Open Science Framework, Protein Perceptions Study
Sarah Coffin, Social Psychology PhD Student, University of Colorado Boulder
2023-04-13
#Data File
PP <- read.csv("protein.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$Dem_Age, na.rm = T)## [1] 13 83describe(PP$Dem_Age, na.rm = T)## PP$Dem_Age
## n missing distinct Info Mean Gmd .05 .10
## 987 18 66 1 42.08 17.25 21 23
## .25 .50 .75 .90 .95
## 30 40 53 65 70
##
## lowest : 13 18 19 20 21, highest: 78 79 81 82 83sd(PP$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$EDU)##
## Elementary/Grammar School Middle School
## 3 13
## High School or Equivalent Vocational/Technical School (2 years)
## 310 82
## Some College College or University (4 years)
## 296 191
## Master's Degree (MS, MA, MBA, etc.) Doctoral Degree (PhD)
## 80 23
## Other
## 5length(PP$EdNum)## [1] 1005Income
# 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)
PP$Party <- PP$PP_Party
describe(PP$Party)## PP$Party
## n missing distinct Info Mean Gmd
## 1003 2 5 0.907 2.197 1.13
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 290 370 263 15 65
## Proportion 0.289 0.369 0.262 0.015 0.065PP$Party <- PP$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$AW_1
## n missing distinct Info Mean Gmd .05 .10
## 1001 4 99 0.99 69.77 30.34 16 28
## .25 .50 .75 .90 .95
## 52 75 96 100 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100range(PP$AW_1, na.rm=TRUE)## [1] 0 100describe(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$AW_2, na.rm=TRUE)## [1] 0 100#Histograms
hist(PP$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$AW_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.69 0.69 4.5 0.011 71 25 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.84
## Duhachek 0.80 0.82 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.AW_1 0.73 0.69 0.48 0.69 2.3 NA 0 0.69
## PP.AW_2 0.66 0.69 0.48 0.69 2.3 NA 0 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.AW_1 1001 0.92 0.92 0.77 0.69 70 27
## PP.AW_2 1000 0.92 0.92 0.77 0.69 71 26Aversion to Tampering with Nature
# Aversion to Tampering with Nature: How much do you agree or disagree with the following statements?
## Item 1: People who push for technological fixes to environmental problems are underestimating the risks.
## Item 2: People who say we shouldn’t tamper with nature are just being naïve.
## Item 3: Human beings have no right to meddle with the natural environment.
## Item 4: I would prefer to live in a world where humans leave nature alone.
## Item 5: Altering nature will be our downfall as a species.
# Item Definitions
PP$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$ATNS_1, main = 'People who push for technological fixes to environmental problems are underestimating the risks.')
hist(PP$ATNS_2R, main = 'People who say we shouldn’t tamper with nature are just being naïve.')
hist(PP$ATNS_3, main = 'Human beings have no right to meddle with the natural environment.')
hist(PP$ATNS_4, main = 'I would prefer to live in a world where humans leave nature alone.')
hist(PP$ATNS_5, main = 'Altering nature will be our downfall as a species.')
#Cronbach's Alpha (Item 2 reverse coded)
PP$ATNS_Scale <- data.frame(PP$ATNS_1, PP$ATNS_2R, PP$ATNS_3, PP$ATNS_4, PP$ATNS_5)
psych::alpha(PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.58 0.62 0.66
## Duhachek 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
## --------------------------------------------------------------------------------PP$ATNS_Score <- rowMeans(PP [, c("ATNS_1", "ATNS_2R", "ATNS_3", "ATNS_4", "ATNS_5")], na.rm=TRUE)
describe(PP$ATNS_Score)## PP$ATNS_Score
## n missing distinct Info Mean Gmd .05 .10
## 1002 3 313 1 62.48 19.4 34.21 42.42
## .25 .50 .75 .90 .95
## 51.20 62.00 73.40 84.38 94.79
##
## lowest : 0.0 1.0 2.4 3.6 7.2, highest: 98.8 99.2 99.6 99.8 100.0Benefit
# Benefit perception was measured with 3 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Benefit score calculated by averaging these items.
### Item 1: This is beneficial to my health.
### Item 2: This is beneficial to society.
### Item 3: This is beneficial to the environment.Grain-fed Feedlot Burger (GFFB)
#GFFB
PP$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$Benefit_1_GFFB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_GFFB, main = 'This is beneficial to society.')
PP$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$Benefit_3_GFFB, main = 'This is beneficial to the environment.')
#GFFB Benefit Scale
PP$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$Benefit_3_GFFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.88 0.78 11 0.0046 56 27 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.92
## Duhachek 0.91 0.92 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_GFFB 0.89 0.89 0.79 0.79 7.7 0.0072 NA
## PP.Benefit_2_GFFB 0.87 0.87 0.78 0.78 7.0 0.0079 NA
## PP.Benefit_3_GFFB 0.88 0.88 0.78 0.78 7.1 0.0078 NA
## med.r
## PP.Benefit_1_GFFB 0.79
## PP.Benefit_2_GFFB 0.78
## PP.Benefit_3_GFFB 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_GFFB 497 0.92 0.92 0.86 0.82 57 29
## PP.Benefit_2_GFFB 497 0.93 0.93 0.87 0.83 57 29
## PP.Benefit_3_GFFB 495 0.93 0.93 0.87 0.83 55 29hist(PP$Ben_Score_GFFB, main = 'GFFB Benefit Scale Score')
#Correlation between benefit items in scale
PP$Ben.GFFB <-cbind (PP$Benefit_1_GFFB, PP$Benefit_2_GFFB, PP$Benefit_3_GFFB)
cor(PP$Ben.GFFB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.7796516 0.7770885
## [2,] 0.7796516 1.0000000 0.7942827
## [3,] 0.7770885 0.7942827 1.0000000Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$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$Benefit_1_GFPRB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_GFPRB, main = 'This is beneficial to society.')
PP$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$Benefit_3_GFPRB, main = 'This is beneficial to the environment.')
#GFPRB Benefit Scale
PP$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$Benefit_3_GFPRB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.83 0.71 7.5 0.0065 67 24 0.71
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.87 0.88 0.89
## Duhachek 0.87 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_GFPRB 0.83 0.83 0.71 0.71 4.9 0.0108 NA
## PP.Benefit_2_GFPRB 0.82 0.82 0.69 0.69 4.5 0.0115 NA
## PP.Benefit_3_GFPRB 0.85 0.85 0.74 0.74 5.7 0.0095 NA
## med.r
## PP.Benefit_1_GFPRB 0.71
## PP.Benefit_2_GFPRB 0.69
## PP.Benefit_3_GFPRB 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_GFPRB 514 0.9 0.90 0.83 0.77 69 26
## PP.Benefit_2_GFPRB 514 0.9 0.91 0.84 0.79 68 26
## PP.Benefit_3_GFPRB 513 0.9 0.89 0.80 0.75 65 28hist(PP$Ben_Score_GFPRB, main = 'GFPRB Benefit Scale Score')
#Correlation between benefit items in scale
PP$Ben.GFPRB <-cbind (PP$Benefit_1_GFPRB, PP$Benefit_2_GFPRB, PP$Benefit_3_GFPRB)
cor(PP$Ben.GFPRB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.7390440 0.6926519
## [2,] 0.7390440 1.0000000 0.7079603
## [3,] 0.6926519 0.7079603 1.0000000Cultured beef burgers (CBB)
#CBB
PP$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$Benefit_1_CBB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_CBB, main = 'This is beneficial to society.')
PP$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$Benefit_3_CBB, main = 'This is beneficial to the environment.')
#Benefit Scale
PP$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$Benefit_3_CBB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.87 0.77 9.9 0.005 53 28 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_CBB 0.89 0.89 0.80 0.80 8.0 0.0070 NA 0.80
## PP.Benefit_2_CBB 0.86 0.86 0.75 0.75 6.0 0.0091 NA 0.75
## PP.Benefit_3_CBB 0.86 0.86 0.76 0.76 6.2 0.0088 NA 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_CBB 514 0.91 0.91 0.83 0.79 49 32
## PP.Benefit_2_CBB 514 0.92 0.93 0.87 0.83 53 30
## PP.Benefit_3_CBB 513 0.92 0.92 0.87 0.83 56 31hist(PP$Ben_Score_CBB, main = 'CBB Benefit Scale Score')
#Correlation
PP$Ben.CBB <-cbind (PP$Benefit_1_CBB, PP$Benefit_2_CBB, PP$Benefit_3_CBB)
cor(PP$Ben.CBB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.7551648 0.7489175
## [2,] 0.7551648 1.0000000 0.8000254
## [3,] 0.7489175 0.8000254 1.0000000Plant-based Protein Burger (PBPB)
#PBPB
PP$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$Benefit_1_PBPB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_PBPB, main = 'This is beneficial to society.')
PP$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$Benefit_3_PBPB, main = 'This is beneficial to the environment.')
#Benefit Scale
PP$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$Benefit_3_PBPB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.86 0.76 9.3 0.0053 61 26 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.91
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_PBPB 0.87 0.87 0.76 0.76 6.5 0.0085 NA
## PP.Benefit_2_PBPB 0.84 0.84 0.73 0.73 5.4 0.0098 NA
## PP.Benefit_3_PBPB 0.87 0.87 0.78 0.78 6.9 0.0080 NA
## med.r
## PP.Benefit_1_PBPB 0.76
## PP.Benefit_2_PBPB 0.73
## PP.Benefit_3_PBPB 0.78
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_PBPB 520 0.91 0.91 0.84 0.80 61 29
## PP.Benefit_2_PBPB 520 0.93 0.92 0.87 0.83 61 29
## PP.Benefit_3_PBPB 520 0.91 0.91 0.83 0.79 62 28hist(PP$Ben_Score_PBPB, main = 'PBPB Benefit Scale Score')
#Correlation
PP$Ben.PBPB <-cbind (PP$Benefit_1_PBPB, PP$Benefit_2_PBPB, PP$Benefit_3_PBPB)
cor(PP$Ben.PBPB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.7743993 0.7312628
## [2,] 0.7743993 1.0000000 0.7626700
## [3,] 0.7312628 0.7626700 1.0000000Plant-based Fermentation Burger (PBFB)
#PBFB
PP$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$Benefit_1_PBFB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_PBFB, main = 'This is beneficial to society.')
PP$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$Benefit_3_PBFB, main = 'This is beneficial to the environment.')
#Benefit Scale
PP$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$Benefit_3_PBFB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.87 0.78 10 0.0048 57 28 0.76
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Benefit_1_PBFB 0.86 0.86 0.76 0.76 6.3 0.0086 NA
## PP.Benefit_2_PBFB 0.86 0.86 0.76 0.76 6.3 0.0086 NA
## PP.Benefit_3_PBFB 0.89 0.89 0.80 0.80 8.2 0.0068 NA
## med.r
## PP.Benefit_1_PBFB 0.76
## PP.Benefit_2_PBFB 0.76
## PP.Benefit_3_PBFB 0.80
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_PBFB 479 0.93 0.93 0.88 0.83 55 32
## PP.Benefit_2_PBFB 479 0.93 0.93 0.88 0.83 57 31
## PP.Benefit_3_PBFB 479 0.91 0.91 0.84 0.80 59 30hist(PP$Ben_Score_PBFB, main = 'PBFB Benefit Scale Score')
#Correlation
PP$Ben.PBFB <-cbind (PP$Benefit_1_PBFB, PP$Benefit_2_PBFB, PP$Benefit_3_PBFB)
cor(PP$Ben.PBFB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.8048236 0.7603559
## [2,] 0.8048236 1.0000000 0.7602524
## [3,] 0.7603559 0.7602524 1.0000000Veggie Burger (VB)
#VB
PP$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$Benefit_1_VB, main = 'This is beneficial to my health.')
PP$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$Benefit_2_VB, main = 'This is beneficial to society.')
PP$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$Benefit_3_VB, main = 'This is beneficial to the environment.')
