2.037e+00 2.503e+03 -0.858
## C3 1.761e+01 2.018e+00 2.500e+03 8.726
## C4 -9.915e+00 2.308e+00 2.521e+03 -4.296
## C5 -2.615e+01 2.368e+00 2.509e+03 -11.042
## Individualism_Score.c:Naturalness.c 6.239e-03 1.708e-03 2.901e+03 3.654
## Individualism_Score.c:C1 -2.409e-01 7.471e-02 2.517e+03 -3.225
## Individualism_Score.c:C2 1.443e-01 1.117e-01 2.497e+03 1.292
## Individualism_Score.c:C3 1.813e-01 1.138e-01 2.528e+03 1.593
## Individualism_Score.c:C4 -7.774e-02 1.258e-01 2.499e+03 -0.618
## Individualism_Score.c:C5 -5.286e-01 1.318e-01 2.487e+03 -4.011
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 8.45e-09 ***
## Naturalness.c < 2e-16 ***
## C1 2.08e-07 ***
## C2 0.391084
## C3 < 2e-16 ***
## C4 1.80e-05 ***
## C5 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.000263 ***
## Individualism_Score.c:C1 0.001277 **
## Individualism_Score.c:C2 0.196542
## Individualism_Score.c:C3 0.111303
## Individualism_Score.c:C4 0.536513
## Individualism_Score.c:C5 6.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9177,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 12.70 | 0.83 | 11.07 – 14.34 | 15.27 | <0.001 |
| Individualism Score c | 0.27 | 0.05 | 0.18 – 0.36 | 5.81 | <0.001 |
| Naturalness c | 0.76 | 0.03 | 0.70 – 0.83 | 22.71 | <0.001 |
| C1 | -7.02 | 1.35 | -9.66 – -4.38 | -5.21 | <0.001 |
| C2 | -1.75 | 2.04 | -5.74 – 2.25 | -0.86 | 0.391 |
| C3 | 17.61 | 2.02 | 13.65 – 21.56 | 8.73 | <0.001 |
| C4 | -9.91 | 2.31 | -14.44 – -5.39 | -4.30 | <0.001 |
| C5 | -26.15 | 2.37 | -30.79 – -21.51 | -11.04 | <0.001 |
|
Individualism Score c * Naturalness c |
0.01 | 0.00 | 0.00 – 0.01 | 3.65 | <0.001 |
|
Individualism Score c * C1 |
-0.24 | 0.07 | -0.39 – -0.09 | -3.22 | 0.001 |
|
Individualism Score c * C2 |
0.14 | 0.11 | -0.07 – 0.36 | 1.29 | 0.197 |
|
Individualism Score c * C3 |
0.18 | 0.11 | -0.04 – 0.40 | 1.59 | 0.111 |
|
Individualism Score c * C4 |
-0.08 | 0.13 | -0.32 – 0.17 | -0.62 | 0.537 |
|
Individualism Score c * C5 |
-0.53 | 0.13 | -0.79 – -0.27 | -4.01 | <0.001 |
| Random Effects | |||||
| σ2 | 1192.08 | ||||
| τ00 id | 275.56 | ||||
| ICC | 0.19 | ||||
| N id | 1002 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.244 / 0.386 | ||||
Political Ideology
Q.1 (POLITICAL ORIENTATION) How does individualism predict the benefit-risk difference, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.918 <- lmer(BRDiff ~ Ideology.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.918)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c * C1 +
## Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 + Ideology.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30792.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4065 -0.5166 -0.0326 0.5590 2.8783
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 370.2 19.24
## Residual 1451.1 38.09
## Number of obs: 2992, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.572e+01 1.361e+00 9.960e+02 11.551 < 2e-16 ***
## Ideology.c -2.816e-03 8.533e-01 1.000e+03 -0.003 0.997368
## C1 -1.503e+01 2.149e+00 2.507e+03 -6.995 3.39e-12 ***
## C2 -1.690e+01 3.114e+00 2.450e+03 -5.427 6.28e-08 ***
## C3 1.420e+01 3.310e+00 2.481e+03 4.289 1.86e-05 ***
## C4 -2.434e+01 3.734e+00 2.519e+03 -6.517 8.64e-11 ***
## C5 -1.426e+01 3.706e+00 2.487e+03 -3.847 0.000123 ***
## Ideology.c:C1 -1.361e+00 1.349e+00 2.507e+03 -1.009 0.312945
## Ideology.c:C2 -1.144e+00 1.962e+00 2.459e+03 -0.583 0.559917
## Ideology.c:C3 2.721e-01 2.096e+00 2.497e+03 0.130 0.896740
## Ideology.c:C4 -4.122e+00 2.336e+00 2.513e+03 -1.764 0.077817 .
## Ideology.c:C5 -3.373e+00 2.304e+00 2.485e+03 -1.464 0.143287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.733
## C1 -0.025 -0.025
## C2 -0.021 -0.016 -0.030
## C3 0.021 0.020 -0.036 0.003
## C4 0.008 0.001 -0.007 0.003 -0.015
## C5 0.014 0.016 0.012 -0.024 0.014 -0.002
## Idelgy.c:C1 -0.025 -0.030 0.733 -0.022 -0.031 0.001 0.015
## Idelgy.c:C2 -0.016 -0.011 -0.022 0.719 0.002 0.002 -0.025 -0.017
## Idelgy.c:C3 0.020 0.030 -0.031 0.002 0.740 -0.005 0.016 -0.044 0.000
## Idelgy.c:C4 0.001 -0.006 0.000 0.002 -0.005 0.739 -0.001 0.010 0.001
## Idelgy.c:C5 0.016 0.034 0.016 -0.025 0.016 -0.001 0.732 0.040 -0.048
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.004
## Idelgy.c:C5 0.024 -0.004tab_model(modA.918,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 15.72 | 1.36 | 13.05 – 18.38 | 11.55 | <0.001 |
| Ideology c | -0.00 | 0.85 | -1.68 – 1.67 | -0.00 | 0.997 |
| C1 | -15.03 | 2.15 | -19.25 – -10.82 | -6.99 | <0.001 |
| C2 | -16.90 | 3.11 | -23.01 – -10.79 | -5.43 | <0.001 |
| C3 | 14.20 | 3.31 | 7.71 – 20.69 | 4.29 | <0.001 |
| C4 | -24.34 | 3.73 | -31.66 – -17.01 | -6.52 | <0.001 |
| C5 | -14.26 | 3.71 | -21.52 – -6.99 | -3.85 | <0.001 |
| Ideology c * C1 | -1.36 | 1.35 | -4.01 – 1.28 | -1.01 | 0.313 |
| Ideology c * C2 | -1.14 | 1.96 | -4.99 – 2.70 | -0.58 | 0.560 |
| Ideology c * C3 | 0.27 | 2.10 | -3.84 – 4.38 | 0.13 | 0.897 |
| Ideology c * C4 | -4.12 | 2.34 | -8.70 – 0.46 | -1.76 | 0.078 |
| Ideology c * C5 | -3.37 | 2.30 | -7.89 – 1.14 | -1.46 | 0.143 |
| Random Effects | |||||
| σ2 | 1451.15 | ||||
| τ00 id | 370.21 | ||||
| ICC | 0.20 | ||||
| N id | 1002 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.070 / 0.259 | ||||
Q.2 (POLITICAL ORIENTATION) How does individualism depend on naturalness perception in predicting the benefit-risk difference, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.9188 <- lmer(BRDiff ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.9188)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: BRDiff ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## Ideology.c * C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c *
## C4 + Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 30239
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1049 -0.5081 -0.0120 0.5334 3.2098
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 309.5 17.59
## Residual 1205.0 34.71
## Number of obs: 2991, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.73691 1.25454 1025.61418 9.356 < 2e-16 ***
## Ideology.c -0.67968 0.78186 1010.16539 -0.869 0.384882
## Naturalness.c 0.85113 0.04749 2803.47202 17.922 < 2e-16 ***
## C1 -6.57329 1.98939 2516.90325 -3.304 0.000966 ***
## C2 -2.19893 2.94363 2461.93518 -0.747 0.455127
## C3 18.51837 3.02266 2478.80738 6.127 1.04e-09 ***
## C4 -13.00827 3.44678 2519.79493 -3.774 0.000164 ***
## C5 -29.72727 3.49210 2495.01950 -8.513 < 2e-16 ***
## Ideology.c:Naturalness.c 0.05606 0.03336 2812.55049 1.680 0.093038 .
## Ideology.c:C1 0.27875 1.23109 2503.83306 0.226 0.820891
## Ideology.c:C2 -0.34909 1.86219 2471.84048 -0.187 0.851314
## Ideology.c:C3 0.67999 1.91181 2495.30654 0.356 0.722110
## Ideology.c:C4 -2.87568 2.14880 2502.31879 -1.338 0.180930
## Ideology.c:C5 -3.38557 2.21215 2515.64585 -1.530 0.126033
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9188,
show.stat = T, show.se = T)| BRDiff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 11.74 | 1.25 | 9.28 – 14.20 | 9.36 | <0.001 |
| Ideology c | -0.68 | 0.78 | -2.21 – 0.85 | -0.87 | 0.385 |
| Naturalness c | 0.85 | 0.05 | 0.76 – 0.94 | 17.92 | <0.001 |
| C1 | -6.57 | 1.99 | -10.47 – -2.67 | -3.30 | 0.001 |
| C2 | -2.20 | 2.94 | -7.97 – 3.57 | -0.75 | 0.455 |
| C3 | 18.52 | 3.02 | 12.59 – 24.45 | 6.13 | <0.001 |
| C4 | -13.01 | 3.45 | -19.77 – -6.25 | -3.77 | <0.001 |
| C5 | -29.73 | 3.49 | -36.57 – -22.88 | -8.51 | <0.001 |
|
Ideology c * Naturalness c |
0.06 | 0.03 | -0.01 – 0.12 | 1.68 | 0.093 |
| Ideology c * C1 | 0.28 | 1.23 | -2.14 – 2.69 | 0.23 | 0.821 |
| Ideology c * C2 | -0.35 | 1.86 | -4.00 – 3.30 | -0.19 | 0.851 |
| Ideology c * C3 | 0.68 | 1.91 | -3.07 – 4.43 | 0.36 | 0.722 |
| Ideology c * C4 | -2.88 | 2.15 | -7.09 – 1.34 | -1.34 | 0.181 |
| Ideology c * C5 | -3.39 | 2.21 | -7.72 – 0.95 | -1.53 | 0.126 |
| Random Effects | |||||
| σ2 | 1204.95 | ||||
| τ00 id | 309.45 | ||||
| ICC | 0.20 | ||||
| N id | 1002 | ||||
| Observations | 2991 | ||||
| Marginal R2 / Conditional R2 | 0.220 / 0.379 | ||||
Familiarity/Understanding (Mean score)
Q.1 How do burger contrasts predict familiarity/understanding?
modA.920 <- lmer(FR ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)
summary(modA.920)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27138.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8457 -0.5009 0.0654 0.5426 3.3148
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.0 16.43
## Residual 351.5 18.75
## Number of obs: 2986, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.369e+01 6.271e-01 9.978e+02 101.549 < 2e-16 ***
## C1 -4.554e+00 7.688e-01 2.474e+03 -5.923 3.59e-09 ***
## C2 1.780e+01 1.101e+00 2.316e+03 16.174 < 2e-16 ***
## C3 1.941e-01 1.171e+00 2.461e+03 0.166 0.868
## C4 5.538e-18 1.188e+00 1.987e+03 0.000 1.000
## C5 -6.948e+00 1.278e+00 2.306e+03 -5.437 5.97e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3 C4
## C1 -0.041
## C2 -0.005 -0.011
## C3 -0.020 0.041 0.016
## C4 0.000 0.000 0.000 0.000
## C5 0.013 0.034 -0.026 0.018 0.000tab_model(modA.920,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.69 | 0.63 | 62.46 – 64.91 | 101.55 | <0.001 |
| C1 | -4.55 | 0.77 | -6.06 – -3.05 | -5.92 | <0.001 |
| C2 | 17.80 | 1.10 | 15.65 – 19.96 | 16.17 | <0.001 |
| C3 | 0.19 | 1.17 | -2.10 – 2.49 | 0.17 | 0.868 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -6.95 | 1.28 | -9.45 – -4.44 | -5.44 | <0.001 |
| Random Effects | |||||
| σ2 | 351.49 | ||||
| τ00 id | 269.97 | ||||
| ICC | 0.43 | ||||
| N id | 1004 | ||||
| Observations | 2986 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.472 | ||||
Aversion to Tampering with Nature
Q.1 (AVERSION TO TAMPERING WITH NATURE) How does aversion to tampering with nature predict familiarity/understanding, over and above burger contrasts?
modA.921 <- lmer(FR ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)
summary(modA.921)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ ATNS_Score.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c * C1 +
## ATNS_Score.c * C2 + ATNS_Score.c * C3 + ATNS_Score.c * C4 +
## ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27111.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8494 -0.5014 0.0642 0.5456 3.3370
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 262.9 16.21
## Residual 351.7 18.75
## Number of obs: 2983, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.370e+01 6.221e-01 9.932e+02 102.393 < 2e-16 ***
## ATNS_Score.c 1.558e-01 3.581e-02 9.889e+02 4.351 1.50e-05 ***
## C1 -4.504e+00 7.691e-01 2.473e+03 -5.856 5.38e-09 ***
## C2 1.779e+01 1.102e+00 2.314e+03 16.135 < 2e-16 ***
## C3 2.809e-01 1.171e+00 2.460e+03 0.240 0.8105
## C4 -2.338e-17 1.189e+00 1.979e+03 0.000 1.0000
## C5 -6.931e+00 1.280e+00 2.301e+03 -5.416 6.74e-08 ***
## ATNS_Score.c:C1 -9.784e-02 4.411e-02 2.464e+03 -2.218 0.0267 *
## ATNS_Score.c:C2 5.237e-02 6.140e-02 2.278e+03 0.853 0.3938
## ATNS_Score.c:C3 -8.766e-02 6.821e-02 2.459e+03 -1.285 0.1989
## ATNS_Score.c:C4 -5.989e-17 6.851e-02 1.979e+03 0.000 1.0000
## ATNS_Score.c:C5 -4.028e-02 7.382e-02 2.309e+03 -0.546 0.5853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 C5 ATNS_S.:C1
## ATNS_Scor.c -0.003
## C1 -0.040 0.003
## C2 -0.005 0.010 -0.012
## C3 -0.019 0.003 0.040 0.016
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 -0.023 0.035 -0.027 0.018 0.000
## ATNS_Sc.:C1 0.003 -0.052 -0.012 0.022 -0.007 0.000 -0.029
## ATNS_Sc.:C2 0.011 -0.023 0.022 0.015 -0.002 0.000 0.043 -0.030
## ATNS_Sc.:C3 0.004 -0.012 -0.007 -0.001 -0.024 0.000 0.003 0.029
## ATNS_Sc.:C4 0.000 0.000 0.000 0.000 0.000 -0.019 0.000 0.000
## ATNS_Sc.:C5 -0.022 0.005 -0.029 0.043 0.002 0.000 -0.015 0.020
## ATNS_S.:C2 ATNS_S.:C3 ATNS_S.:C4
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## C5
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3 0.031
## ATNS_Sc.:C4 0.000 0.000
## ATNS_Sc.:C5 -0.015 0.025 0.000tab_model(modA.921,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.70 | 0.62 | 62.48 – 64.92 | 102.39 | <0.001 |
| ATNS Score c | 0.16 | 0.04 | 0.09 – 0.23 | 4.35 | <0.001 |
| C1 | -4.50 | 0.77 | -6.01 – -3.00 | -5.86 | <0.001 |
| C2 | 17.79 | 1.10 | 15.63 – 19.95 | 16.14 | <0.001 |
| C3 | 0.28 | 1.17 | -2.02 – 2.58 | 0.24 | 0.810 |
| C4 | -0.00 | 1.19 | -2.33 – 2.33 | -0.00 | 1.000 |
| C5 | -6.93 | 1.28 | -9.44 – -4.42 | -5.42 | <0.001 |
| ATNS Score c * C1 | -0.10 | 0.04 | -0.18 – -0.01 | -2.22 | 0.027 |
| ATNS Score c * C2 | 0.05 | 0.06 | -0.07 – 0.17 | 0.85 | 0.394 |
| ATNS Score c * C3 | -0.09 | 0.07 | -0.22 – 0.05 | -1.29 | 0.199 |
| ATNS Score c * C4 | -0.00 | 0.07 | -0.13 – 0.13 | -0.00 | 1.000 |
| ATNS Score c * C5 | -0.04 | 0.07 | -0.19 – 0.10 | -0.55 | 0.585 |
| Random Effects | |||||
| σ2 | 351.67 | ||||
| τ00 id | 262.86 | ||||
| ICC | 0.43 | ||||
| N id | 1002 | ||||
| Observations | 2983 | ||||
| Marginal R2 / Conditional R2 | 0.078 / 0.473 | ||||
Q.2 (AVERSION TO TAMPERING WITH NATURE) Does aversion to tampering with nature depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.9213 <- lmer(FR ~ ATNS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9213)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ ATNS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## ATNS_Score.c * C1 + ATNS_Score.c * C2 + ATNS_Score.c * C3 +
## ATNS_Score.c * C4 + ATNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21647.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8827 -0.5140 0.0472 0.5865 3.0602
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 251.0 15.84
## Residual 356.4 18.88
## Number of obs: 2371, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.318e+01 6.743e-01 1.162e+03 93.711 < 2e-16
## ATNS_Score.c 1.643e-01 3.893e-02 1.176e+03 4.220 2.63e-05
## Naturalness.c 1.726e-01 2.241e-02 2.037e+03 7.704 2.04e-14
## C1 -3.003e+00 9.803e-01 1.891e+03 -3.064 0.00222
## C2 2.152e+01 1.711e+00 1.974e+03 12.579 < 2e-16
## C3 9.607e-01 1.357e+00 1.899e+03 0.708 0.47896
## C4 1.594e+00 1.721e+00 1.575e+03 0.927 0.35426
## C5 -1.015e+01 1.369e+00 1.731e+03 -7.411 1.95e-13
## ATNS_Score.c:Naturalness.c 2.566e-03 1.084e-03 2.051e+03 2.368 0.01799
## ATNS_Score.c:C1 -4.617e-02 5.743e-02 1.925e+03 -0.804 0.42154
## ATNS_Score.c:C2 2.649e-01 9.623e-02 1.970e+03 2.753 0.00597
## ATNS_Score.c:C3 -1.183e-01 7.834e-02 1.884e+03 -1.510 0.13116
## ATNS_Score.c:C4 7.972e-03 9.893e-02 1.569e+03 0.081 0.93579
## ATNS_Score.c:C5 -1.448e-01 7.727e-02 1.743e+03 -1.874 0.06114
##
## (Intercept) ***
## ATNS_Score.c ***
## Naturalness.c ***
## C1 **
## C2 ***
## C3
## C4
## C5 ***
## ATNS_Score.c:Naturalness.c *
## ATNS_Score.c:C1
## ATNS_Score.c:C2 **
## ATNS_Score.c:C3
## ATNS_Score.c:C4
## ATNS_Score.c:C5 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9213,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.18 | 0.67 | 61.86 – 64.51 | 93.71 | <0.001 |
| ATNS Score c | 0.16 | 0.04 | 0.09 – 0.24 | 4.22 | <0.001 |
| Naturalness c | 0.17 | 0.02 | 0.13 – 0.22 | 7.70 | <0.001 |
| C1 | -3.00 | 0.98 | -4.93 – -1.08 | -3.06 | 0.002 |
| C2 | 21.52 | 1.71 | 18.17 – 24.88 | 12.58 | <0.001 |
| C3 | 0.96 | 1.36 | -1.70 – 3.62 | 0.71 | 0.479 |
| C4 | 1.59 | 1.72 | -1.78 – 4.97 | 0.93 | 0.354 |
| C5 | -10.15 | 1.37 | -12.83 – -7.46 | -7.41 | <0.001 |
|
ATNS Score c * Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 2.37 | 0.018 |
| ATNS Score c * C1 | -0.05 | 0.06 | -0.16 – 0.07 | -0.80 | 0.422 |
| ATNS Score c * C2 | 0.26 | 0.10 | 0.08 – 0.45 | 2.75 | 0.006 |
| ATNS Score c * C3 | -0.12 | 0.08 | -0.27 – 0.04 | -1.51 | 0.131 |
| ATNS Score c * C4 | 0.01 | 0.10 | -0.19 – 0.20 | 0.08 | 0.936 |
| ATNS Score c * C5 | -0.14 | 0.08 | -0.30 – 0.01 | -1.87 | 0.061 |
| Random Effects | |||||
| σ2 | 356.39 | ||||
| τ00 id | 251.03 | ||||
| ICC | 0.41 | ||||
| N id | 1002 | ||||
| Observations | 2371 | ||||
| Marginal R2 / Conditional R2 | 0.098 / 0.471 | ||||
Animal Welfare
Q.1 (ANIMAL WELFARE) How does concern for animal welfare predict familiarity/understanding, over and above burger contrasts?