#VB Benefit Scale
PP$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$Benefit_3_VB))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.85 0.74 8.5 0.0058 68 25 0.74
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.91
## Duhachek 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Benefit_1_VB 0.85 0.85 0.75 0.75 5.9 0.0092 NA 0.75
## PP.Benefit_2_VB 0.84 0.84 0.73 0.73 5.4 0.0098 NA 0.73
## PP.Benefit_3_VB 0.85 0.85 0.74 0.74 5.7 0.0095 NA 0.74
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Benefit_1_VB 471 0.91 0.91 0.83 0.79 68 28
## PP.Benefit_2_VB 470 0.91 0.91 0.84 0.80 67 27
## PP.Benefit_3_VB 470 0.91 0.91 0.84 0.79 68 27hist(PP$Ben_Score_VB, main = 'VB Benefit Scale Score')
#Correlation
PP$Ben.VB <-cbind (PP$Benefit_1_VB, PP$Benefit_2_VB, PP$Benefit_3_VB)
cor(PP$Ben.VB, use = "pairwise.complete.obs")## [,1] [,2] [,3]
## [1,] 1.0000000 0.7395123 0.7308844
## [2,] 0.7395123 1.0000000 0.7456287
## [3,] 0.7308844 0.7456287 1.0000000Climate Change Belief
# Climate Change Belief: How much do you agree or disagree with the following statements?
## Item #1: Climate change is happening.
## Item #2: Climate change poses a risk to human health, safety, and prosperity.
## Item #3: Human activity is largely responsible for recent climate change.
## Item #4: Reducing greenhouse gas emissions will reduce global warming and climate change.
## Item Definitions
PP$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$CCB_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.88 0.71 9.8 0.0048 73 23 0.7
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.91 0.92
## Duhachek 0.9 0.91 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.CCB_48 0.87 0.87 0.82 0.69 6.5 0.0072 0.0011 0.70
## PP.CCB_49 0.87 0.87 0.82 0.69 6.7 0.0072 0.0035 0.66
## PP.CCB_50 0.88 0.88 0.84 0.71 7.5 0.0065 0.0037 0.70
## PP.CCB_51 0.90 0.90 0.86 0.75 9.0 0.0055 0.0013 0.76
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.CCB_48 1001 0.90 0.91 0.87 0.83 76 25
## PP.CCB_49 1001 0.90 0.90 0.86 0.82 72 27
## PP.CCB_50 1001 0.88 0.88 0.82 0.78 73 27
## PP.CCB_51 1001 0.85 0.85 0.77 0.73 69 26PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.07 0.16 0.24
## Duhachek 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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.81
## Duhachek 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$Control_GFFB, main = 'We have control over the processes in this method.')
#GFPRB
PP$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 = '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$Control_CBB, main = 'We have control over the processes in this method.')
#PBPB
PP$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 = '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$Control_PBFB, main = 'We have control over the processes in this method.')
#VB
PP$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 = '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$Disgust_GFFB, main = 'This is disgusting.')
#GFPRB
PP$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 = '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$Disgust_CBB, main = 'This is disgusting.')
#PBPB
PP$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 = '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$Disgust_PBFB, main = 'This is disgusting.')
#VB
PP$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 = '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$DS_Scale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.27 0.28 0.24 0.12 0.39 0.04 58 21 0.12
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.19 0.27 0.34
## Duhachek 0.19 0.27 0.35
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.DS_1D -0.10 -0.10 -0.05 -0.05 -0.094 0.070 NA -0.05
## PP.DS_2R 0.43 0.43 0.27 0.27 0.750 0.036 NA 0.27
## PP.DS_3D 0.22 0.22 0.12 0.12 0.281 0.049 NA 0.12
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.DS_1D 1000 0.70 0.73 0.52 0.29 66 30
## PP.DS_2R 1000 0.58 0.56 0.10 0.04 49 34
## PP.DS_3D 1001 0.65 0.64 0.32 0.14 58 34PP$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$Familiarity_GFFB, main = 'This is familiar.')
Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$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$Familiarity_GFPRB, main = 'This is familiar.')
Cultured beef burgers (CBB)
#CBB
PP$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$Familiarity_CBB, main = 'This is familiar.')
Plant-based Protein Burger (PBPB)
#PBPB
PP$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$Familiarity_PBPB, main = 'This is familiar.')
Plant-based Fermentation Burger (PBFB)
#PBFB
PP$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$Familiarity_PBFB, main = 'This is familiar.')
Veggie Burger (VB)
#VB
PP$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$Familiarity_VB, main = 'This is familiar.')
Ideology
#Political Orientation
##Which of the following best describes your political orientation? ( 1 = Strongly Conservative to 7 = Strongly Liberal)
PP$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$Party)## PP$Party
## n missing distinct Info Mean Gmd
## 1003 2 5 0.907 2.197 1.13
##
## lowest : 1 2 3 4 5, highest: 1 2 3 4 5
##
## Value 1 2 3 4 5
## Frequency 290 370 263 15 65
## Proportion 0.289 0.369 0.262 0.015 0.065PP$Party <- PP$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$Ind_6))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.75 0.5 3.9 0.01 74 18 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.8 0.82
## Duhachek 0.78 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_1 0.72 0.72 0.63 0.46 2.5 0.015 0.00254 0.43
## PP.Ind_2 0.74 0.74 0.67 0.49 2.9 0.014 0.00574 0.47
## PP.Ind_5 0.74 0.74 0.66 0.49 2.8 0.014 0.00397 0.47
## PP.Ind_6 0.79 0.79 0.71 0.55 3.7 0.012 0.00089 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_1 1000 0.82 0.82 0.75 0.67 75 23
## PP.Ind_2 1000 0.79 0.79 0.69 0.62 74 23
## PP.Ind_5 1000 0.80 0.80 0.70 0.63 74 23
## PP.Ind_6 1000 0.74 0.74 0.59 0.53 72 23hist(PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.74 0.76 0.79
## Duhachek 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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.8 0.82
## Duhachek 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$CollScale)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.77 0.73 0.45 3.3 0.012 67 20 0.42
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.74 0.76 0.79
## Duhachek 0.74 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## PP.Ind_3 0.69 0.69 0.61 0.43 2.2 0.017 0.0090 0.38
## PP.Ind_4 0.71 0.70 0.63 0.44 2.4 0.016 0.0138 0.38
## PP.Ind_7 0.70 0.72 0.64 0.46 2.5 0.016 0.0059 0.45
## PP.Ind_8 0.73 0.73 0.65 0.47 2.7 0.014 0.0102 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Ind_3 998 0.82 0.79 0.69 0.61 60 31
## PP.Ind_4 1000 0.75 0.77 0.66 0.57 72 24
## PP.Ind_7 1000 0.77 0.76 0.64 0.57 64 27
## PP.Ind_8 1001 0.72 0.75 0.62 0.52 70 24Meat Consumption
# Beef Consumption Frequency measured with the question, "In the average week, how often do you eat beef?" (1 = Never, 2 = Less than once a week, 3 = 1-2 times a week, 4 = 3-4 times a week, 5 = 5+ times a week)
describe(PP$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, main = 'This is natural.')
hist(PP$Nat_2R_GFFB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_GFFB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_GFFB, main = 'This is artificial.')
# Scales and Scores
PP$Naturalness.GFFB <- rowMeans(PP [, c( "Nat_1_GFFB" , "Nat_2R_GFFB", "Nat_3R_GFFB", "Nat_4R_GFFB")], na.rm=TRUE)
describe(PP$Naturalness.GFFB)## PP$Naturalness.GFFB
## 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$Naturalness.GFFB, na.rm = TRUE)## [1] 21.26861PP$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
## --------------------------------------------------------------------------------#Correlation
PP$Nat.GFFB <-cbind (PP$Nat_1_GFFB , PP$Nat_2R_GFFB , PP$Nat_3R_GFFB, PP$Nat_4R_GFFB)
cor(PP$Nat.GFFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.1821792 -0.1493404 0.1770718
## [2,] 0.1821792 1.0000000 0.4422381 0.6077149
## [3,] -0.1493404 0.4422381 1.0000000 0.4976025
## [4,] 0.1770718 0.6077149 0.4976025 1.0000000Grain-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$Nat_1_GFPRB, main = 'This is natural.')
hist(PP$Nat_2R_GFPRB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_GFPRB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_GFPRB, main = 'This is artificial.')
#### Score and Scale
PP$Naturalness.GFPRB <- rowMeans(PP [, c( "Nat_1_GFPRB" , "Nat_4R_GFPRB", "Nat_2R_GFPRB" , "Nat_3R_GFPRB")], na.rm=TRUE)
describe(PP$Naturalness.GFPRB)## PP$Naturalness.GFPRB
## n missing distinct Info Mean Gmd .05 .10
## 514 491 240 0.999 62.49 27.37 25.00 32.08
## .25 .50 .75 .90 .95
## 44.81 59.12 81.44 98.75 100.00
##
## lowest : 0.00 1.75 7.00 10.75 11.75, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Naturalness.GFPRB, 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$Nat_3R_GFPRB)
#Correlation
PP$Nat.GFPRB <-cbind (PP$Nat_1_GFPRB , PP$Nat_2R_GFPRB , PP$Nat_3R_GFPRB,PP$Nat_4R_GFPRB)
cor(PP$Nat.GFPRB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.2527056 0.1424887 0.3835835
## [2,] 0.2527056 1.0000000 0.5088795 0.6761920
## [3,] 0.1424887 0.5088795 1.0000000 0.5228007
## [4,] 0.3835835 0.6761920 0.5228007 1.0000000Cultured beef burgers (CBB)
#Defines naturalness variables and reverse codes items 2, 3, and 4.
PP$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, main = 'This is natural.')
hist(PP$Nat_2R_CBB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_CBB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_CBB, main = 'This is artificial.')
#### Score and Scale
PP$Naturalness.CBB <- rowMeans(PP [, c( "Nat_1_CBB" , "Nat_4R_CBB", "Nat_2R_CBB" , "Nat_3R_CBB")], na.rm=TRUE)
describe(PP$Naturalness.CBB)## PP$Naturalness.CBB
## 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$Naturalness.CBB, na.rm = TRUE)## [1] 21.71332PP$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
## --------------------------------------------------------------------------------#Correlation
PP$Nat.CBB <-cbind (PP$Nat_1_CBB, PP$Nat_2R_CBB, PP$Nat_3R_CBB,PP$Nat_4R_CBB)
cor(PP$Nat.CBB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.2131318 0.1020426 0.2589238
## [2,] 0.2131318 1.0000000 0.6201159 0.6227848
## [3,] 0.1020426 0.6201159 1.0000000 0.4981525
## [4,] 0.2589238 0.6227848 0.4981525 1.0000000Plant-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$Nat_1_PBPB, main = 'This is natural.')
hist(PP$Nat_2R_PBPB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_PBPB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_PBPB, main = 'This is artificial.')
#### Score and Scale
PP$Naturalness.PBPB <- rowMeans(PP [, c( "Nat_1_PBPB" , "Nat_4R_PBPB", "Nat_2R_PBPB" , "Nat_3R_PBPB")], na.rm=TRUE)
describe(PP$Naturalness.PBPB)## PP$Naturalness.PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 236 1 42.37 22.52 2.288 12.900
## .25 .50 .75 .90 .95
## 29.688 44.000 53.750 67.100 74.962
##
## lowest : 0.00 0.50 0.75 1.00 1.25, highest: 92.25 96.75 97.00 98.50 100.00sd(PP$Naturalness.PBPB, 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$Naturalness_Scale_PBPB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 99 0.998 53.99 36.26 0 3
## .25 .50 .75 .90 .95
## 29 58 79 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 524 481 97 0.998 43.33 34.89 0 0
## .25 .50 .75 .90 .95
## 20 39 68 87 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 96 0.998 39.82 33.02 0.0 0.0
## .25 .50 .75 .90 .95
## 18.0 35.0 61.0 83.9 97.0
##
## lowest : 0 1 2 3 4, highest: 95 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_PBPB
## n missing distinct Info Mean Gmd .05 .10
## 522 483 88 0.996 32.13 28.98 0.0 0.0
## .25 .50 .75 .90 .95
## 11.0 29.0 48.0 70.9 82.0
##
## lowest : 0 1 2 3 4, highest: 94 97 98 99 100
## --------------------------------------------------------------------------------#Correlation
PP$Nat.PBPB <-cbind (PP$Nat_1_PBPB, PP$Nat_2R_PBPB , PP$Nat_3R_PBPB, PP$Nat_4R_PBPB)
cor(PP$Nat.PBPB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.00000000 0.2050461 -0.02094861 0.2790013
## [2,] 0.20504614 1.0000000 0.44902730 0.4896993
## [3,] -0.02094861 0.4490273 1.00000000 0.3952031
## [4,] 0.27900132 0.4896993 0.39520306 1.0000000Plant-based Fermentation Burger (PBFB)
#Reverse Code Items #2-4
PP$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, main = 'This is natural.')
hist(PP$Nat_2R_PBFB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_PBFB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_PBFB, main = 'This is artificial.')