modA.922 <- lmer(FR ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)
summary(modA.922)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ AW_Score.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c * C1 +
## AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 + AW_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27049.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9160 -0.4937 0.0711 0.5419 3.0696
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 244.4 15.63
## Residual 351.8 18.76
## Number of obs: 2981, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.368e+01 6.075e-01 9.921e+02 104.821 < 2e-16 ***
## AW_Score.c 2.092e-01 2.481e-02 9.985e+02 8.433 < 2e-16 ***
## C1 -4.385e+00 7.673e-01 2.489e+03 -5.715 1.23e-08 ***
## C2 1.768e+01 1.099e+00 2.325e+03 16.082 < 2e-16 ***
## C3 3.688e-01 1.170e+00 2.480e+03 0.315 0.7525
## C4 -1.992e-16 1.192e+00 1.979e+03 0.000 1.0000
## C5 -7.149e+00 1.278e+00 2.315e+03 -5.594 2.49e-08 ***
## AW_Score.c:C1 2.775e-02 3.128e-02 2.468e+03 0.887 0.3752
## AW_Score.c:C2 -3.528e-02 4.338e-02 2.306e+03 -0.813 0.4162
## AW_Score.c:C3 -2.672e-02 4.916e-02 2.544e+03 -0.544 0.5868
## AW_Score.c:C4 1.588e-15 4.997e-02 1.979e+03 0.000 1.0000
## AW_Score.c:C5 -9.029e-02 5.182e-02 2.312e+03 -1.742 0.0816 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 C5 AW_S.:C1 AW_S.:C2
## AW_Score.c -0.010
## C1 -0.042 0.025
## C2 -0.005 -0.005 -0.012
## C3 -0.021 0.022 0.041 0.015
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.013 -0.014 0.033 -0.026 0.018 0.000
## AW_Scr.c:C1 0.025 -0.065 -0.021 0.000 -0.032 0.000 -0.018
## AW_Scr.c:C2 -0.005 -0.022 0.000 0.009 0.004 0.000 0.024 -0.036
## AW_Scr.c:C3 0.022 -0.031 -0.032 0.004 -0.046 0.000 0.001 0.060 0.018
## AW_Scr.c:C4 0.000 0.000 0.000 0.000 0.000 -0.077 0.000 0.000 0.000
## AW_Scr.c:C5 -0.014 0.012 -0.018 0.024 0.001 0.000 0.026 0.037 -0.021
## AW_S.:C3 AW_S.:C4
## AW_Score.c
## C1
## C2
## C3
## C4
## C5
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 0.000
## AW_Scr.c:C5 0.016 0.000tab_model(modA.922,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.68 | 0.61 | 62.49 – 64.87 | 104.82 | <0.001 |
| AW Score c | 0.21 | 0.02 | 0.16 – 0.26 | 8.43 | <0.001 |
| C1 | -4.39 | 0.77 | -5.89 – -2.88 | -5.72 | <0.001 |
| C2 | 17.68 | 1.10 | 15.53 – 19.84 | 16.08 | <0.001 |
| C3 | 0.37 | 1.17 | -1.92 – 2.66 | 0.32 | 0.753 |
| C4 | -0.00 | 1.19 | -2.34 – 2.34 | -0.00 | 1.000 |
| C5 | -7.15 | 1.28 | -9.65 – -4.64 | -5.59 | <0.001 |
| AW Score c * C1 | 0.03 | 0.03 | -0.03 – 0.09 | 0.89 | 0.375 |
| AW Score c * C2 | -0.04 | 0.04 | -0.12 – 0.05 | -0.81 | 0.416 |
| AW Score c * C3 | -0.03 | 0.05 | -0.12 – 0.07 | -0.54 | 0.587 |
| AW Score c * C4 | 0.00 | 0.05 | -0.10 – 0.10 | 0.00 | 1.000 |
| AW Score c * C5 | -0.09 | 0.05 | -0.19 – 0.01 | -1.74 | 0.082 |
| Random Effects | |||||
| σ2 | 351.80 | ||||
| τ00 id | 244.40 | ||||
| ICC | 0.41 | ||||
| N id | 1001 | ||||
| Observations | 2981 | ||||
| Marginal R2 / Conditional R2 | 0.107 / 0.473 | ||||
Q.2 (ANIMAL WELFARE) Does animal welfare depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.9224 <- lmer(FR ~ AW_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c*C1 + AW_Score.c*C2 + AW_Score.c*C3 + AW_Score.c*C4 + AW_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9224)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FR ~ AW_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + AW_Score.c *
## C1 + AW_Score.c * C2 + AW_Score.c * C3 + AW_Score.c * C4 +
## AW_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21605.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7219 -0.5137 0.0698 0.5747 3.1372
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 229.6 15.15
## Residual 361.7 19.02
## Number of obs: 2369, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.312e+01 6.603e-01 1.170e+03 95.590 < 2e-16 ***
## AW_Score.c 1.940e-01 2.673e-02 1.149e+03 7.255 7.36e-13 ***
## Naturalness.c 1.764e-01 2.220e-02 2.100e+03 7.943 3.19e-15 ***
## C1 -2.977e+00 9.832e-01 1.922e+03 -3.028 0.0025 **
## C2 2.101e+01 1.708e+00 1.998e+03 12.303 < 2e-16 ***
## C3 1.044e+00 1.363e+00 1.935e+03 0.766 0.4440
## C4 1.368e+00 1.729e+00 1.591e+03 0.791 0.4291
## C5 -1.027e+01 1.376e+00 1.758e+03 -7.465 1.31e-13 ***
## AW_Score.c:Naturalness.c -3.507e-04 8.201e-04 2.195e+03 -0.428 0.6690
## AW_Score.c:C1 -1.960e-02 3.991e-02 1.892e+03 -0.491 0.6234
## AW_Score.c:C2 -7.966e-02 6.625e-02 2.010e+03 -1.202 0.2294
## AW_Score.c:C3 -4.980e-02 5.660e-02 1.964e+03 -0.880 0.3790
## AW_Score.c:C4 1.961e-02 7.017e-02 1.570e+03 0.279 0.7800
## AW_Score.c:C5 -1.040e-01 5.543e-02 1.744e+03 -1.876 0.0609 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9224,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.12 | 0.66 | 61.82 – 64.41 | 95.59 | <0.001 |
| AW Score c | 0.19 | 0.03 | 0.14 – 0.25 | 7.26 | <0.001 |
| Naturalness c | 0.18 | 0.02 | 0.13 – 0.22 | 7.94 | <0.001 |
| C1 | -2.98 | 0.98 | -4.90 – -1.05 | -3.03 | 0.002 |
| C2 | 21.01 | 1.71 | 17.66 – 24.36 | 12.30 | <0.001 |
| C3 | 1.04 | 1.36 | -1.63 – 3.72 | 0.77 | 0.444 |
| C4 | 1.37 | 1.73 | -2.02 – 4.76 | 0.79 | 0.429 |
| C5 | -10.27 | 1.38 | -12.97 – -7.57 | -7.46 | <0.001 |
|
AW Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.43 | 0.669 |
| AW Score c * C1 | -0.02 | 0.04 | -0.10 – 0.06 | -0.49 | 0.623 |
| AW Score c * C2 | -0.08 | 0.07 | -0.21 – 0.05 | -1.20 | 0.229 |
| AW Score c * C3 | -0.05 | 0.06 | -0.16 – 0.06 | -0.88 | 0.379 |
| AW Score c * C4 | 0.02 | 0.07 | -0.12 – 0.16 | 0.28 | 0.780 |
| AW Score c * C5 | -0.10 | 0.06 | -0.21 – 0.00 | -1.88 | 0.061 |
| Random Effects | |||||
| σ2 | 361.73 | ||||
| τ00 id | 229.59 | ||||
| ICC | 0.39 | ||||
| N id | 1001 | ||||
| Observations | 2369 | ||||
| Marginal R2 / Conditional R2 | 0.121 / 0.462 | ||||
Connectedness to Nature
Q.1 (CONNECTEDNESS TO NATURE) How does connectedness to nature predict familiarity/understanding, over and above burger contrasts?