#### Scale and Score
PP$Naturalness.PBFB <- rowMeans(PP [, c( "Nat_1_PBFB" , "Nat_4R_PBFB", "Nat_2R_PBFB" , "Nat_3R_PBFB")], na.rm=TRUE)
describe(PP$Naturalness.PBFB)## PP$Naturalness.PBFB
## 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$Naturalness.PBFB, na.rm = TRUE)## [1] 18.3293PP$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
## --------------------------------------------------------------------------------#Correlation
PP$Nat.PBFB <-cbind (PP$Nat_1_PBFB, PP$Nat_2R_PBFB , PP$Nat_3R_PBFB, PP$Nat_4R_PBFB)
cor(PP$Nat.PBFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.000000000 -0.09751735 -0.005024283 -0.2611645
## [2,] -0.097517348 1.00000000 0.534934052 0.4945029
## [3,] -0.005024283 0.53493405 1.000000000 0.4480419
## [4,] -0.261164491 0.49450290 0.448041865 1.0000000Veggie 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$Nat_1_VB, main = 'This is natural.')
hist(PP$Nat_2R_VB, main = 'This involves humans altering naturally occurring processes.')
hist(PP$Nat_3R_VB, main = 'This relies on science-based technology.')
hist(PP$Nat_4R_VB, main = 'This is artificial.')
#### Scale and Score
PP$Naturalness.VB <- rowMeans(PP [, c( "Nat_1_VB" , "Nat_4R_VB", "Nat_2R_VB" , "Nat_3R_VB")], na.rm=TRUE)
describe(PP$Naturalness.VB)## PP$Naturalness.VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 237 1 51.39 25.22 16.50 25.00
## .25 .50 .75 .90 .95
## 36.19 49.00 65.50 84.00 96.36
##
## lowest : 0.00 1.25 3.00 3.25 4.25, highest: 99.00 99.25 99.50 99.75 100.00sd(PP$Naturalness.VB, 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$Naturalness_Scale_VB_Tot
##
## 4 Variables 1005 Observations
## --------------------------------------------------------------------------------
## PP.Nat_1_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 93 0.996 65.13 32.6 4.55 21.00
## .25 .50 .75 .90 .95
## 50.00 71.00 89.00 100.00 100.00
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_4R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 96 0.998 49.87 37.8 0 4
## .25 .50 .75 .90 .95
## 21 48 80 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_2R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 99 0.998 49.76 36.59 0 6
## .25 .50 .75 .90 .95
## 24 48 78 99 100
##
## lowest : 0 1 2 3 4, highest: 96 97 98 99 100
## --------------------------------------------------------------------------------
## PP.Nat_3R_VB
## n missing distinct Info Mean Gmd .05 .10
## 472 533 91 0.999 40.8 33.64 0.00 2.00
## .25 .50 .75 .90 .95
## 19.00 34.00 61.25 91.80 100.00
##
## lowest : 0 1 2 3 4, highest: 95 96 98 99 100
## --------------------------------------------------------------------------------#Correlation
PP$Nat.VB <-cbind (PP$Nat_1_VB, PP$Nat_2R_VB , PP$Nat_3R_VB, PP$Nat_4R_VB)
cor(PP$Nat.VB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.2334202 0.1060943 0.3288012
## [2,] 0.2334202 1.0000000 0.4771669 0.6057221
## [3,] 0.1060943 0.4771669 1.0000000 0.4076616
## [4,] 0.3288012 0.6057221 0.4076616 1.0000000Risk Perceptions
# Risk perception was measured with 2 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Risk score calculated by averaging these items.
### Item 1: This is risky to eat.
### Item 2: Producing this is risky for society.
### Item 3: Producing this is risky for the environment.
### Item 4: This is frightening.Grain-fed Feedlot Burger (GFFB)
#GFFB
PP$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$Risk_1_GFFB, main = 'This is risky to eat.')
PP$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$Risk_2_GFFB, main = 'Producing this is risky for society.')
PP$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$Risk_3_GFFB, main = 'Producing this is risky for the environment.')
PP$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$Risk_4_GFFB, main = 'This is frightening.')
#GFFB Risk Scale
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.9
## Duhachek 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$Risk_Score_GFFB, main = 'GFFB Risk Scale Score')
#Correlation
PP$Risk.GFFB <-cbind (PP$Risk_1_GFFB, PP$Risk_2_GFFB, PP$Risk_3_GFFB, PP$Risk_4_GFFB)
cor(PP$Risk.GFFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.7283147 0.6825550 0.5893209
## [2,] 0.7283147 1.0000000 0.7471631 0.6384920
## [3,] 0.6825550 0.7471631 1.0000000 0.6153396
## [4,] 0.5893209 0.6384920 0.6153396 1.0000000Grain-fed Pasture Raised Burger (GFPRB)
#GFPRB
PP$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$Risk_2_GFPRB, main = 'Producing this is risky for society.')
PP$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$Risk_3_GFPRB, main = 'Producing this is risky for the environment.')
PP$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$Risk_4_GFPRB, main = 'This is frightening.')
#GFPRB Risk Scale
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.9
## Duhachek 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$Risk_Score_GFPRB, main = 'GFPRB Risk Scale Score')
#Correlation
PP$Risk.GFPRB <-cbind (PP$Risk_1_GFPRB, PP$Risk_2_GFPRB, PP$Risk_3_GFPRB, PP$Risk_4_GFPRB)
cor(PP$Risk.GFPRB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.6766418 0.6600000 0.6792032
## [2,] 0.6766418 1.0000000 0.7396801 0.6318567
## [3,] 0.6600000 0.7396801 1.0000000 0.6093402
## [4,] 0.6792032 0.6318567 0.6093402 1.0000000Cultured Beef Burgers (CBB)
#CBB
PP$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$Risk_1_CBB, main = 'This is risky to eat.')
PP$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$Risk_2_CBB, main = 'Producing this is risky for society.')
PP$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$Risk_3_CBB, main = 'Producing this is risky for the environment.')
PP$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$Risk_4_CBB, main = 'This is frightening.')
#CBB Risk Scale
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.90
## Duhachek 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$Risk_Score_CBB, main = 'CBB Risk Scale Score')
#Correlation
PP$Risk.CBB <-cbind (PP$Risk_1_CBB, PP$Risk_2_CBB, PP$Risk_3_CBB, PP$Risk_4_CBB)
cor(PP$Risk.CBB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.7090082 0.6619614 0.7300456
## [2,] 0.7090082 1.0000000 0.7041794 0.6731257
## [3,] 0.6619614 0.7041794 1.0000000 0.5951831
## [4,] 0.7300456 0.6731257 0.5951831 1.0000000Plant-based Protein Burger (PBPB)
#PBPB
PP$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$Risk_1_PBPB, main = 'This is risky to eat.')
PP$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$Risk_2_PBPB, main = 'Producing this is risky for society.')
PP$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$Risk_3_PBPB, main = 'Producing this is risky for the environment.')
PP$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$Risk_4_PBPB, main = 'This is frightening.')
#PBPB Risk Scale
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.91
## Duhachek 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$Risk_Score_PBPB, main = 'PBPB Risk Scale Score')
#Correlation
PP$Risk.PBPB <-cbind (PP$Risk_1_PBPB, PP$Risk_2_PBPB, PP$Risk_3_PBPB, PP$Risk_4_PBPB)
cor(PP$Risk.PBPB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.7104953 0.6402508 0.7367189
## [2,] 0.7104953 1.0000000 0.7216761 0.6915020
## [3,] 0.6402508 0.7216761 1.0000000 0.6392415
## [4,] 0.7367189 0.6915020 0.6392415 1.0000000Plant-based Fermentation Burger (PBFB)
#PBFB
PP$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$Risk_1_PBFB, main = 'This is risky to eat.')
PP$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$Risk_2_PBFB, main = 'Producing this is risky for society.')
PP$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$Risk_3_PBFB, main = 'Producing this is risky for the environment.')
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.84 0.86
## Duhachek 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$Risk_Score_PBFB, main = 'PBFB Risk Scale Score')
#Correlation
PP$Risk.PBFB <-cbind (PP$Risk_1_PBFB, PP$Risk_2_PBFB, PP$Risk_3_PBFB, PP$Risk_4_PBFB)
cor(PP$Risk.PBFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.5195135 0.4496435 0.5055023
## [2,] 0.5195135 1.0000000 0.7216761 0.6915020
## [3,] 0.4496435 0.7216761 1.0000000 0.6392415
## [4,] 0.5055023 0.6915020 0.6392415 1.0000000Veggie Burger (VB)
#VB
PP$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$Risk_1_VB, main = 'This is risky to eat.')
PP$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$Risk_2_VB, main = 'Producing this is risky for society.')
PP$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$Risk_3_VB, main = 'Producing this is risky for the environment.')
PP$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$Risk_4_VB, main = 'This is frightening.')
#VB Risk Scale
PP$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
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.90
## Duhachek 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$Risk_Score_VB, main = 'VB Risk Scale Score')
#Correlation
PP$Risk.VB <-cbind (PP$Risk_1_VB, PP$Risk_2_VB, PP$Risk_3_VB, PP$Risk_4_VB)
cor(PP$Risk.VB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.6772380 0.6877016 0.7074650
## [2,] 0.6772380 1.0000000 0.7285788 0.6531574
## [3,] 0.6877016 0.7285788 1.0000000 0.6224504
## [4,] 0.7074650 0.6531574 0.6224504 1.0000000Support
# Willingness to support was measured with 4 items on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree'). Support score calculated by averaging these items.
### Item 1: I support this.
### Item 2: I would purchase this product.
### Item 3: Society should support this.
### Item 4: Society should purchase this product.Grain-fed Feedlot Burger (GFFB)
# Behavioral Intent (SUPPORT) Scales and Scores
### Rename Variables
PP$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, main = 'I support this.')
hist(PP$BehavInt2_GFFB, main = 'I would purchase this product.')
hist(PP$BehavInt3_GFFB, main = 'Society should support this.')
hist(PP$BehavInt4_GFFB, main = 'Society should purchase this product.')
#Support Score
PP$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] 29.21738#Correlation
PP$Support.GFFB <-cbind (PP$BehavInt1_GFFB, PP$BehavInt2_GFFB, PP$BehavInt3_GFFB, PP$BehavInt4_GFFB)
cor(PP$Support.GFFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8314723 0.8708751 0.8670036
## [2,] 0.8314723 1.0000000 0.7982267 0.7824067
## [3,] 0.8708751 0.7982267 1.0000000 0.8803280
## [4,] 0.8670036 0.7824067 0.8803280 1.0000000Grain-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$BehavInt1_GFPRB, main = 'I support this.')
hist(PP$BehavInt2_GFPRB, main = 'I would purchase this product.')
hist(PP$BehavInt3_GFPRB, main = 'Society should support this.')
hist(PP$BehavInt4_GFPRB, main = 'Society should purchase this product.')
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$Behav_Score_GFPRB, na.rm= TRUE)## [1] 29.36235#Correlation
PP$Support.GFPRB <-cbind (PP$BehavInt1_GFPRB, PP$BehavInt2_GFPRB, PP$BehavInt3_GFPRB, PP$BehavInt4_GFPRB)
cor(PP$Support.GFPRB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8284063 0.8612284 0.8438345
## [2,] 0.8284063 1.0000000 0.7901925 0.8035970
## [3,] 0.8612284 0.7901925 1.0000000 0.8805271
## [4,] 0.8438345 0.8035970 0.8805271 1.0000000Cultured Beef Burgers (CBB)
##CBB
PP$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, main = 'I support this.')
hist(PP$BehavInt2_CBB, main = 'I would purchase this product.')
hist(PP$BehavInt3_CBB, main = 'Society should support this.')
hist(PP$BehavInt4_CBB, main = 'Society should purchase this product.')
# Scales
PP$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] 31.7638#Correlation
PP$Support.CBB <-cbind (PP$BehavInt1_CBB, PP$BehavInt2_CBB, PP$BehavInt3_CBB, PP$BehavInt4_CBB)
cor(PP$Support.CBB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8872947 0.8861464 0.8609682
## [2,] 0.8872947 1.0000000 0.8424466 0.8318791
## [3,] 0.8861464 0.8424466 1.0000000 0.8710896
## [4,] 0.8609682 0.8318791 0.8710896 1.0000000Plant-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$BehavInt1_PBPB, main = 'I support this.')
hist(PP$BehavInt2_PBPB, main = 'I would purchase this product.')
hist(PP$BehavInt3_PBPB, main = 'Society should support this.')
hist(PP$BehavInt4_PBPB, main = 'Society should purchase this product.')
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$Behav_Score_PBPB, na.rm= TRUE)## [1] 29.36235#Correlation
PP$Support.PBPB <-cbind (PP$BehavInt1_PBPB, PP$BehavInt2_PBPB, PP$BehavInt3_PBPB, PP$BehavInt4_PBPB)
cor(PP$Support.PBPB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8284063 0.8612284 0.8438345
## [2,] 0.8284063 1.0000000 0.7901925 0.8035970
## [3,] 0.8612284 0.7901925 1.0000000 0.8805271
## [4,] 0.8438345 0.8035970 0.8805271 1.0000000Plant-based Fermentation Burger (PBFB)
##PBFB
PP$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, main = 'I support this.')
hist(PP$BehavInt2_PBFB, main = 'I would purchase this product.')
hist(PP$BehavInt3_PBFB, main = 'Society should support this.')
hist(PP$BehavInt4_PBFB, main = 'Society should purchase this product.')
PP$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] 31.0349#Correlation
PP$Support.PBFB <-cbind (PP$BehavInt1_PBFB, PP$BehavInt2_PBFB, PP$BehavInt3_PBFB, PP$BehavInt4_PBFB)
cor(PP$Support.PBFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8170622 0.8522341 0.8256578
## [2,] 0.8170622 1.0000000 0.7768021 0.8080175
## [3,] 0.8522341 0.7768021 1.0000000 0.8944153
## [4,] 0.8256578 0.8080175 0.8944153 1.0000000Veggie 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$BehavInt1_VB, main = 'I support this.')
hist(PP$BehavInt2_VB, main = 'I would purchase this product.')
hist(PP$BehavInt3_VB, main = 'Society should support this.')
hist(PP$BehavInt4_VB, main = 'Society should purchase this product.')
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$Behav_Score_VB, na.rm= TRUE)## [1] 27.38456#Correlation
PP$Support.PBFB <-cbind (PP$BehavInt1_PBFB, PP$BehavInt2_PBFB, PP$BehavInt3_PBFB, PP$BehavInt4_PBFB)
cor(PP$Support.PBFB, use = "pairwise.complete.obs")## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.8170622 0.8522341 0.8256578
## [2,] 0.8170622 1.0000000 0.7768021 0.8080175
## [3,] 0.8522341 0.7768021 1.0000000 0.8944153
## [4,] 0.8256578 0.8080175 0.8944153 1.0000000Understanding
# Understanding was measured with 1 item on a 0-100 scale ( 0 = 'Strongly disagree' to 100 = 'Strongly agree').
### Item 1: I understand how this works.Grain-fed Feedlot Burger (GFFB)
PP$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$Understanding_GFFB, main = 'I understand how this works.')
Grain-fed Pasture Raised Burger (GFPRB)
PP$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$Understanding_GFPRB, main = 'I understand how this works.')
Cultured Beef Burgers (CBB)
PP$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$Understanding_CBB, main = 'I understand how this works.')
Plant-based Protein Burger (PBPB)
PP$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$Understanding_PBPB, main = 'I understand how this works.')
Plant-based Fermentation Burger (PBFB)
PP$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$Understanding_PBFB, main = 'I understand how this works.')
#VB
PP$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$Understanding_VB, main = 'I understand how this works.')
Combined Fam/Und Mean Scores
#Familiarity and Understanding were highly correlated. Thus, we decided to combine them as a mean score.
#Create mean scores
PP$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$FR_Scale_GFFB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.66 0.5 0.5 2 0.021 66 25 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.61 0.66 0.7
## Duhachek 0.62 0.66 0.7
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_GFFB 0.56 0.5 0.25 0.5 0.98 NA 0
## PP.Understanding_GFFB 0.44 0.5 0.25 0.5 0.98 NA 0
## med.r
## PP.Familiarity_GFFB 0.5
## PP.Understanding_GFFB 0.5
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_GFFB 493 0.88 0.86 0.61 0.5 63 30
## PP.Understanding_GFFB 497 0.85 0.86 0.61 0.5 70 27psych::alpha(PP$FR_Scale_GFPRB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_GFPRB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.62 0.62 3.2 0.015 73 24 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.76 0.79
## Duhachek 0.73 0.76 0.79
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_GFPRB 0.59 0.62 0.38 0.62 1.6 NA 0
## PP.Understanding_GFPRB 0.64 0.62 0.38 0.62 1.6 NA 0
## med.r
## PP.Familiarity_GFPRB 0.62
## PP.Understanding_GFPRB 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_GFPRB 511 0.9 0.9 0.71 0.62 73 26
## PP.Understanding_GFPRB 512 0.9 0.9 0.71 0.62 73 27psych::alpha(PP$FR_Scale_CBB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_CBB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.69 0.69 0.53 0.53 2.2 0.019 52 28 0.53
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.65 0.69 0.73
## Duhachek 0.65 0.69 0.73
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_CBB 0.57 0.53 0.28 0.53 1.1 NA 0
## PP.Understanding_CBB 0.49 0.53 0.28 0.53 1.1 NA 0
## med.r
## PP.Familiarity_CBB 0.53
## PP.Understanding_CBB 0.53
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_CBB 515 0.88 0.87 0.64 0.53 46 34
## PP.Understanding_CBB 515 0.86 0.87 0.64 0.53 58 31psych::alpha(PP$FR_Scale_PBPB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_PBPB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.57 0.57 0.4 0.4 1.3 0.027 59 24 0.4
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.51 0.57 0.62
## Duhachek 0.52 0.57 0.62
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_PBPB 0.43 0.4 0.16 0.4 0.66 NA 0
## PP.Understanding_PBPB 0.37 0.4 0.16 0.4 0.66 NA 0
## med.r
## PP.Familiarity_PBPB 0.4
## PP.Understanding_PBPB 0.4
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_PBPB 524 0.85 0.84 0.53 0.4 54 30
## PP.Understanding_PBPB 524 0.82 0.84 0.53 0.4 63 28psych::alpha(PP$FR_Scale_PBFB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_PBFB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.7 0.7 0.54 0.54 2.3 0.019 53 28 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.66 0.7 0.74
## Duhachek 0.66 0.7 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_PBFB 0.58 0.54 0.29 0.54 1.2 NA 0
## PP.Understanding_PBFB 0.51 0.54 0.29 0.54 1.2 NA 0
## med.r
## PP.Familiarity_PBFB 0.54
## PP.Understanding_PBFB 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_PBFB 481 0.89 0.88 0.64 0.54 48 33
## PP.Understanding_PBFB 480 0.87 0.88 0.64 0.54 58 31psych::alpha(PP$FR_Scale_VB)## Number of categories should be increased in order to count frequencies.##
## Reliability analysis
## Call: psych::alpha(x = PP$FR_Scale_VB)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.6 0.6 0.43 0.43 1.5 0.025 65 24 0.43
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.55 0.6 0.65
## Duhachek 0.55 0.6 0.65
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## PP.Familiarity_VB 0.46 0.43 0.18 0.43 0.75 NA 0
## PP.Understanding_VB 0.40 0.43 0.18 0.43 0.75 NA 0
## med.r
## PP.Familiarity_VB 0.43
## PP.Understanding_VB 0.43
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## PP.Familiarity_VB 472 0.86 0.85 0.55 0.43 62 29
## PP.Understanding_VB 471 0.83 0.85 0.55 0.43 69 27Relationships by Technology
# Correlations between key constructs across all six technology types (grain-fed beef, grass-fed beef, cultured beef, plant-based, plant-based fermentation, veggie)
#Naturalness Perceptions of Technologies
PP$corNScales <- data.frame(PP$Naturalness.GFFB, PP$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.Naturalness.GFFB PP.Nat_1_GFPRB PP.Nat_4R_GFPRB