modA.923 <- lmer(FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.923)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c * C1 +
## CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c * C4 +
## CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27104.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0231 -0.4970 0.0547 0.5518 3.3053
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.6 16.27
## Residual 350.5 18.72
## Number of obs: 2983, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.368e+01 6.231e-01 9.941e+02 102.207 < 2e-16 ***
## CNS_Score.c 1.992e-01 4.987e-02 9.769e+02 3.994 6.98e-05 ***
## C1 -4.481e+00 7.679e-01 2.472e+03 -5.835 6.07e-09 ***
## C2 1.777e+01 1.100e+00 2.312e+03 16.160 < 2e-16 ***
## C3 3.373e-01 1.170e+00 2.457e+03 0.288 0.7731
## C4 2.810e-16 1.187e+00 1.980e+03 0.000 1.0000
## C5 -7.005e+00 1.276e+00 2.302e+03 -5.488 4.52e-08 ***
## CNS_Score.c:C1 -1.521e-01 6.012e-02 2.401e+03 -2.530 0.0115 *
## CNS_Score.c:C2 2.268e-01 8.859e-02 2.291e+03 2.560 0.0105 *
## CNS_Score.c:C3 -1.424e-02 9.341e-02 2.452e+03 -0.152 0.8789
## CNS_Score.c:C4 -3.667e-16 9.314e-02 1.980e+03 0.000 1.0000
## CNS_Score.c:C5 5.287e-02 1.000e-01 2.291e+03 0.529 0.5972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c -0.006
## C1 -0.041 0.007
## C2 -0.004 0.004 -0.011
## C3 -0.020 0.012 0.041 0.015
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 0.004 0.033 -0.026 0.018 0.000
## CNS_Scr.:C1 0.007 -0.034 -0.017 0.013 -0.018 0.000 0.007
## CNS_Scr.:C2 0.004 0.011 0.012 -0.001 0.002 0.000 -0.015 0.034
## CNS_Scr.:C3 0.012 -0.007 -0.018 0.003 -0.020 0.000 0.001 0.022
## CNS_Scr.:C4 0.000 0.000 0.000 0.000 0.000 -0.038 0.000 0.000
## CNS_Scr.:C5 0.004 -0.008 0.009 -0.014 0.002 0.000 -0.025 -0.005
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 0.024
## CNS_Scr.:C4 0.000 0.000
## CNS_Scr.:C5 0.027 0.007 0.000tab_model(modA.923,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.68 | 0.62 | 62.46 – 64.90 | 102.21 | <0.001 |
| CNS Score c | 0.20 | 0.05 | 0.10 – 0.30 | 3.99 | <0.001 |
| C1 | -4.48 | 0.77 | -5.99 – -2.98 | -5.84 | <0.001 |
| C2 | 17.77 | 1.10 | 15.61 – 19.93 | 16.16 | <0.001 |
| C3 | 0.34 | 1.17 | -1.96 – 2.63 | 0.29 | 0.773 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -7.00 | 1.28 | -9.51 – -4.50 | -5.49 | <0.001 |
| CNS Score c * C1 | -0.15 | 0.06 | -0.27 – -0.03 | -2.53 | 0.011 |
| CNS Score c * C2 | 0.23 | 0.09 | 0.05 – 0.40 | 2.56 | 0.011 |
| CNS Score c * C3 | -0.01 | 0.09 | -0.20 – 0.17 | -0.15 | 0.879 |
| CNS Score c * C4 | -0.00 | 0.09 | -0.18 – 0.18 | -0.00 | 1.000 |
| CNS Score c * C5 | 0.05 | 0.10 | -0.14 – 0.25 | 0.53 | 0.597 |
| Random Effects | |||||
| σ2 | 350.48 | ||||
| τ00 id | 264.58 | ||||
| ICC | 0.43 | ||||
| N id | 1002 | ||||
| Observations | 2983 | ||||
| Marginal R2 / Conditional R2 | 0.078 / 0.475 | ||||
Q.2 (CONNECTEDNESS TO NATURE) Does connectedness to nature depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.923 <- lmer(FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c*C1 + CNS_Score.c*C2 + CNS_Score.c*C3 + CNS_Score.c*C4 + CNS_Score.c*C5 + (1|id), data = L)
summary(modA.923)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CNS_Score.c + C1 + C2 + C3 + C4 + C5 + CNS_Score.c * C1 +
## CNS_Score.c * C2 + CNS_Score.c * C3 + CNS_Score.c * C4 +
## CNS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27104.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0231 -0.4970 0.0547 0.5518 3.3053
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 264.6 16.27
## Residual 350.5 18.72
## Number of obs: 2983, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.368e+01 6.231e-01 9.941e+02 102.207 < 2e-16 ***
## CNS_Score.c 1.992e-01 4.987e-02 9.769e+02 3.994 6.98e-05 ***
## C1 -4.481e+00 7.679e-01 2.472e+03 -5.835 6.07e-09 ***
## C2 1.777e+01 1.100e+00 2.312e+03 16.160 < 2e-16 ***
## C3 3.373e-01 1.170e+00 2.457e+03 0.288 0.7731
## C4 2.810e-16 1.187e+00 1.980e+03 0.000 1.0000
## C5 -7.005e+00 1.276e+00 2.302e+03 -5.488 4.52e-08 ***
## CNS_Score.c:C1 -1.521e-01 6.012e-02 2.401e+03 -2.530 0.0115 *
## CNS_Score.c:C2 2.268e-01 8.859e-02 2.291e+03 2.560 0.0105 *
## CNS_Score.c:C3 -1.424e-02 9.341e-02 2.452e+03 -0.152 0.8789
## CNS_Score.c:C4 -3.667e-16 9.314e-02 1.980e+03 0.000 1.0000
## CNS_Score.c:C5 5.287e-02 1.000e-01 2.291e+03 0.529 0.5972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 C5 CNS_S.:C1
## CNS_Score.c -0.006
## C1 -0.041 0.007
## C2 -0.004 0.004 -0.011
## C3 -0.020 0.012 0.041 0.015
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 0.004 0.033 -0.026 0.018 0.000
## CNS_Scr.:C1 0.007 -0.034 -0.017 0.013 -0.018 0.000 0.007
## CNS_Scr.:C2 0.004 0.011 0.012 -0.001 0.002 0.000 -0.015 0.034
## CNS_Scr.:C3 0.012 -0.007 -0.018 0.003 -0.020 0.000 0.001 0.022
## CNS_Scr.:C4 0.000 0.000 0.000 0.000 0.000 -0.038 0.000 0.000
## CNS_Scr.:C5 0.004 -0.008 0.009 -0.014 0.002 0.000 -0.025 -0.005
## CNS_S.:C2 CNS_S.:C3 CNS_S.:C4
## CNS_Score.c
## C1
## C2
## C3
## C4
## C5
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3 0.024
## CNS_Scr.:C4 0.000 0.000
## CNS_Scr.:C5 0.027 0.007 0.000tab_model(modA.923,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.68 | 0.62 | 62.46 – 64.90 | 102.21 | <0.001 |
| CNS Score c | 0.20 | 0.05 | 0.10 – 0.30 | 3.99 | <0.001 |
| C1 | -4.48 | 0.77 | -5.99 – -2.98 | -5.84 | <0.001 |
| C2 | 17.77 | 1.10 | 15.61 – 19.93 | 16.16 | <0.001 |
| C3 | 0.34 | 1.17 | -1.96 – 2.63 | 0.29 | 0.773 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -7.00 | 1.28 | -9.51 – -4.50 | -5.49 | <0.001 |
| CNS Score c * C1 | -0.15 | 0.06 | -0.27 – -0.03 | -2.53 | 0.011 |
| CNS Score c * C2 | 0.23 | 0.09 | 0.05 – 0.40 | 2.56 | 0.011 |
| CNS Score c * C3 | -0.01 | 0.09 | -0.20 – 0.17 | -0.15 | 0.879 |
| CNS Score c * C4 | -0.00 | 0.09 | -0.18 – 0.18 | -0.00 | 1.000 |
| CNS Score c * C5 | 0.05 | 0.10 | -0.14 – 0.25 | 0.53 | 0.597 |
| Random Effects | |||||
| σ2 | 350.48 | ||||
| τ00 id | 264.58 | ||||
| ICC | 0.43 | ||||
| N id | 1002 | ||||
| Observations | 2983 | ||||
| Marginal R2 / Conditional R2 | 0.078 / 0.475 | ||||
Climate Change Belief
Q.1 (CLIMATE CHANGE BELIEF) How does climate change belief predict familiarity/understanding, over and above burger contrasts?