## PP.Naturalness.GFFB 1.00 0.25 0.40
## PP.Nat_1_GFPRB 0.25 1.00 0.38
## PP.Nat_4R_GFPRB 0.40 0.38 1.00
## PP.Nat_2R_GFPRB 0.41 0.25 0.68
## PP.Nat_3R_GFPRB 0.37 0.14 0.52
## PP.Nat_1_CBB -0.26 -0.10 -0.34
## PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB
## PP.Naturalness.GFFB 0.41 0.37 -0.26 0.15
## PP.Nat_1_GFPRB 0.25 0.14 -0.10 -0.05
## PP.Nat_4R_GFPRB 0.68 0.52 -0.34 0.11
## PP.Nat_2R_GFPRB 1.00 0.51 -0.34 0.00
## PP.Nat_3R_GFPRB 0.51 1.00 -0.39 -0.03
## PP.Nat_1_CBB -0.34 -0.39 1.00 0.26
## PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB
## PP.Naturalness.GFFB 0.17 0.10 -0.26 0.04
## PP.Nat_1_GFPRB -0.13 -0.13 -0.04 0.03
## PP.Nat_4R_GFPRB -0.06 -0.06 -0.23 0.20
## PP.Nat_2R_GFPRB -0.07 -0.07 -0.27 0.04
## PP.Nat_3R_GFPRB -0.06 0.03 -0.28 -0.04
## PP.Nat_1_CBB 0.21 0.10 0.39 -0.07
## PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB
## PP.Naturalness.GFFB -0.02 -0.01 -0.31 0.04
## PP.Nat_1_GFPRB 0.05 -0.33 -0.06 0.23
## PP.Nat_4R_GFPRB 0.11 -0.12 -0.35 -0.01
## PP.Nat_2R_GFPRB 0.02 -0.21 -0.38 0.05
## PP.Nat_3R_GFPRB -0.03 0.02 -0.31 0.02
## PP.Nat_1_CBB -0.14 -0.10 0.50 -0.14
## PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB
## PP.Naturalness.GFFB 0.03 0.05 -0.07 0.18
## PP.Nat_1_GFPRB 0.20 0.28 0.09 0.02
## PP.Nat_4R_GFPRB -0.04 0.05 -0.11 0.32
## PP.Nat_2R_GFPRB 0.07 0.09 -0.16 0.24
## PP.Nat_3R_GFPRB -0.01 -0.03 -0.18 0.23
## PP.Nat_1_CBB 0.02 -0.02 0.14 -0.28
## PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Naturalness.GFFB 0.10 0.06
## PP.Nat_1_GFPRB 0.07 0.06
## PP.Nat_4R_GFPRB 0.39 0.25
## PP.Nat_2R_GFPRB 0.35 0.28
## PP.Nat_3R_GFPRB 0.28 0.24
## PP.Nat_1_CBB -0.38 -0.25library("Hmisc")
mydata.rcorr5 = rcorr(as.matrix(mydata.cor5))
mydata.rcorr5## PP.Naturalness.GFFB PP.Nat_1_GFPRB PP.Nat_4R_GFPRB
## PP.Naturalness.GFFB 1.00 0.40 0.73
## PP.Nat_1_GFPRB 0.40 1.00 0.53
## PP.Nat_4R_GFPRB 0.73 0.53 1.00
## PP.Nat_2R_GFPRB 0.74 0.50 0.94
## PP.Nat_3R_GFPRB 0.72 0.34 0.85
## PP.Nat_1_CBB -0.64 -0.36 -0.78
## PP.Nat_4R_CBB 0.04 -0.40 -0.11
## PP.Nat_2R_CBB 0.03 -0.49 -0.24
## PP.Nat_3R_CBB 0.02 -0.48 -0.22
## PP.Nat_1_PBPB -0.71 -0.32 -0.66
## PP.Nat_4R_PBPB -0.09 -0.28 0.11
## PP.Nat_2R_PBPB -0.10 -0.33 0.03
## PP.Nat_3R_PBPB -0.10 -0.61 -0.10
## PP.Nat_1_PBFB -0.75 -0.38 -0.76
## PP.Nat_4R_PBFB 0.15 0.50 0.08
## PP.Nat_2R_PBFB 0.01 0.48 -0.05
## PP.Nat_3R_PBFB 0.04 0.55 0.01
## PP.Nat_1_VB -0.47 -0.06 -0.36
## PP.Nat_4R_VB 0.20 -0.07 0.47
## PP.Nat_2R_VB 0.28 0.04 0.61
## PP.Nat_3R_VB 0.24 -0.03 0.50
## PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB
## PP.Naturalness.GFFB 0.74 0.72 -0.64 0.04
## PP.Nat_1_GFPRB 0.50 0.34 -0.36 -0.40
## PP.Nat_4R_GFPRB 0.94 0.85 -0.78 -0.11
## PP.Nat_2R_GFPRB 1.00 0.86 -0.77 -0.21
## PP.Nat_3R_GFPRB 0.86 1.00 -0.80 -0.18
## PP.Nat_1_CBB -0.77 -0.80 1.00 0.34
## PP.Nat_4R_CBB -0.21 -0.18 0.34 1.00
## PP.Nat_2R_CBB -0.29 -0.21 0.33 0.89
## PP.Nat_3R_CBB -0.27 -0.14 0.26 0.82
## PP.Nat_1_PBPB -0.71 -0.73 0.69 0.09
## PP.Nat_4R_PBPB -0.07 -0.08 -0.15 0.29
## PP.Nat_2R_PBPB -0.11 -0.06 -0.16 0.38
## PP.Nat_3R_PBPB -0.21 -0.01 -0.14 0.29
## PP.Nat_1_PBFB -0.80 -0.78 0.81 0.22
## PP.Nat_4R_PBFB 0.20 0.12 -0.21 -0.58
## PP.Nat_2R_PBFB 0.09 -0.03 0.02 -0.64
## PP.Nat_3R_PBFB 0.11 -0.05 0.02 -0.49
## PP.Nat_1_VB -0.41 -0.46 0.31 -0.24
## PP.Nat_4R_VB 0.35 0.37 -0.52 0.01
## PP.Nat_2R_VB 0.53 0.53 -0.69 -0.07
## PP.Nat_3R_VB 0.46 0.51 -0.61 -0.10
## PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB
## PP.Naturalness.GFFB 0.03 0.02 -0.71 -0.09
## PP.Nat_1_GFPRB -0.49 -0.48 -0.32 -0.28
## PP.Nat_4R_GFPRB -0.24 -0.22 -0.66 0.11
## PP.Nat_2R_GFPRB -0.29 -0.27 -0.71 -0.07
## PP.Nat_3R_GFPRB -0.21 -0.14 -0.73 -0.08
## PP.Nat_1_CBB 0.33 0.26 0.69 -0.15
## PP.Nat_4R_CBB 0.89 0.82 0.09 0.29
## PP.Nat_2R_CBB 1.00 0.92 0.04 0.20
## PP.Nat_3R_CBB 0.92 1.00 -0.02 0.13
## PP.Nat_1_PBPB 0.04 -0.02 1.00 0.29
## PP.Nat_4R_PBPB 0.20 0.13 0.29 1.00
## PP.Nat_2R_PBPB 0.46 0.42 0.17 0.78
## PP.Nat_3R_PBPB 0.41 0.40 0.00 0.66
## PP.Nat_1_PBFB 0.18 0.16 0.90 0.09
## PP.Nat_4R_PBFB -0.50 -0.45 -0.43 -0.67
## PP.Nat_2R_PBFB -0.66 -0.63 -0.09 -0.62
## PP.Nat_3R_PBFB -0.57 -0.54 -0.06 -0.58
## PP.Nat_1_VB -0.31 -0.35 0.74 0.22
## PP.Nat_4R_VB -0.12 -0.14 0.00 0.69
## PP.Nat_2R_VB -0.14 -0.15 -0.28 0.45
## PP.Nat_3R_VB -0.13 -0.15 -0.39 0.41
## PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB
## PP.Naturalness.GFFB -0.10 -0.10 -0.75 0.15
## PP.Nat_1_GFPRB -0.33 -0.61 -0.38 0.50
## PP.Nat_4R_GFPRB 0.03 -0.10 -0.76 0.08
## PP.Nat_2R_GFPRB -0.11 -0.21 -0.80 0.20
## PP.Nat_3R_GFPRB -0.06 -0.01 -0.78 0.12
## PP.Nat_1_CBB -0.16 -0.14 0.81 -0.21
## PP.Nat_4R_CBB 0.38 0.29 0.22 -0.58
## PP.Nat_2R_CBB 0.46 0.41 0.18 -0.50
## PP.Nat_3R_CBB 0.42 0.40 0.16 -0.45
## PP.Nat_1_PBPB 0.17 0.00 0.90 -0.43
## PP.Nat_4R_PBPB 0.78 0.66 0.09 -0.67
## PP.Nat_2R_PBPB 1.00 0.77 0.07 -0.62
## PP.Nat_3R_PBPB 0.77 1.00 -0.02 -0.57
## PP.Nat_1_PBFB 0.07 -0.02 1.00 -0.42
## PP.Nat_4R_PBFB -0.62 -0.57 -0.42 1.00
## PP.Nat_2R_PBFB -0.80 -0.75 -0.14 0.81
## PP.Nat_3R_PBFB -0.74 -0.86 -0.10 0.77
## PP.Nat_1_VB 0.02 -0.12 0.66 -0.33
## PP.Nat_4R_VB 0.53 0.44 -0.16 -0.56
## PP.Nat_2R_VB 0.46 0.38 -0.35 -0.36
## PP.Nat_3R_VB 0.39 0.55 -0.41 -0.33
## PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB
## PP.Naturalness.GFFB 0.01 0.04 -0.47 0.20
## PP.Nat_1_GFPRB 0.48 0.55 -0.06 -0.07
## PP.Nat_4R_GFPRB -0.05 0.01 -0.36 0.47
## PP.Nat_2R_GFPRB 0.09 0.11 -0.41 0.35
## PP.Nat_3R_GFPRB -0.03 -0.05 -0.46 0.37
## PP.Nat_1_CBB 0.02 0.02 0.31 -0.52
## PP.Nat_4R_CBB -0.64 -0.49 -0.24 0.01
## PP.Nat_2R_CBB -0.66 -0.57 -0.31 -0.12
## PP.Nat_3R_CBB -0.63 -0.54 -0.35 -0.14
## PP.Nat_1_PBPB -0.09 -0.06 0.74 0.00
## PP.Nat_4R_PBPB -0.62 -0.58 0.22 0.69
## PP.Nat_2R_PBPB -0.80 -0.74 0.02 0.53
## PP.Nat_3R_PBPB -0.75 -0.86 -0.12 0.44
## PP.Nat_1_PBFB -0.14 -0.10 0.66 -0.16
## PP.Nat_4R_PBFB 0.81 0.77 -0.33 -0.56
## PP.Nat_2R_PBFB 1.00 0.88 0.01 -0.56
## PP.Nat_3R_PBFB 0.88 1.00 0.01 -0.50
## PP.Nat_1_VB 0.01 0.01 1.00 0.31
## PP.Nat_4R_VB -0.56 -0.50 0.31 1.00
## PP.Nat_2R_VB -0.49 -0.45 0.12 0.87
## PP.Nat_3R_VB -0.47 -0.57 -0.01 0.73
## PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Naturalness.GFFB 0.28 0.24
## PP.Nat_1_GFPRB 0.04 -0.03
## PP.Nat_4R_GFPRB 0.61 0.50
## PP.Nat_2R_GFPRB 0.53 0.46
## PP.Nat_3R_GFPRB 0.53 0.51
## PP.Nat_1_CBB -0.69 -0.61
## PP.Nat_4R_CBB -0.07 -0.10
## PP.Nat_2R_CBB -0.14 -0.13
## PP.Nat_3R_CBB -0.15 -0.15
## PP.Nat_1_PBPB -0.28 -0.39
## PP.Nat_4R_PBPB 0.45 0.41
## PP.Nat_2R_PBPB 0.46 0.39
## PP.Nat_3R_PBPB 0.38 0.55
## PP.Nat_1_PBFB -0.35 -0.41
## PP.Nat_4R_PBFB -0.36 -0.33
## PP.Nat_2R_PBFB -0.49 -0.47
## PP.Nat_3R_PBFB -0.45 -0.57
## PP.Nat_1_VB 0.12 -0.01
## PP.Nat_4R_VB 0.87 0.73
## PP.Nat_2R_VB 1.00 0.82
## PP.Nat_3R_VB 0.82 1.00
##
## n= 21
##
##
## P
## PP.Naturalness.GFFB PP.Nat_1_GFPRB PP.Nat_4R_GFPRB
## PP.Naturalness.GFFB 0.0720 0.0002
## PP.Nat_1_GFPRB 0.0720 0.0133
## PP.Nat_4R_GFPRB 0.0002 0.0133
## PP.Nat_2R_GFPRB 0.0001 0.0197 0.0000
## PP.Nat_3R_GFPRB 0.0002 0.1334 0.0000
## PP.Nat_1_CBB 0.0018 0.1058 0.0000
## PP.Nat_4R_CBB 0.8743 0.0709 0.6270
## PP.Nat_2R_CBB 0.9045 0.0243 0.3048
## PP.Nat_3R_CBB 0.9269 0.0270 0.3362
## PP.Nat_1_PBPB 0.0003 0.1550 0.0012
## PP.Nat_4R_PBPB 0.7112 0.2245 0.6473
## PP.Nat_2R_PBPB 0.6608 0.1462 0.8822
## PP.Nat_3R_PBPB 0.6817 0.0032 0.6601
## PP.Nat_1_PBFB 0.0000 0.0894 0.0000
## PP.Nat_4R_PBFB 0.5246 0.0206 0.7396
## PP.Nat_2R_PBFB 0.9778 0.0284 0.8256
## PP.Nat_3R_PBFB 0.8772 0.0099 0.9767
## PP.Nat_1_VB 0.0316 0.7965 0.1063
## PP.Nat_4R_VB 0.3817 0.7676 0.0331
## PP.Nat_2R_VB 0.2142 0.8583 0.0036
## PP.Nat_3R_VB 0.2859 0.9089 0.0197
## PP.Nat_2R_GFPRB PP.Nat_3R_GFPRB PP.Nat_1_CBB PP.Nat_4R_CBB
## PP.Naturalness.GFFB 0.0001 0.0002 0.0018 0.8743
## PP.Nat_1_GFPRB 0.0197 0.1334 0.1058 0.0709
## PP.Nat_4R_GFPRB 0.0000 0.0000 0.0000 0.6270
## PP.Nat_2R_GFPRB 0.0000 0.0000 0.3675
## PP.Nat_3R_GFPRB 0.0000 0.0000 0.4250
## PP.Nat_1_CBB 0.0000 0.0000 0.1262
## PP.Nat_4R_CBB 0.3675 0.4250 0.1262
## PP.Nat_2R_CBB 0.2019 0.3505 0.1459 0.0000
## PP.Nat_3R_CBB 0.2376 0.5435 0.2553 0.0000
## PP.Nat_1_PBPB 0.0003 0.0002 0.0006 0.6996
## PP.Nat_4R_PBPB 0.7608 0.7316 0.5280 0.2081
## PP.Nat_2R_PBPB 0.6262 0.7821 0.4762 0.0851
## PP.Nat_3R_PBPB 0.3708 0.9806 0.5308 0.2018
## PP.Nat_1_PBFB 0.0000 0.0000 0.0000 0.3451
## PP.Nat_4R_PBFB 0.3828 0.5970 0.3568 0.0060
## PP.Nat_2R_PBFB 0.7052 0.8916 0.9209 0.0018
## PP.Nat_3R_PBFB 0.6357 0.8426 0.9249 0.0234
## PP.Nat_1_VB 0.0671 0.0363 0.1731 0.2986
## PP.Nat_4R_VB 0.1149 0.0980 0.0153 0.9822
## PP.Nat_2R_VB 0.0133 0.0127 0.0006 0.7679
## PP.Nat_3R_VB 0.0357 0.0193 0.0032 0.6614
## PP.Nat_2R_CBB PP.Nat_3R_CBB PP.Nat_1_PBPB PP.Nat_4R_PBPB
## PP.Naturalness.GFFB 0.9045 0.9269 0.0003 0.7112
## PP.Nat_1_GFPRB 0.0243 0.0270 0.1550 0.2245
## PP.Nat_4R_GFPRB 0.3048 0.3362 0.0012 0.6473
## PP.Nat_2R_GFPRB 0.2019 0.2376 0.0003 0.7608
## PP.Nat_3R_GFPRB 0.3505 0.5435 0.0002 0.7316
## PP.Nat_1_CBB 0.1459 0.2553 0.0006 0.5280
## PP.Nat_4R_CBB 0.0000 0.0000 0.6996 0.2081
## PP.Nat_2R_CBB 0.0000 0.8603 0.3821
## PP.Nat_3R_CBB 0.0000 0.9431 0.5881
## PP.Nat_1_PBPB 0.8603 0.9431 0.2049
## PP.Nat_4R_PBPB 0.3821 0.5881 0.2049
## PP.Nat_2R_PBPB 0.0352 0.0562 0.4701 0.0000
## PP.Nat_3R_PBPB 0.0645 0.0688 0.9992 0.0012
## PP.Nat_1_PBFB 0.4269 0.4808 0.0000 0.7107
## PP.Nat_4R_PBFB 0.0196 0.0384 0.0490 0.0010
## PP.Nat_2R_PBFB 0.0012 0.0021 0.7135 0.0026
## PP.Nat_3R_PBFB 0.0075 0.0119 0.7949 0.0060
## PP.Nat_1_VB 0.1774 0.1251 0.0001 0.3437
## PP.Nat_4R_VB 0.6009 0.5437 0.9989 0.0005
## PP.Nat_2R_VB 0.5557 0.5255 0.2141 0.0394
## PP.Nat_3R_VB 0.5688 0.5272 0.0832 0.0671
## PP.Nat_2R_PBPB PP.Nat_3R_PBPB PP.Nat_1_PBFB PP.Nat_4R_PBFB
## PP.Naturalness.GFFB 0.6608 0.6817 0.0000 0.5246
## PP.Nat_1_GFPRB 0.1462 0.0032 0.0894 0.0206
## PP.Nat_4R_GFPRB 0.8822 0.6601 0.0000 0.7396
## PP.Nat_2R_GFPRB 0.6262 0.3708 0.0000 0.3828
## PP.Nat_3R_GFPRB 0.7821 0.9806 0.0000 0.5970
## PP.Nat_1_CBB 0.4762 0.5308 0.0000 0.3568
## PP.Nat_4R_CBB 0.0851 0.2018 0.3451 0.0060
## PP.Nat_2R_CBB 0.0352 0.0645 0.4269 0.0196
## PP.Nat_3R_CBB 0.0562 0.0688 0.4808 0.0384
## PP.Nat_1_PBPB 0.4701 0.9992 0.0000 0.0490
## PP.Nat_4R_PBPB 0.0000 0.0012 0.7107 0.0010
## PP.Nat_2R_PBPB 0.0000 0.7544 0.0029
## PP.Nat_3R_PBPB 0.0000 0.9156 0.0071
## PP.Nat_1_PBFB 0.7544 0.9156 0.0556
## PP.Nat_4R_PBFB 0.0029 0.0071 0.0556
## PP.Nat_2R_PBFB 0.0000 0.0000 0.5312 0.0000
## PP.Nat_3R_PBFB 0.0001 0.0000 0.6776 0.0000
## PP.Nat_1_VB 0.9179 0.6096 0.0012 0.1474
## PP.Nat_4R_VB 0.0126 0.0441 0.4803 0.0077
## PP.Nat_2R_VB 0.0365 0.0924 0.1195 0.1073
## PP.Nat_3R_VB 0.0806 0.0105 0.0632 0.1406
## PP.Nat_2R_PBFB PP.Nat_3R_PBFB PP.Nat_1_VB PP.Nat_4R_VB
## PP.Naturalness.GFFB 0.9778 0.8772 0.0316 0.3817
## PP.Nat_1_GFPRB 0.0284 0.0099 0.7965 0.7676
## PP.Nat_4R_GFPRB 0.8256 0.9767 0.1063 0.0331
## PP.Nat_2R_GFPRB 0.7052 0.6357 0.0671 0.1149
## PP.Nat_3R_GFPRB 0.8916 0.8426 0.0363 0.0980
## PP.Nat_1_CBB 0.9209 0.9249 0.1731 0.0153
## PP.Nat_4R_CBB 0.0018 0.0234 0.2986 0.9822
## PP.Nat_2R_CBB 0.0012 0.0075 0.1774 0.6009
## PP.Nat_3R_CBB 0.0021 0.0119 0.1251 0.5437
## PP.Nat_1_PBPB 0.7135 0.7949 0.0001 0.9989
## PP.Nat_4R_PBPB 0.0026 0.0060 0.3437 0.0005
## PP.Nat_2R_PBPB 0.0000 0.0001 0.9179 0.0126
## PP.Nat_3R_PBPB 0.0000 0.0000 0.6096 0.0441
## PP.Nat_1_PBFB 0.5312 0.6776 0.0012 0.4803
## PP.Nat_4R_PBFB 0.0000 0.0000 0.1474 0.0077
## PP.Nat_2R_PBFB 0.0000 0.9648 0.0090
## PP.Nat_3R_PBFB 0.0000 0.9745 0.0215
## PP.Nat_1_VB 0.9648 0.9745 0.1759
## PP.Nat_4R_VB 0.0090 0.0215 0.1759
## PP.Nat_2R_VB 0.0256 0.0397 0.5996 0.0000
## PP.Nat_3R_VB 0.0307 0.0069 0.9577 0.0002
## PP.Nat_2R_VB PP.Nat_3R_VB
## PP.Naturalness.GFFB 0.2142 0.2859
## PP.Nat_1_GFPRB 0.8583 0.9089
## PP.Nat_4R_GFPRB 0.0036 0.0197
## PP.Nat_2R_GFPRB 0.0133 0.0357
## PP.Nat_3R_GFPRB 0.0127 0.0193
## PP.Nat_1_CBB 0.0006 0.0032
## PP.Nat_4R_CBB 0.7679 0.6614
## PP.Nat_2R_CBB 0.5557 0.5688
## PP.Nat_3R_CBB 0.5255 0.5272
## PP.Nat_1_PBPB 0.2141 0.0832
## PP.Nat_4R_PBPB 0.0394 0.0671
## PP.Nat_2R_PBPB 0.0365 0.0806
## PP.Nat_3R_PBPB 0.0924 0.0105
## PP.Nat_1_PBFB 0.1195 0.0632
## PP.Nat_4R_PBFB 0.1073 0.1406
## PP.Nat_2R_PBFB 0.0256 0.0307
## PP.Nat_3R_PBFB 0.0397 0.0069
## PP.Nat_1_VB 0.5996 0.9577
## PP.Nat_4R_VB 0.0000 0.0002
## PP.Nat_2R_VB 0.0000
## PP.Nat_3R_VB 0.0000library(corrplot)## corrplot 0.92 loadedcorrplot(mydata.cor5, method="color")
corrplot(mydata.cor5, addCoef.col = 1, number.cex = 0.3, method = 'number')
#Naturalness and Support of Technology
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 Perceptions, Willingness to Support Technologies, 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, Risk, Benefit Perceptions and Willingness to Support Technology
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')
Long Form (Reshape Dataset)
## Checking length of variables before transforming
length(PP$Naturalness.GFFB)## [1] 1005length(PP$Naturalness.GFPRB)## [1] 1005length(PP$Naturalness.CBB)## [1] 1005length(PP$Naturalness.PBPB)## [1] 1005length(PP$Naturalness.PBFB)## [1] 1005length(PP$Naturalness.VB)## [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$FR.GFFB)## [1] 1005length(PP$FR.GFPRB)## [1] 1005length(PP$FR.CBB)## [1] 1005length(PP$FR.PBPB)## [1] 1005length(PP$FR.PBFB)## [1] 1005length(PP$FR.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] 1005#Renaming variables to fit pivot_longer command
PP$Support.GFFB <- PP$Behav_Score_GFFB
length(PP$Support.GFFB)## [1] 1005PP$Support.GFPRB <- PP$Behav_Score_GFPRB
length(PP$Support.GFPRB)## [1] 1005PP$Support.CBB <- PP$Behav_Score_CBB
length(PP$Support.CBB)## [1] 1005PP$Support.PBPB <- PP$Behav_Score_PBPB
length(PP$Support.PBPB)## [1] 1005PP$Support.PBFB <- PP$Behav_Score_PBFB
length(PP$Support.PBFB)## [1] 1005PP$Support.VB <- PP$Behav_Score_VB
length(PP$Support.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", "Support.GFFB", "Support.GFPRB", "Support.CBB","Support.PBPB", "Support.PBFB", "Support.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", "FR.GFFB", "FR.GFPRB", "FR.CBB", "FR.PBPB", "FR.PBFB", "FR.VB")
L <- reshape(data = PP,
varying = PPvector,
timevar = "Type",
direction = "long")
#Check length of variables after being long-transformed
length(L$Ben)## [1] 6030length(L$FR)## [1] 6030length(L$Naturalness)## [1] 6030length(L$Risk)## [1] 6030length(L$Support)## [1] 6030length(L$AW_Score)## [1] 6030length(L$ATNS_Score)## [1] 6030length(L$CCBelief_Score)## [1] 6030length(L$CNS_Score)## [1] 6030length(L$Individualism_Score)## [1] 6030length(PP$Collectivism_Score)## [1] 1005length(L$Ideology)## [1] 6030length(L$SESNum)## [1] 6030length(L$EdNum)## [1] 6030length(L$Dem_Age)## [1] 6030length(L$Dem_Gen)## [1] 6030Correlations
Environmentalism
#Correlation between environmentalism measures, including animal welfare concerns, aversion to tampering with nature, climate change belief, connectedness to nature, and political ideology
PP$corID <- data.frame(PP$AW_Score, PP$ATNS_Score, PP$CCBelief_Score, PP$CNS_Score, PP$Ideology)
mydata.corID = cor(PP$corID, use = "pairwise.complete.obs")
head(round(mydata.corID,2))## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 1.00 0.30 0.44 0.31
## PP.ATNS_Score 0.30 1.00 0.26 0.29
## PP.CCBelief_Score 0.44 0.26 1.00 0.38
## PP.CNS_Score 0.31 0.29 0.38 1.00
## PP.Ideology 0.00 0.03 -0.04 0.05
## PP.Ideology
## PP.AW_Score 0.00
## PP.ATNS_Score 0.03
## PP.CCBelief_Score -0.04
## PP.CNS_Score 0.05
## PP.Ideology 1.00library("Hmisc")
mydata.rcorrID = rcorr(as.matrix(mydata.corID))
mydata.rcorrID## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 1.00 0.06 0.44 0.07
## PP.ATNS_Score 0.06 1.00 -0.02 0.01
## PP.CCBelief_Score 0.44 -0.02 1.00 0.26
## PP.CNS_Score 0.07 0.01 0.26 1.00
## PP.Ideology -0.65 -0.50 -0.71 -0.52
## PP.Ideology
## PP.AW_Score -0.65
## PP.ATNS_Score -0.50
## PP.CCBelief_Score -0.71
## PP.CNS_Score -0.52
## PP.Ideology 1.00
##
## n= 5
##
##
## P
## PP.AW_Score PP.ATNS_Score PP.CCBelief_Score PP.CNS_Score
## PP.AW_Score 0.9214 0.4595 0.9075
## PP.ATNS_Score 0.9214 0.9780 0.9871
## PP.CCBelief_Score 0.4595 0.9780 0.6785
## PP.CNS_Score 0.9075 0.9871 0.6785
## PP.Ideology 0.2382 0.3930 0.1820 0.3726
## PP.Ideology
## PP.AW_Score 0.2382
## PP.ATNS_Score 0.3930
## PP.CCBelief_Score 0.1820
## PP.CNS_Score 0.3726
## PP.Ideologylibrary(corrplot)
corrplot(mydata.corID, method="color")
corrplot(mydata.corID, addCoef.col = 1, number.cex = 0.3, method = 'number')
Individual Differences
# Correlation between animal welfare concern, aversion to tampering with nature, climate change belief, connectedness to nature, disgust sensitivity, highest education attained, individualism, collectivism, political orientation, and political party.