modA.924 <- lmer(FR ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)
summary(modA.924)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CCBelief_Score.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c *
## C1 + CCBelief_Score.c * C2 + CCBelief_Score.c * C3 + CCBelief_Score.c *
## C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27037.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9670 -0.4872 0.0770 0.5646 3.1706
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 241.1 15.53
## Residual 351.4 18.75
## Number of obs: 2981, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.366e+01 6.043e-01 9.911e+02 105.358 < 2e-16 ***
## CCBelief_Score.c 2.325e-01 2.586e-02 9.817e+02 8.991 < 2e-16 ***
## C1 -4.591e+00 7.659e-01 2.494e+03 -5.995 2.33e-09 ***
## C2 1.764e+01 1.100e+00 2.330e+03 16.043 < 2e-16 ***
## C3 1.746e-01 1.166e+00 2.481e+03 0.150 0.881
## C4 -3.400e-16 1.188e+00 1.980e+03 0.000 1.000
## C5 -7.060e+00 1.276e+00 2.318e+03 -5.533 3.50e-08 ***
## CCBelief_Score.c:C1 4.846e-02 3.248e-02 2.460e+03 1.492 0.136
## CCBelief_Score.c:C2 2.388e-02 4.654e-02 2.299e+03 0.513 0.608
## CCBelief_Score.c:C3 -1.561e-02 5.084e-02 2.491e+03 -0.307 0.759
## CCBelief_Score.c:C4 -6.545e-16 5.040e-02 1.980e+03 0.000 1.000
## CCBelief_Score.c:C5 7.058e-02 5.363e-02 2.305e+03 1.316 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 C5 CCB_S.:C1
## CCBlf_Scr.c 0.001
## C1 -0.040 0.000
## C2 -0.004 -0.012 -0.010
## C3 -0.019 -0.004 0.038 0.015
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 -0.011 0.033 -0.027 0.018 0.000
## CCBlf_S.:C1 0.000 -0.045 0.004 -0.023 0.007 0.000 -0.008
## CCBlf_S.:C2 -0.011 -0.001 -0.022 -0.024 -0.003 0.000 0.021 0.016
## CCBlf_S.:C3 -0.004 -0.002 0.007 -0.003 0.000 0.000 0.011 0.013
## CCBlf_S.:C4 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.000
## CCBlf_S.:C5 -0.011 0.017 -0.008 0.021 0.011 0.000 0.030 0.033
## CCB_S.:C2 CCB_S.:C3 CCB_S.:C4
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## C5
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3 0.018
## CCBlf_S.:C4 0.000 0.000
## CCBlf_S.:C5 -0.033 0.006 0.000tab_model(modA.924,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.66 | 0.60 | 62.48 – 64.85 | 105.36 | <0.001 |
| CCBelief Score c | 0.23 | 0.03 | 0.18 – 0.28 | 8.99 | <0.001 |
| C1 | -4.59 | 0.77 | -6.09 – -3.09 | -6.00 | <0.001 |
| C2 | 17.64 | 1.10 | 15.49 – 19.80 | 16.04 | <0.001 |
| C3 | 0.17 | 1.17 | -2.11 – 2.46 | 0.15 | 0.881 |
| C4 | -0.00 | 1.19 | -2.33 – 2.33 | -0.00 | 1.000 |
| C5 | -7.06 | 1.28 | -9.56 – -4.56 | -5.53 | <0.001 |
| CCBelief Score c * C1 | 0.05 | 0.03 | -0.02 – 0.11 | 1.49 | 0.136 |
| CCBelief Score c * C2 | 0.02 | 0.05 | -0.07 – 0.12 | 0.51 | 0.608 |
| CCBelief Score c * C3 | -0.02 | 0.05 | -0.12 – 0.08 | -0.31 | 0.759 |
| CCBelief Score c * C4 | -0.00 | 0.05 | -0.10 – 0.10 | -0.00 | 1.000 |
| CCBelief Score c * C5 | 0.07 | 0.05 | -0.03 – 0.18 | 1.32 | 0.188 |
| Random Effects | |||||
| σ2 | 351.42 | ||||
| τ00 id | 241.05 | ||||
| ICC | 0.41 | ||||
| N id | 1001 | ||||
| Observations | 2981 | ||||
| Marginal R2 / Conditional R2 | 0.113 / 0.474 | ||||
Q.2 (CLIMATE CHANGE BELIEF) Does climate change belief depend on perceptinos of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.9245 <- lmer(FR ~ CCBelief_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + CCBelief_Score.c*C1 + CCBelief_Score.c*C2 + CCBelief_Score.c*C3 + CCBelief_Score.c*C4 + CCBelief_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9245)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ CCBelief_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 +
## CCBelief_Score.c * C1 + CCBelief_Score.c * C2 + CCBelief_Score.c *
## C3 + CCBelief_Score.c * C4 + CCBelief_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21604
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7920 -0.5323 0.0718 0.5812 2.9511
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 227.7 15.09
## Residual 362.5 19.04
## Number of obs: 2369, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.313e+01 6.590e-01 1.174e+03 95.792
## CCBelief_Score.c 2.096e-01 2.843e-02 1.197e+03 7.372
## Naturalness.c 1.720e-01 2.235e-02 2.106e+03 7.697
## C1 -3.217e+00 9.831e-01 1.921e+03 -3.272
## C2 2.089e+01 1.713e+00 2.006e+03 12.189
## C3 8.110e-01 1.364e+00 1.937e+03 0.595
## C4 1.524e+00 1.731e+00 1.597e+03 0.880
## C5 -1.012e+01 1.378e+00 1.769e+03 -7.345
## CCBelief_Score.c:Naturalness.c -2.193e-04 8.768e-04 2.213e+03 -0.250
## CCBelief_Score.c:C1 2.910e-02 4.319e-02 1.875e+03 0.674
## CCBelief_Score.c:C2 -9.104e-03 7.485e-02 2.009e+03 -0.122
## CCBelief_Score.c:C3 -2.110e-03 6.027e-02 1.944e+03 -0.035
## CCBelief_Score.c:C4 6.670e-02 7.543e-02 1.603e+03 0.884
## CCBelief_Score.c:C5 1.147e-02 5.687e-02 1.710e+03 0.202
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c 3.12e-13 ***
## Naturalness.c 2.12e-14 ***
## C1 0.00109 **
## C2 < 2e-16 ***
## C3 0.55206
## C4 0.37886
## C5 3.13e-13 ***
## CCBelief_Score.c:Naturalness.c 0.80248
## CCBelief_Score.c:C1 0.50059
## CCBelief_Score.c:C2 0.90321
## CCBelief_Score.c:C3 0.97207
## CCBelief_Score.c:C4 0.37665
## CCBelief_Score.c:C5 0.84023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9245,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.13 | 0.66 | 61.83 – 64.42 | 95.79 | <0.001 |
| CCBelief Score c | 0.21 | 0.03 | 0.15 – 0.27 | 7.37 | <0.001 |
| Naturalness c | 0.17 | 0.02 | 0.13 – 0.22 | 7.70 | <0.001 |
| C1 | -3.22 | 0.98 | -5.14 – -1.29 | -3.27 | 0.001 |
| C2 | 20.89 | 1.71 | 17.53 – 24.25 | 12.19 | <0.001 |
| C3 | 0.81 | 1.36 | -1.86 – 3.48 | 0.59 | 0.552 |
| C4 | 1.52 | 1.73 | -1.87 – 4.92 | 0.88 | 0.379 |
| C5 | -10.12 | 1.38 | -12.83 – -7.42 | -7.34 | <0.001 |
|
CCBelief Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.25 | 0.802 |
| CCBelief Score c * C1 | 0.03 | 0.04 | -0.06 – 0.11 | 0.67 | 0.501 |
| CCBelief Score c * C2 | -0.01 | 0.07 | -0.16 – 0.14 | -0.12 | 0.903 |
| CCBelief Score c * C3 | -0.00 | 0.06 | -0.12 – 0.12 | -0.04 | 0.972 |
| CCBelief Score c * C4 | 0.07 | 0.08 | -0.08 – 0.21 | 0.88 | 0.377 |
| CCBelief Score c * C5 | 0.01 | 0.06 | -0.10 – 0.12 | 0.20 | 0.840 |
| Random Effects | |||||
| σ2 | 362.51 | ||||
| τ00 id | 227.70 | ||||
| ICC | 0.39 | ||||
| N id | 1001 | ||||
| Observations | 2369 | ||||
| Marginal R2 / Conditional R2 | 0.121 / 0.460 | ||||
Disgust Sensitivity
Q.1 (DISGUST SENSITIVITY) How does sensitivity to disgust predict familiarity/understanding difference, above and beyond burger contrasts?
modA.925 <- lmer(FR ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)
summary(modA.925)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ DS_Score.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c * C1 +
## DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 + DS_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27116.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8042 -0.5028 0.0672 0.5429 3.2552
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.3 16.44
## Residual 352.0 18.76
## Number of obs: 2981, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.369e+01 6.285e-01 9.940e+02 101.339 < 2e-16 ***
## DS_Score.c -2.641e-02 3.026e-02 1.002e+03 -0.873 0.3831
## C1 -4.637e+00 7.710e-01 2.468e+03 -6.014 2.08e-09 ***
## C2 1.771e+01 1.105e+00 2.307e+03 16.034 < 2e-16 ***
## C3 1.597e-01 1.174e+00 2.452e+03 0.136 0.8918
## C4 -7.723e-16 1.190e+00 1.979e+03 0.000 1.0000
## C5 -6.912e+00 1.281e+00 2.298e+03 -5.394 7.58e-08 ***
## DS_Score.c:C1 1.612e-02 3.717e-02 2.455e+03 0.434 0.6646
## DS_Score.c:C2 -8.748e-02 5.272e-02 2.328e+03 -1.659 0.0972 .
## DS_Score.c:C3 8.193e-02 5.714e-02 2.500e+03 1.434 0.1517
## DS_Score.c:C4 7.453e-16 5.795e-02 1.979e+03 0.000 1.0000
## DS_Score.c:C5 -2.125e-02 6.251e-02 2.298e+03 -0.340 0.7339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 C5 DS_S.:C1 DS_S.:C2
## DS_Score.c -0.005
## C1 -0.041 0.019
## C2 -0.004 0.014 -0.011
## C3 -0.020 0.005 0.040 0.016
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 -0.013 0.035 -0.027 0.018 0.000
## DS_Scr.c:C1 0.019 -0.043 -0.035 0.034 -0.002 0.000 -0.015
## DS_Scr.c:C2 0.014 -0.017 0.036 0.046 -0.001 0.000 0.017 -0.046
## DS_Scr.c:C3 0.005 -0.030 -0.001 -0.001 -0.034 0.000 0.019 0.052 0.025
## DS_Scr.c:C4 0.000 0.000 0.000 0.000 0.000 -0.042 0.000 0.000 0.000
## DS_Scr.c:C5 -0.012 -0.008 -0.013 0.017 0.019 0.000 -0.002 -0.004 0.014
## DS_S.:C3 DS_S.:C4
## DS_Score.c
## C1
## C2
## C3
## C4
## C5
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 0.000
## DS_Scr.c:C5 0.014 0.000tab_model(modA.925,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.69 | 0.63 | 62.46 – 64.92 | 101.34 | <0.001 |
| DS Score c | -0.03 | 0.03 | -0.09 – 0.03 | -0.87 | 0.383 |
| C1 | -4.64 | 0.77 | -6.15 – -3.13 | -6.01 | <0.001 |
| C2 | 17.71 | 1.10 | 15.55 – 19.88 | 16.03 | <0.001 |
| C3 | 0.16 | 1.17 | -2.14 – 2.46 | 0.14 | 0.892 |
| C4 | -0.00 | 1.19 | -2.33 – 2.33 | -0.00 | 1.000 |
| C5 | -6.91 | 1.28 | -9.42 – -4.40 | -5.39 | <0.001 |
| DS Score c * C1 | 0.02 | 0.04 | -0.06 – 0.09 | 0.43 | 0.665 |
| DS Score c * C2 | -0.09 | 0.05 | -0.19 – 0.02 | -1.66 | 0.097 |
| DS Score c * C3 | 0.08 | 0.06 | -0.03 – 0.19 | 1.43 | 0.152 |
| DS Score c * C4 | 0.00 | 0.06 | -0.11 – 0.11 | 0.00 | 1.000 |
| DS Score c * C5 | -0.02 | 0.06 | -0.14 – 0.10 | -0.34 | 0.734 |
| Random Effects | |||||
| σ2 | 351.97 | ||||
| τ00 id | 270.26 | ||||
| ICC | 0.43 | ||||
| N id | 1001 | ||||
| Observations | 2981 | ||||
| Marginal R2 / Conditional R2 | 0.068 / 0.473 | ||||
Q.2 (DISGUST SENSITIVITY) Does disgust sensitivity depend on perceptions of naturalness in predicting familiarity/understanding, above and beyond burger contrasts?