L$corID <- data.frame(L$AW_Score, L$ATNS_Score, L$CCBelief_Score, L$CNS_Score, L$DS_Score, L$EdNum, L$Individualism_Score, L$Collectivism_Score, L$Ideology)
mydata.cor9 = cor(L$corID, use = "pairwise.complete.obs")
head(round(mydata.cor9,2))## L.AW_Score L.ATNS_Score L.CCBelief_Score L.CNS_Score
## L.AW_Score 1.00 0.30 0.44 0.31
## L.ATNS_Score 0.30 1.00 0.26 0.29
## L.CCBelief_Score 0.44 0.26 1.00 0.38
## L.CNS_Score 0.31 0.29 0.38 1.00
## L.DS_Score 0.08 0.16 0.04 -0.05
## L.EdNum 0.01 0.00 0.05 0.05
## L.DS_Score L.EdNum L.Individualism_Score L.Collectivism_Score
## L.AW_Score 0.08 0.01 0.40 0.27
## L.ATNS_Score 0.16 0.00 0.34 0.31
## L.CCBelief_Score 0.04 0.05 0.37 0.18
## L.CNS_Score -0.05 0.05 0.27 0.08
## L.DS_Score 1.00 -0.01 0.14 0.22
## L.EdNum -0.01 1.00 0.00 0.01
## L.Ideology
## L.AW_Score 0.00
## L.ATNS_Score 0.03
## L.CCBelief_Score -0.04
## L.CNS_Score 0.05
## L.DS_Score -0.04
## L.EdNum -0.01library("Hmisc")
mydata.rcorr9 = rcorr(as.matrix(mydata.cor9))
mydata.rcorr9## L.AW_Score L.ATNS_Score L.CCBelief_Score L.CNS_Score
## L.AW_Score 1.00 0.28 0.58 0.33
## L.ATNS_Score 0.28 1.00 0.20 0.23
## L.CCBelief_Score 0.58 0.20 1.00 0.48
## L.CNS_Score 0.33 0.23 0.48 1.00
## L.DS_Score -0.20 -0.01 -0.28 -0.46
## L.EdNum -0.35 -0.40 -0.25 -0.21
## L.Individualism_Score 0.46 0.35 0.39 0.18
## L.Collectivism_Score 0.21 0.30 0.05 -0.18
## L.Ideology -0.37 -0.30 -0.41 -0.18
## L.DS_Score L.EdNum L.Individualism_Score
## L.AW_Score -0.20 -0.35 0.46
## L.ATNS_Score -0.01 -0.40 0.35
## L.CCBelief_Score -0.28 -0.25 0.39
## L.CNS_Score -0.46 -0.21 0.18
## L.DS_Score 1.00 -0.23 -0.06
## L.EdNum -0.23 1.00 -0.42
## L.Individualism_Score -0.06 -0.42 1.00
## L.Collectivism_Score 0.19 -0.33 0.59
## L.Ideology -0.29 -0.15 -0.35
## L.Collectivism_Score L.Ideology
## L.AW_Score 0.21 -0.37
## L.ATNS_Score 0.30 -0.30
## L.CCBelief_Score 0.05 -0.41
## L.CNS_Score -0.18 -0.18
## L.DS_Score 0.19 -0.29
## L.EdNum -0.33 -0.15
## L.Individualism_Score 0.59 -0.35
## L.Collectivism_Score 1.00 -0.42
## L.Ideology -0.42 1.00
##
## n= 9
##
##
## P
## L.AW_Score L.ATNS_Score L.CCBelief_Score L.CNS_Score
## L.AW_Score 0.4683 0.0995 0.3795
## L.ATNS_Score 0.4683 0.6059 0.5502
## L.CCBelief_Score 0.0995 0.6059 0.1963
## L.CNS_Score 0.3795 0.5502 0.1963
## L.DS_Score 0.6067 0.9842 0.4686 0.2175
## L.EdNum 0.3589 0.2864 0.5242 0.5835
## L.Individualism_Score 0.2127 0.3557 0.2956 0.6343
## L.Collectivism_Score 0.5863 0.4331 0.9037 0.6481
## L.Ideology 0.3269 0.4349 0.2788 0.6369
## L.DS_Score L.EdNum L.Individualism_Score
## L.AW_Score 0.6067 0.3589 0.2127
## L.ATNS_Score 0.9842 0.2864 0.3557
## L.CCBelief_Score 0.4686 0.5242 0.2956
## L.CNS_Score 0.2175 0.5835 0.6343
## L.DS_Score 0.5431 0.8696
## L.EdNum 0.5431 0.2594
## L.Individualism_Score 0.8696 0.2594
## L.Collectivism_Score 0.6274 0.3822 0.0928
## L.Ideology 0.4501 0.7026 0.3527
## L.Collectivism_Score L.Ideology
## L.AW_Score 0.5863 0.3269
## L.ATNS_Score 0.4331 0.4349
## L.CCBelief_Score 0.9037 0.2788
## L.CNS_Score 0.6481 0.6369
## L.DS_Score 0.6274 0.4501
## L.EdNum 0.3822 0.7026
## L.Individualism_Score 0.0928 0.3527
## L.Collectivism_Score 0.2555
## L.Ideology 0.2555library(corrplot)
corrplot(mydata.cor9, method="color")
corrplot(mydata.cor9, addCoef.col = 1, number.cex = 0.3, method = 'number')
Technology Ratings
#Correlation between perceived benefits, familiarity/understanding, naturalness, risk, and willingness to support technology.
L$corT <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Support)
mydata.cor11AT = cor(L$corT, use = "pairwise.complete.obs")
head(round(mydata.cor11AT,2))## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.51 0.27 -0.26 0.80
## L.FR 0.51 1.00 0.27 -0.12 0.55
## L.Naturalness 0.27 0.27 1.00 -0.39 0.28
## L.Risk -0.26 -0.12 -0.39 1.00 -0.25
## L.Support 0.80 0.55 0.28 -0.25 1.00library("Hmisc")
mydata.rcorr11AT = rcorr(as.matrix(mydata.cor11AT))
mydata.rcorr11AT## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 1.00 0.66 0.35 -0.78 0.96
## L.FR 0.66 1.00 0.31 -0.65 0.70
## L.Naturalness 0.35 0.31 1.00 -0.84 0.35
## L.Risk -0.78 -0.65 -0.84 1.00 -0.78
## L.Support 0.96 0.70 0.35 -0.78 1.00
##
## n= 5
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Support
## L.Ben 0.2222 0.5639 0.1165 0.0108
## L.FR 0.2222 0.6173 0.2345 0.1900
## L.Naturalness 0.5639 0.6173 0.0719 0.5634
## L.Risk 0.1165 0.2345 0.0719 0.1171
## L.Support 0.0108 0.1900 0.5634 0.1171library(corrplot)
corrplot(mydata.cor11AT, method="color")
corrplot(mydata.cor11AT, addCoef.col = 1, number.cex = 0.3, method = 'number')
All: Individual Differences & Tech Ratings
#Correlation between perceived benefits, familiarity/understanding, naturalness, risk, willingness to support technology, age, gender, highest EDU attained, socioeconomic status, animal welfare, aversion to tampering with nature, climate change belief, collectivism, connectedness to nature, disgust sensitivity, individualism, and political ideology.
L$corR <- data.frame(L$Ben, L$FR, L$Naturalness, L$Risk, L$Support, L$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.Support L.Dem_Age L.Dem_Gen
## L.Ben 1.00 0.51 0.27 -0.26 0.80 -0.10 0.06
## L.FR 0.51 1.00 0.27 -0.12 0.55 -0.06 0.08
## L.Naturalness 0.27 0.27 1.00 -0.39 0.28 0.05 0.01
## L.Risk -0.26 -0.12 -0.39 1.00 -0.25 -0.08 -0.02
## L.Support 0.80 0.55 0.28 -0.25 1.00 -0.18 0.07
## L.Dem_Age -0.10 -0.06 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 0.07 0.07 0.19 0.07 0.19
## 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.Support 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 0.07 -0.02
## L.Naturalness -0.01 0.04 -0.01
## L.Risk 0.08 -0.08 0.04
## L.Support 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 0.17 -0.05
## L.Naturalness 0.01 0.03
## L.Risk -0.03 -0.05
## L.Support 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.Support L.Dem_Age
## L.Ben 1.00 0.80 0.49 -0.58 0.97 -0.39
## L.FR 0.80 1.00 0.47 -0.46 0.82 -0.36
## L.Naturalness 0.49 0.47 1.00 -0.74 0.49 -0.06
## L.Risk -0.58 -0.46 -0.74 1.00 -0.56 -0.09
## L.Support 0.97 0.82 0.49 -0.56 1.00 -0.46
## L.Dem_Age -0.39 -0.36 -0.06 -0.09 -0.46 1.00
## L.Dem_Gen -0.01 0.01 -0.02 -0.09 0.01 -0.09
## L.EdNum -0.08 -0.07 -0.05 -0.12 -0.05 0.03
## L.SESNum -0.08 -0.07 -0.06 -0.10 -0.07 0.00
## L.AW_Score 0.20 0.18 -0.09 -0.05 0.21 -0.08
## L.ATNS_Score -0.14 -0.13 -0.24 0.18 -0.18 -0.04
## L.CCBelief_Score 0.26 0.22 -0.05 -0.15 0.27 -0.14
## L.Collectivism_Score 0.21 0.15 -0.17 0.06 0.15 -0.03
## L.CNS_Score -0.05 -0.04 -0.05 -0.15 -0.04 0.09
## PP.DS_Score -0.09 -0.20 -0.16 0.11 -0.13 0.00
## L.Individualism_Score 0.11 0.10 -0.14 -0.06 0.06 -0.01
## L.Ideology -0.26 -0.27 -0.03 -0.07 -0.25 -0.04
## L.Dem_Gen L.EdNum L.SESNum L.AW_Score L.ATNS_Score
## L.Ben -0.01 -0.08 -0.08 0.20 -0.14
## L.FR 0.01 -0.07 -0.07 0.18 -0.13
## L.Naturalness -0.02 -0.05 -0.06 -0.09 -0.24
## L.Risk -0.09 -0.12 -0.10 -0.05 0.18
## L.Support 0.01 -0.05 -0.07 0.21 -0.18
## L.Dem_Age -0.09 0.03 0.00 -0.08 -0.04
## L.Dem_Gen 1.00 0.24 0.34 -0.39 -0.38
## L.EdNum 0.24 1.00 0.57 -0.28 -0.30
## 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 -0.30 -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 -0.35 -0.21 -0.23 0.04 0.21
## L.Individualism_Score -0.36 -0.30 -0.24 0.62 0.55
## L.Ideology -0.11 -0.11 -0.07 -0.18 -0.09
## L.CCBelief_Score L.Collectivism_Score L.CNS_Score
## L.Ben 0.26 0.21 -0.05
## L.FR 0.22 0.15 -0.04
## L.Naturalness -0.05 -0.17 -0.05
## L.Risk -0.15 0.06 -0.15
## L.Support 0.27 0.15 -0.04
## L.Dem_Age -0.14 -0.03 0.09
## 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 -0.09 0.11 -0.26
## L.FR -0.20 0.10 -0.27
## L.Naturalness -0.16 -0.14 -0.03
## L.Risk 0.11 -0.06 -0.07
## L.Support -0.13 0.06 -0.25
## L.Dem_Age 0.00 -0.01 -0.04
## L.Dem_Gen -0.35 -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 0.55 -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 -0.13
## L.Ideology -0.16 -0.13 1.00
##
## n= 17
##
##
## P
## L.Ben L.FR L.Naturalness L.Risk L.Support L.Dem_Age
## L.Ben 0.0001 0.0456 0.0157 0.0000 0.1178
## L.FR 0.0001 0.0544 0.0657 0.0000 0.1570
## L.Naturalness 0.0456 0.0544 0.0006 0.0438 0.8215
## L.Risk 0.0157 0.0657 0.0006 0.0201 0.7406
## L.Support 0.0000 0.0000 0.0438 0.0201 0.0639
## L.Dem_Age 0.1178 0.1570 0.8215 0.7406 0.0639
## L.Dem_Gen 0.9603 0.9800 0.9413 0.7350 0.9672 0.7234
## L.EdNum 0.7644 0.8012 0.8386 0.6600 0.8557 0.9027
## L.SESNum 0.7508 0.7769 0.8313 0.6893 0.7907 0.9972
## L.AW_Score 0.4455 0.4902 0.7400 0.8400 0.4257 0.7557
## L.ATNS_Score 0.5942 0.6195 0.3576 0.4889 0.4890 0.8791
## L.CCBelief_Score 0.3096 0.3917 0.8600 0.5780 0.2856 0.5868
## L.Collectivism_Score 0.4232 0.5702 0.5257 0.8090 0.5768 0.8976
## L.CNS_Score 0.8613 0.8763 0.8478 0.5534 0.8670 0.7393
## PP.DS_Score 0.7418 0.4476 0.5382 0.6773 0.6288 0.9938
## L.Individualism_Score 0.6779 0.7139 0.5964 0.8130 0.8279 0.9676
## L.Ideology 0.3062 0.2945 0.9117 0.7774 0.3339 0.8926
## L.Dem_Gen L.EdNum L.SESNum L.AW_Score L.ATNS_Score
## L.Ben 0.9603 0.7644 0.7508 0.4455 0.5942
## L.FR 0.9800 0.8012 0.7769 0.4902 0.6195
## L.Naturalness 0.9413 0.8386 0.8313 0.7400 