modA.9258 <- lmer(FR ~ DS_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c*C1 + DS_Score.c*C2 + DS_Score.c*C3 + DS_Score.c*C4 + DS_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9258)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FR ~ DS_Score.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + DS_Score.c *
## C1 + DS_Score.c * C2 + DS_Score.c * C3 + DS_Score.c * C4 +
## DS_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21666.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7356 -0.5184 0.0448 0.5794 3.0985
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 253.3 15.92
## Residual 362.4 19.04
## Number of obs: 2369, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.311e+01 6.793e-01 1.170e+03 92.907 < 2e-16 ***
## DS_Score.c -2.014e-02 3.261e-02 1.167e+03 -0.618 0.53700
## Naturalness.c 1.784e-01 2.198e-02 2.043e+03 8.116 8.22e-16 ***
## C1 -3.123e+00 9.889e-01 1.897e+03 -3.157 0.00162 **
## C2 2.118e+01 1.725e+00 1.975e+03 12.278 < 2e-16 ***
## C3 9.161e-01 1.369e+00 1.905e+03 0.669 0.50352
## C4 1.652e+00 1.735e+00 1.582e+03 0.952 0.34126
## C5 -1.004e+01 1.382e+00 1.742e+03 -7.265 5.59e-13 ***
## DS_Score.c:Naturalness.c -6.450e-04 9.550e-04 2.108e+03 -0.675 0.49952
## DS_Score.c:C1 2.181e-02 4.828e-02 1.869e+03 0.452 0.65143
## DS_Score.c:C2 -4.533e-03 8.107e-02 1.989e+03 -0.056 0.95542
## DS_Score.c:C3 1.019e-01 6.558e-02 1.927e+03 1.554 0.12037
## DS_Score.c:C4 4.602e-02 8.204e-02 1.566e+03 0.561 0.57495
## DS_Score.c:C5 2.425e-03 6.731e-02 1.756e+03 0.036 0.97126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9258,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.11 | 0.68 | 61.78 – 64.44 | 92.91 | <0.001 |
| DS Score c | -0.02 | 0.03 | -0.08 – 0.04 | -0.62 | 0.537 |
| Naturalness c | 0.18 | 0.02 | 0.14 – 0.22 | 8.12 | <0.001 |
| C1 | -3.12 | 0.99 | -5.06 – -1.18 | -3.16 | 0.002 |
| C2 | 21.18 | 1.72 | 17.79 – 24.56 | 12.28 | <0.001 |
| C3 | 0.92 | 1.37 | -1.77 – 3.60 | 0.67 | 0.504 |
| C4 | 1.65 | 1.74 | -1.75 – 5.05 | 0.95 | 0.341 |
| C5 | -10.04 | 1.38 | -12.75 – -7.33 | -7.27 | <0.001 |
|
DS Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.68 | 0.500 |
| DS Score c * C1 | 0.02 | 0.05 | -0.07 – 0.12 | 0.45 | 0.651 |
| DS Score c * C2 | -0.00 | 0.08 | -0.16 – 0.15 | -0.06 | 0.955 |
| DS Score c * C3 | 0.10 | 0.07 | -0.03 – 0.23 | 1.55 | 0.120 |
| DS Score c * C4 | 0.05 | 0.08 | -0.11 – 0.21 | 0.56 | 0.575 |
| DS Score c * C5 | 0.00 | 0.07 | -0.13 – 0.13 | 0.04 | 0.971 |
| Random Effects | |||||
| σ2 | 362.41 | ||||
| τ00 id | 253.29 | ||||
| ICC | 0.41 | ||||
| N id | 1001 | ||||
| Observations | 2369 | ||||
| Marginal R2 / Conditional R2 | 0.083 / 0.460 | ||||
Collectivism
Q.1 (COLLECTIVISM) How does collectivism predict familiarity/understanding, over and above burger contrasts?
modA.926 <- lmer(FR ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)
summary(modA.926)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FR ~ Collectivism_Score.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c *
## C1 + Collectivism_Score.c * C2 + Collectivism_Score.c * C3 +
## Collectivism_Score.c * C4 + Collectivism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27088.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9285 -0.4849 0.0552 0.5539 3.2153
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 246.0 15.68
## Residual 351.8 18.76
## Number of obs: 2985, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.369e+01 6.079e-01 9.916e+02 104.765 < 2e-16 ***
## Collectivism_Score.c 2.381e-01 2.960e-02 9.762e+02 8.045 2.49e-15 ***
## C1 -4.597e+00 7.666e-01 2.492e+03 -5.997 2.30e-09 ***
## C2 1.771e+01 1.099e+00 2.327e+03 16.115 < 2e-16 ***
## C3 8.945e-02 1.167e+00 2.478e+03 0.077 0.9389
## C4 1.906e-15 1.189e+00 1.980e+03 0.000 1.0000
## C5 -6.988e+00 1.276e+00 2.315e+03 -5.474 4.86e-08 ***
## Collectivism_Score.c:C1 3.052e-03 3.663e-02 2.420e+03 0.083 0.9336
## Collectivism_Score.c:C2 1.175e-01 5.339e-02 2.306e+03 2.200 0.0279 *
## Collectivism_Score.c:C3 -1.719e-02 5.760e-02 2.473e+03 -0.298 0.7654
## Collectivism_Score.c:C4 1.002e-15 5.561e-02 1.980e+03 0.000 1.0000
## Collectivism_Score.c:C5 -1.947e-03 6.206e-02 2.303e+03 -0.031 0.9750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C5 C_S.:C1 C_S.:C2
## Cllctvsm_S. 0.002
## C1 -0.041 -0.004
## C2 -0.005 -0.004 -0.012
## C3 -0.019 -0.007 0.039 0.016
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 -0.012 0.033 -0.026 0.017 0.000
## Cllct_S.:C1 -0.005 -0.026 0.008 -0.013 0.015 0.000 -0.020
## Cllct_S.:C2 -0.004 -0.007 -0.013 -0.012 -0.004 0.000 0.021 -0.003
## Cllct_S.:C3 -0.007 0.014 0.015 -0.004 -0.007 0.000 0.005 -0.027 0.017
## Cllct_S.:C4 0.000 0.000 0.000 0.000 0.000 0.019 0.000 0.000 0.000
## Cllct_S.:C5 -0.012 0.003 -0.021 0.021 0.004 0.000 0.016 0.015 0.000
## C_S.:C3 C_S.:C4
## Cllctvsm_S.
## C1
## C2
## C3
## C4
## C5
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3
## Cllct_S.:C4 0.000
## Cllct_S.:C5 0.013 0.000tab_model(modA.926,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.69 | 0.61 | 62.50 – 64.88 | 104.76 | <0.001 |
| Collectivism Score c | 0.24 | 0.03 | 0.18 – 0.30 | 8.04 | <0.001 |
| C1 | -4.60 | 0.77 | -6.10 – -3.09 | -6.00 | <0.001 |
| C2 | 17.71 | 1.10 | 15.55 – 19.86 | 16.11 | <0.001 |
| C3 | 0.09 | 1.17 | -2.20 – 2.38 | 0.08 | 0.939 |
| C4 | 0.00 | 1.19 | -2.33 – 2.33 | 0.00 | 1.000 |
| C5 | -6.99 | 1.28 | -9.49 – -4.48 | -5.47 | <0.001 |
| Collectivism Score c * C1 | 0.00 | 0.04 | -0.07 – 0.07 | 0.08 | 0.934 |
| Collectivism Score c * C2 | 0.12 | 0.05 | 0.01 – 0.22 | 2.20 | 0.028 |
| Collectivism Score c * C3 | -0.02 | 0.06 | -0.13 – 0.10 | -0.30 | 0.765 |
| Collectivism Score c * C4 | 0.00 | 0.06 | -0.11 – 0.11 | 0.00 | 1.000 |
| Collectivism Score c * C5 | -0.00 | 0.06 | -0.12 – 0.12 | -0.03 | 0.975 |
| Random Effects | |||||
| σ2 | 351.76 | ||||
| τ00 id | 246.01 | ||||
| ICC | 0.41 | ||||
| N id | 1003 | ||||
| Observations | 2985 | ||||
| Marginal R2 / Conditional R2 | 0.104 / 0.473 | ||||
Q.2 (COLLECTIVISM) Does collectivism depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.9267 <- lmer(FR ~ Collectivism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Collectivism_Score.c*C1 + Collectivism_Score.c*C2 + Collectivism_Score.c*C3 + Collectivism_Score.c*C4 + Collectivism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9267)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Collectivism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + Collectivism_Score.c * C1 + Collectivism_Score.c * C2 +
## Collectivism_Score.c * C3 + Collectivism_Score.c * C4 + Collectivism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21634.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7674 -0.5092 0.0506 0.5775 3.0129
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 228.7 15.12
## Residual 362.9 19.05
## Number of obs: 2372, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.310e+01 6.593e-01 1.170e+03 95.697
## Collectivism_Score.c 2.442e-01 3.182e-02 1.129e+03 7.673
## Naturalness.c 1.849e-01 2.205e-02 2.060e+03 8.388
## C1 -2.975e+00 9.839e-01 1.921e+03 -3.024
## C2 2.122e+01 1.709e+00 2.000e+03 12.416
## C3 7.366e-01 1.360e+00 1.935e+03 0.542
## C4 1.748e+00 1.732e+00 1.594e+03 1.009
## C5 -1.011e+01 1.376e+00 1.761e+03 -7.348
## Collectivism_Score.c:Naturalness.c -1.452e-03 9.349e-04 1.985e+03 -1.553
## Collectivism_Score.c:C1 -5.934e-02 4.734e-02 1.873e+03 -1.254
## Collectivism_Score.c:C2 7.011e-02 8.501e-02 1.988e+03 0.825
## Collectivism_Score.c:C3 -1.081e-02 6.606e-02 1.931e+03 -0.164
## Collectivism_Score.c:C4 -6.042e-02 8.039e-02 1.579e+03 -0.752
## Collectivism_Score.c:C5 -1.044e-02 6.687e-02 1.751e+03 -0.156
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c 3.61e-14 ***
## Naturalness.c < 2e-16 ***
## C1 0.00253 **
## C2 < 2e-16 ***
## C3 0.58815
## C4 0.31304
## C5 3.05e-13 ***
## Collectivism_Score.c:Naturalness.c 0.12049
## Collectivism_Score.c:C1 0.21018
## Collectivism_Score.c:C2 0.40963
## Collectivism_Score.c:C3 0.87005
## Collectivism_Score.c:C4 0.45242
## Collectivism_Score.c:C5 0.87594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9267,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.10 | 0.66 | 61.80 – 64.39 | 95.70 | <0.001 |
| Collectivism Score c | 0.24 | 0.03 | 0.18 – 0.31 | 7.67 | <0.001 |
| Naturalness c | 0.18 | 0.02 | 0.14 – 0.23 | 8.39 | <0.001 |
| C1 | -2.97 | 0.98 | -4.90 – -1.05 | -3.02 | 0.003 |
| C2 | 21.22 | 1.71 | 17.87 – 24.57 | 12.42 | <0.001 |
| C3 | 0.74 | 1.36 | -1.93 – 3.40 | 0.54 | 0.588 |
| C4 | 1.75 | 1.73 | -1.65 – 5.14 | 1.01 | 0.313 |
| C5 | -10.11 | 1.38 | -12.81 – -7.42 | -7.35 | <0.001 |
|
Collectivism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.55 | 0.120 |
| Collectivism Score c * C1 | -0.06 | 0.05 | -0.15 – 0.03 | -1.25 | 0.210 |
| Collectivism Score c * C2 | 0.07 | 0.09 | -0.10 – 0.24 | 0.82 | 0.410 |
| Collectivism Score c * C3 | -0.01 | 0.07 | -0.14 – 0.12 | -0.16 | 0.870 |
| Collectivism Score c * C4 | -0.06 | 0.08 | -0.22 – 0.10 | -0.75 | 0.452 |
| Collectivism Score c * C5 | -0.01 | 0.07 | -0.14 – 0.12 | -0.16 | 0.876 |
| Random Effects | |||||
| σ2 | 362.89 | ||||
| τ00 id | 228.71 | ||||
| ICC | 0.39 | ||||
| N id | 1003 | ||||
| Observations | 2372 | ||||
| Marginal R2 / Conditional R2 | 0.117 / 0.458 | ||||
Individualism
Q.1 (INDIVIDUALISM) How does individualism predict familiarity/understanding, over and above burger contrasts?
modA.927 <- lmer(FR ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)
summary(modA.927)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FR ~ Individualism_Score.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c *
## C1 + Individualism_Score.c * C2 + Individualism_Score.c *
## C3 + Individualism_Score.c * C4 + Individualism_Score.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27042
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9351 -0.4738 0.0688 0.5478 3.3623
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 243.4 15.60
## Residual 348.9 18.68
## Number of obs: 2983, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.366e+01 6.052e-01 9.903e+02 105.177 < 2e-16 ***
## Individualism_Score.c 2.862e-01 3.366e-02 9.883e+02 8.501 < 2e-16 ***
## C1 -4.487e+00 7.634e-01 2.490e+03 -5.878 4.71e-09 ***
## C2 1.783e+01 1.094e+00 2.325e+03 16.289 < 2e-16 ***
## C3 2.974e-01 1.162e+00 2.477e+03 0.256 0.7981
## C4 -1.503e-16 1.184e+00 1.978e+03 0.000 1.0000
## C5 -6.825e+00 1.272e+00 2.314e+03 -5.364 8.94e-08 ***
## Individualism_Score.c:C1 -1.155e-01 4.224e-02 2.459e+03 -2.734 0.0063 **
## Individualism_Score.c:C2 2.512e-01 6.060e-02 2.325e+03 4.145 3.52e-05 ***
## Individualism_Score.c:C3 -8.999e-02 6.564e-02 2.497e+03 -1.371 0.1705
## Individualism_Score.c:C4 7.760e-16 6.444e-02 1.978e+03 0.000 1.0000
## Individualism_Score.c:C5 8.654e-03 7.189e-02 2.313e+03 0.120 0.9042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 C5 I_S.:C1 I_S.:C2
## Indvdlsm_S. -0.003
## C1 -0.040 0.003
## C2 -0.006 0.004 -0.012
## C3 -0.020 0.011 0.039 0.016
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.014 -0.012 0.034 -0.027 0.018 0.000
## Indvd_S.:C1 0.002 -0.029 -0.008 0.006 -0.015 0.000 -0.023
## Indvd_S.:C2 0.004 -0.021 0.006 0.001 -0.005 0.000 0.023 -0.033
## Indvd_S.:C3 0.012 0.001 -0.015 -0.006 -0.007 0.000 0.002 0.000 0.018
## Indvd_S.:C4 0.000 0.000 0.000 0.000 0.000 -0.025 0.000 0.000 0.000
## Indvd_S.:C5 -0.012 0.004 -0.022 0.023 0.002 0.000 -0.005 0.018 -0.010
## I_S.:C3 I_S.:C4
## Indvdlsm_S.
## C1
## C2
## C3
## C4
## C5
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3
## Indvd_S.:C4 0.000
## Indvd_S.:C5 0.017 0.000tab_model(modA.927,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.66 | 0.61 | 62.47 – 64.84 | 105.18 | <0.001 |
| Individualism Score c | 0.29 | 0.03 | 0.22 – 0.35 | 8.50 | <0.001 |
| C1 | -4.49 | 0.76 | -5.98 – -2.99 | -5.88 | <0.001 |
| C2 | 17.83 | 1.09 | 15.68 – 19.97 | 16.29 | <0.001 |
| C3 | 0.30 | 1.16 | -1.98 – 2.58 | 0.26 | 0.798 |
| C4 | -0.00 | 1.18 | -2.32 – 2.32 | -0.00 | 1.000 |
| C5 | -6.83 | 1.27 | -9.32 – -4.33 | -5.36 | <0.001 |
|
Individualism Score c * C1 |
-0.12 | 0.04 | -0.20 – -0.03 | -2.73 | 0.006 |
|
Individualism Score c * C2 |
0.25 | 0.06 | 0.13 – 0.37 | 4.14 | <0.001 |
|
Individualism Score c * C3 |
-0.09 | 0.07 | -0.22 – 0.04 | -1.37 | 0.171 |
|
Individualism Score c * C4 |
0.00 | 0.06 | -0.13 – 0.13 | 0.00 | 1.000 |
|
Individualism Score c * C5 |
0.01 | 0.07 | -0.13 – 0.15 | 0.12 | 0.904 |
| Random Effects | |||||
| σ2 | 348.86 | ||||
| τ00 id | 243.42 | ||||
| ICC | 0.41 | ||||
| N id | 1002 | ||||
| Observations | 2983 | ||||
| Marginal R2 / Conditional R2 | 0.112 / 0.477 | ||||
Q.2 (INDIVIDUALISM) Does individualism depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
modA.9275 <- lmer(FR ~ Individualism_Score.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 + Individualism_Score.c*C1 + Individualism_Score.c*C2 + Individualism_Score.c*C3 + Individualism_Score.c*C4 + Individualism_Score.c*C5 + (1|id), data = L)## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.9275)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Individualism_Score.c * Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + Individualism_Score.c * C1 + Individualism_Score.c *
## C2 + Individualism_Score.c * C3 + Individualism_Score.c *
## C4 + Individualism_Score.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21603.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7879 -0.5096 0.0709 0.5633 3.1064
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 232.4 15.25
## Residual 358.7 18.94
## Number of obs: 2370, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.310e+01 6.608e-01 1.171e+03 95.495
## Individualism_Score.c 2.723e-01 3.630e-02 1.126e+03 7.503
## Naturalness.c 1.788e-01 2.295e-02 2.084e+03 7.792
## C1 -3.042e+00 9.783e-01 1.919e+03 -3.110
## C2 2.129e+01 1.704e+00 1.995e+03 12.492
## C3 9.930e-01 1.354e+00 1.928e+03 0.733
## C4 1.539e+00 1.723e+00 1.593e+03 0.893
## C5 -1.004e+01 1.371e+00 1.755e+03 -7.326
## Individualism_Score.c:Naturalness.c -2.307e-06 1.147e-03 2.107e+03 -0.002
## Individualism_Score.c:C1 -1.299e-01 5.264e-02 1.885e+03 -2.467
## Individualism_Score.c:C2 2.439e-01 9.004e-02 1.985e+03 2.708
## Individualism_Score.c:C3 -7.643e-02 7.537e-02 1.943e+03 -1.014
## Individualism_Score.c:C4 4.308e-02 9.213e-02 1.591e+03 0.468
## Individualism_Score.c:C5 -5.862e-02 7.575e-02 1.730e+03 -0.774
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 1.26e-13 ***
## Naturalness.c 1.03e-14 ***
## C1 0.00190 **
## C2 < 2e-16 ***
## C3 0.46351
## C4 0.37181
## C5 3.59e-13 ***
## Individualism_Score.c:Naturalness.c 0.99840
## Individualism_Score.c:C1 0.01370 *
## Individualism_Score.c:C2 0.00682 **
## Individualism_Score.c:C3 0.31066
## Individualism_Score.c:C4 0.64017
## Individualism_Score.c:C5 0.43909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.9275,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 63.10 | 0.66 | 61.80 – 64.39 | 95.49 | <0.001 |
| Individualism Score c | 0.27 | 0.04 | 0.20 – 0.34 | 7.50 | <0.001 |
| Naturalness c | 0.18 | 0.02 | 0.13 – 0.22 | 7.79 | <0.001 |
| C1 | -3.04 | 0.98 | -4.96 – -1.12 | -3.11 | 0.002 |
| C2 | 21.29 | 1.70 | 17.95 – 24.63 | 12.49 | <0.001 |
| C3 | 0.99 | 1.35 | -1.66 – 3.65 | 0.73 | 0.463 |
| C4 | 1.54 | 1.72 | -1.84 – 4.92 | 0.89 | 0.372 |
| C5 | -10.04 | 1.37 | -12.73 – -7.36 | -7.33 | <0.001 |
|
Individualism Score c * Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -0.00 | 0.998 |
|
Individualism Score c * C1 |
-0.13 | 0.05 | -0.23 – -0.03 | -2.47 | 0.014 |
|
Individualism Score c * C2 |
0.24 | 0.09 | 0.07 – 0.42 | 2.71 | 0.007 |
|
Individualism Score c * C3 |
-0.08 | 0.08 | -0.22 – 0.07 | -1.01 | 0.311 |
|
Individualism Score c * C4 |
0.04 | 0.09 | -0.14 – 0.22 | 0.47 | 0.640 |
|
Individualism Score c * C5 |
-0.06 | 0.08 | -0.21 – 0.09 | -0.77 | 0.439 |
| Random Effects | |||||
| σ2 | 358.72 | ||||
| τ00 id | 232.42 | ||||
| ICC | 0.39 | ||||
| N id | 1002 | ||||
| Observations | 2370 | ||||
| Marginal R2 / Conditional R2 | 0.120 / 0.466 | ||||
Political Ideology
Q.1 (POLITICAL IDEOLOGY) How does political ideology predict familiarity/understanding, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.928 <- lmer(FR ~ Ideology.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.928)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FR ~ Ideology.c + C1 + C2 + C3 + C4 + C5 + Ideology.c * C1 +
## Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 + Ideology.c *
## C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 27078.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9145 -0.5127 0.0592 0.5358 3.3421
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 270.6 16.45
## Residual 348.7 18.67
## Number of obs: 2983, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.298e+01 9.250e-01 1.002e+03 68.092 < 2e-16 ***
## Ideology.c -6.138e-01 5.783e-01 9.982e+02 -1.061 0.288786
## C1 -6.414e+00 1.139e+00 2.484e+03 -5.629 2.01e-08 ***
## C2 2.204e+01 1.621e+00 2.311e+03 13.593 < 2e-16 ***
## C3 1.205e+00 1.743e+00 2.464e+03 0.691 0.489426
## C4 -1.256e-13 1.759e+00 1.979e+03 0.000 1.000000
## C5 -1.026e+01 1.877e+00 2.312e+03 -5.466 5.09e-08 ***
## Ideology.c:C1 -1.590e+00 7.093e-01 2.470e+03 -2.241 0.025096 *
## Ideology.c:C2 3.657e+00 1.024e+00 2.318e+03 3.572 0.000362 ***
## Ideology.c:C3 8.065e-01 1.096e+00 2.457e+03 0.736 0.461698
## Ideology.c:C4 -1.042e-13 1.086e+00 1.979e+03 0.000 1.000000
## Ideology.c:C5 -2.919e+00 1.161e+00 2.310e+03 -2.513 0.012026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 C5 Id.:C1 Id.:C2
## Ideology.c 0.735
## C1 -0.049 -0.038
## C2 -0.002 0.002 0.000
## C3 -0.019 -0.009 0.039 0.022
## C4 0.000 0.000 0.000 0.000 0.000
## C5 0.010 0.009 0.014 -0.023 0.029 0.000
## Idelgy.c:C1 -0.038 -0.047 0.740 0.009 0.020 0.000 0.014
## Idelgy.c:C2 0.002 0.007 0.010 0.736 0.015 0.000 -0.027 0.017
## Idelgy.c:C3 -0.009 -0.005 0.019 0.015 0.742 0.000 0.033 0.015 0.014
## Idelgy.c:C4 0.000 0.000 0.000 0.000 0.000 0.740 0.000 0.000 0.000
## Idelgy.c:C5 0.009 0.019 0.014 -0.027 0.033 0.000 0.734 0.040 -0.055
## Id.:C3 Id.:C4
## Ideology.c
## C1
## C2
## C3
## C4
## C5
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3
## Idelgy.c:C4 0.000
## Idelgy.c:C5 0.050 0.000tab_model(modA.928,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 62.98 | 0.92 | 61.17 – 64.80 | 68.09 | <0.001 |
| Ideology c | -0.61 | 0.58 | -1.75 – 0.52 | -1.06 | 0.289 |
| C1 | -6.41 | 1.14 | -8.65 – -4.18 | -5.63 | <0.001 |
| C2 | 22.04 | 1.62 | 18.86 – 25.22 | 13.59 | <0.001 |
| C3 | 1.20 | 1.74 | -2.21 – 4.62 | 0.69 | 0.489 |
| C4 | -0.00 | 1.76 | -3.45 – 3.45 | -0.00 | 1.000 |
| C5 | -10.26 | 1.88 | -13.94 – -6.58 | -5.47 | <0.001 |
| Ideology c * C1 | -1.59 | 0.71 | -2.98 – -0.20 | -2.24 | 0.025 |
| Ideology c * C2 | 3.66 | 1.02 | 1.65 – 5.66 | 3.57 | <0.001 |
| Ideology c * C3 | 0.81 | 1.10 | -1.34 – 2.95 | 0.74 | 0.462 |
| Ideology c * C4 | -0.00 | 1.09 | -2.13 – 2.13 | -0.00 | 1.000 |
| Ideology c * C5 | -2.92 | 1.16 | -5.20 – -0.64 | -2.51 | 0.012 |
| Random Effects | |||||
| σ2 | 348.69 | ||||
| τ00 id | 270.55 | ||||
| ICC | 0.44 | ||||
| N id | 1002 | ||||
| Observations | 2983 | ||||
| Marginal R2 / Conditional R2 | 0.072 / 0.477 | ||||
Q.2 (POLITICAL IDEOLOGY) Does political ideology depend on perceptions of naturalness in predicting familiarity/understanding, over and above burger contrasts?
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
modA.9281 <- lmer(FR ~ Ideology.c*Naturalness.c + C1 + C2 + C3 + C4 + C5 +Ideology.c*C1 + Ideology.c*C2 + Ideology.c*C3 + Ideology.c*C4 + Ideology.c*C5 + (1|id), data = L)
summary(modA.9281)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## FR ~ Ideology.c * Naturalness.c + C1 + C2 + C3 + C4 + C5 + Ideology.c *
## C1 + Ideology.c * C2 + Ideology.c * C3 + Ideology.c * C4 +
## Ideology.c * C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 21623.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7715 -0.5242 0.0321 0.5817 3.2819
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 252.3 15.88
## Residual 358.9 18.94
## Number of obs: 2371, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 62.29945 0.99812 1184.31901 62.417 < 2e-16 ***
## Ideology.c -0.70224 0.62066 1164.09761 -1.131 0.25811
## Naturalness.c 0.20114 0.03243 2081.51276 6.201 6.73e-10 ***
## C1 -4.55819 1.46377 1928.81088 -3.114 0.00187 **
## C2 26.15656 2.46335 1966.30888 10.618 < 2e-16 ***
## C3 1.92634 2.04141 1897.39335 0.944 0.34548
## C4 1.68170 2.62008 1585.80171 0.642 0.52106
## C5 -13.56009 2.01510 1753.73675 -6.729 2.30e-11 ***
## Ideology.c:Naturalness.c 0.02422 0.02256 2046.79465 1.074 0.28312
## Ideology.c:C1 -1.21326 0.90788 1943.54615 -1.336 0.18159
## Ideology.c:C2 4.36492 1.55922 1973.19107 2.799 0.00517 **
## Ideology.c:C3 0.85792 1.27611 1886.81126 0.672 0.50148
## Ideology.c:C4 0.12723 1.61587 1581.31410 0.079 0.93725
## Ideology.c:C5 -3.16454 1.27472 1756.59658 -2.483 0.01314 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(modA.9281,
show.stat = T, show.se = T)| FR | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 62.30 | 1.00 | 60.34 – 64.26 | 62.42 | <0.001 |
| Ideology c | -0.70 | 0.62 | -1.92 – 0.51 | -1.13 | 0.258 |
| Naturalness c | 0.20 | 0.03 | 0.14 – 0.26 | 6.20 | <0.001 |
| C1 | -4.56 | 1.46 | -7.43 – -1.69 | -3.11 | 0.002 |
| C2 | 26.16 | 2.46 | 21.33 – 30.99 | 10.62 | <0.001 |
| C3 | 1.93 | 2.04 | -2.08 – 5.93 | 0.94 | 0.345 |
| C4 | 1.68 | 2.62 | -3.46 – 6.82 | 0.64 | 0.521 |
| C5 | -13.56 | 2.02 | -17.51 – -9.61 | -6.73 | <0.001 |
|
Ideology c * Naturalness c |
0.02 | 0.02 | -0.02 – 0.07 | 1.07 | 0.283 |
| Ideology c * C1 | -1.21 | 0.91 | -2.99 – 0.57 | -1.34 | 0.182 |
| Ideology c * C2 | 4.36 | 1.56 | 1.31 – 7.42 | 2.80 | 0.005 |
| Ideology c * C3 | 0.86 | 1.28 | -1.64 – 3.36 | 0.67 | 0.501 |
| Ideology c * C4 | 0.13 | 1.62 | -3.04 – 3.30 | 0.08 | 0.937 |
| Ideology c * C5 | -3.16 | 1.27 | -5.66 – -0.66 | -2.48 | 0.013 |
| Random Effects | |||||
| σ2 | 358.87 | ||||
| τ00 id | 252.26 | ||||
| ICC | 0.41 | ||||
| N id | 1002 | ||||
| Observations | 2371 | ||||
| Marginal R2 / Conditional R2 | 0.089 / 0.465 | ||||