0.3576
## L.Risk 0.7350 0.6600 0.6893 0.8400 0.4889
## L.Support 0.9672 0.8557 0.7907 0.4257 0.4890
## L.Dem_Age 0.7234 0.9027 0.9972 0.7557 0.8791
## L.Dem_Gen 0.3581 0.1803 0.1208 0.1333
## L.EdNum 0.3581 0.0174 0.2743 0.2342
## L.SESNum 0.1803 0.0174 0.1609 0.1946
## L.AW_Score 0.1208 0.2743 0.1609 0.0557
## L.ATNS_Score 0.1333 0.2342 0.1946 0.0557
## L.CCBelief_Score 0.2073 0.4755 0.3165 0.0018 0.1201
## L.Collectivism_Score 0.2725 0.2969 0.6087 0.1264 0.0819
## L.CNS_Score 0.2965 0.5576 0.3134 0.0447 0.0861
## PP.DS_Score 0.1746 0.4280 0.3745 0.8856 0.4182
## L.Individualism_Score 0.1539 0.2470 0.3477 0.0079 0.0234
## L.Ideology 0.6749 0.6830 0.7869 0.4836 0.7442
## L.CCBelief_Score L.Collectivism_Score L.CNS_Score
## L.Ben 0.3096 0.4232 0.8613
## L.FR 0.3917 0.5702 0.8763
## L.Naturalness 0.8600 0.5257 0.8478
## L.Risk 0.5780 0.8090 0.5534
## L.Support 0.2856 0.5768 0.8670
## L.Dem_Age 0.5868 0.8976 0.7393
## L.Dem_Gen 0.2073 0.2725 0.2965
## L.EdNum 0.4755 0.2969 0.5576
## L.SESNum 0.3165 0.6087 0.3134
## L.AW_Score 0.0018 0.1264 0.0447
## L.ATNS_Score 0.1201 0.0819 0.0861
## L.CCBelief_Score 0.3248 0.0142
## L.Collectivism_Score 0.3248 0.8674
## L.CNS_Score 0.0142 0.8674
## PP.DS_Score 0.8616 0.2381 0.4724
## L.Individualism_Score 0.0186 0.0032 0.1014
## L.Ideology 0.3843 0.2663 0.9386
## PP.DS_Score L.Individualism_Score L.Ideology
## L.Ben 0.7418 0.6779 0.3062
## L.FR 0.4476 0.7139 0.2945
## L.Naturalness 0.5382 0.5964 0.9117
## L.Risk 0.6773 0.8130 0.7774
## L.Support 0.6288 0.8279 0.3339
## L.Dem_Age 0.9938 0.9676 0.8926
## L.Dem_Gen 0.1746 0.1539 0.6749
## L.EdNum 0.4280 0.2470 0.6830
## L.SESNum 0.3745 0.3477 0.7869
## L.AW_Score 0.8856 0.0079 0.4836
## L.ATNS_Score 0.4182 0.0234 0.7442
## L.CCBelief_Score 0.8616 0.0186 0.3843
## L.Collectivism_Score 0.2381 0.0032 0.2663
## L.CNS_Score 0.4724 0.1014 0.9386
## PP.DS_Score 0.5425 0.5403
## L.Individualism_Score 0.5425 0.6096
## L.Ideology 0.5403 0.6096library(corrplot)
corrplot(mydata.cor11A, method="color")
corrplot(mydata.cor11A, addCoef.col = 1, number.cex = 0.3, method = 'number')
Mixed Effects Models
Center Variables
#Counts of observations (in long form) for each of the technology types
table(L$Type) ##
## CBB GFFB GFPRB PBFB PBPB VB
## 1005 1005 1005 1005 1005 1005#Individual Differences
describe(L$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$Collectivism_Score - 66.53
# Note: Ideology score is the mean of political party (-3 Dem to +3 Rep) and political orientation (-3 Lib to +3 Con).
describe(L$Ideology)## L$Ideology
## n missing distinct Info Mean Gmd .05 .10
## 6018 12 14 0.949 1.785 1.154 -0.5 0.5
## .25 .50 .75 .90 .95
## 1.5 2.0 2.5 3.0 3.5
##
## lowest : -1.0 -0.5 0.0 0.5 1.0, highest: 3.5 4.0 4.5 5.0 6.0
##
## Value -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
## Frequency 150 162 210 480 474 852 2142 756 312 294 84
## Proportion 0.025 0.027 0.035 0.080 0.079 0.142 0.356 0.126 0.052 0.049 0.014
##
## Value 4.5 5.0 6.0
## Frequency 36 54 12
## Proportion 0.006 0.009 0.002L$Ideology.c <- L$Ideology -1.785
# Technology Ratings
describe(L$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$Naturalness - 50.22
describe(L$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$Risk - 44.56
describe(L$Support)## L$Support
## n missing distinct Info Mean Gmd .05 .10
## 3008 3022 402 0.999 56.27 34.23 0.000 6.925
## .25 .50 .75 .90 .95
## 36.000 58.500 80.250 96.750 100.000
##
## lowest : 0.00 0.25 0.50 0.75 1.00, highest: 99.00 99.25 99.50 99.75 100.00L$Support.c <- L$Support - 56.27
describe(L$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$FR
## n missing distinct Info Mean Gmd .05 .10
## 3004 3026 199 0.999 61.41 30.2 10.0 24.0
## .25 .50 .75 .90 .95
## 46.0 61.5 83.5 98.0 100.0
##
## lowest : 0.0 0.5 1.0 1.5 2.5, highest: 98.0 98.5 99.0 99.5 100.0L$FR.c <- L$FR - 61.41Contrast Codes
#CBB, PBPB,PBFB vs. VB, GFPRB, GFFB - NEWTECH
L$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 <- (1/6)*(L$Type == 'CBB') + (1/6)*(L$Type == 'PBFB') + (1/6)*(L$Type == 'PBPB') +(1/6)*(L$Type == 'VB') + (-2/6)*(L$Type == 'GFFB') + (-2/6)*(L$Type == 'GFPRB')
#GFFB vs. GFPRB - GRAIN FED BEEF vs. GRASS FED BEEF
L$C4 <- (0)*(L$Type == 'CBB') + (-1/4)*(L$Type == 'PBFB') + (0)*(L$Type == 'PBPB') + (-1/4)*(L$Type == 'VB') + (1/4)*(L$Type == 'GFFB') + (1/4)*(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') + (0)*(L$Type == 'PBPB') + (-1/2)*(L$Type == 'VB') + (0)*(L$Type == 'GFFB') + (0)*(L$Type == 'GFPRB')Support
Q.1: (SIMPLE MODEL) How do burger contrasts predict support?
#Do burger contrasts predict support?
modA.9 <- lmer(Support ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.9)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 28167.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5392 -0.4186 0.0524 0.4297 3.3516
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 449.2 21.19
## Residual 431.6 20.77
## Number of obs: 3008, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.4876 0.7746 1001.9618 72.924 < 2e-16 ***
## C1 -13.3522 1.5398 2277.9453 -8.672 < 2e-16 ***
## C2 -8.7750 1.4015 2276.3850 -6.261 4.56e-10 ***
## C3 30.8670 6.1346 2220.8054 5.032 5.25e-07 ***
## C4 20.5062 5.7431 2299.5777 3.571 0.000364 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3
## C1 -0.006
## C2 -0.023 0.292
## C3 0.009 -0.810 -0.429
## C4 0.012 -0.623 -0.483 0.909
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.9,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.49 | 0.77 | 54.97 – 58.01 | 72.92 | <0.001 |
| C1 | -13.35 | 1.54 | -16.37 – -10.33 | -8.67 | <0.001 |
| C2 | -8.77 | 1.40 | -11.52 – -6.03 | -6.26 | <0.001 |
| C3 | 30.87 | 6.13 | 18.84 – 42.90 | 5.03 | <0.001 |
| C4 | 20.51 | 5.74 | 9.25 – 31.77 | 3.57 | <0.001 |
| Random Effects | |||||
| σ2 | 431.58 | ||||
| τ00 id | 449.23 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 3008 | ||||
| Marginal R2 / Conditional R2 | 0.022 / 0.521 | ||||
confint(modA.9)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 19.962741 22.471364
## .sigma 20.130473 21.412718
## (Intercept) 54.969127 58.006549
## C1 -16.368527 -10.335569
## C2 -11.520497 -6.028999
## C3 18.848647 42.884305
## C4 9.254872 31.756553Q.2: Does naturalness predict support, over and above burger contrasts?
#Does naturalness predict support?
modA.10 <- lmer(Support ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.10)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 25175.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7610 -0.4466 0.0490 0.4857 3.1640
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 401.5 20.04
## Residual 415.3 20.38
## Number of obs: 2697, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.72570 0.75076 1014.19018 75.558 < 2e-16 ***
## Naturalness.c 0.37880 0.02132 2148.33807 17.770 < 2e-16 ***
## C1 -16.50189 1.52687 1971.50502 -10.808 < 2e-16 ***
## C2 -6.10144 1.38482 1946.99820 -4.406 1.11e-05 ***
## C3 63.34300 6.38492 1931.32546 9.921 < 2e-16 ***
## C4 51.47974 5.89950 1991.60498 8.726 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3
## Naturlnss.c 0.016
## C1 -0.009 -0.115
## C2 -0.016 0.106 0.276
## C3 -0.015 0.287 -0.792 -0.373
## C4 0.014 0.293 -0.625 -0.425 0.907
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.10,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.73 | 0.75 | 55.25 – 58.20 | 75.56 | <0.001 |
| Naturalness c | 0.38 | 0.02 | 0.34 – 0.42 | 17.77 | <0.001 |
| C1 | -16.50 | 1.53 | -19.50 – -13.51 | -10.81 | <0.001 |
| C2 | -6.10 | 1.38 | -8.82 – -3.39 | -4.41 | <0.001 |
| C3 | 63.34 | 6.38 | 50.82 – 75.86 | 9.92 | <0.001 |
| C4 | 51.48 | 5.90 | 39.91 – 63.05 | 8.73 | <0.001 |
| Random Effects | |||||
| σ2 | 415.26 | ||||
| τ00 id | 401.53 | ||||
| ICC | 0.49 | ||||
| N id | 1003 | ||||
| Observations | 2697 | ||||
| Marginal R2 / Conditional R2 | 0.096 / 0.540 | ||||
confint(modA.10)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 18.8077789 21.3074765
## .sigma 19.6845967 21.0547464
## (Intercept) 55.2541504 58.1977410
## Naturalness.c 0.3370447 0.4205579
## C1 -19.4920471 -13.5116174
## C2 -8.8136824 -3.3886462
## C3 50.8387153 75.8470175
## C4 39.9262693 63.0331464Q.3: Does perceived benefit predict support, over and above perceived risk, naturalness, and burger contrasts?
modA.11 <- lmer(Support ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.11)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Risk.c + Benefit.c + C1 + C2 + C3 +
## C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 23088.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0825 -0.3651 0.0974 0.4435 4.5839
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 79.84 8.935
## Residual 247.22 15.723
## Number of obs: 2691, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.18738 0.41942 941.98623 136.349 < 2e-16 ***
## Naturalness.c 0.08093 0.01708 2435.83329 4.739 2.27e-06 ***
## Risk.c -0.04704 0.01410 2443.81745 -3.335 0.000865 ***
## Benefit.c 0.80453 0.01445 2378.77229 55.674 < 2e-16 ***
## C1 -1.96051 1.18317 2196.62267 -1.657 0.097663 .
## C2 -0.09958 1.04455 2105.22428 -0.095 0.924060
## C3 6.79245 4.95069 2111.22505 1.372 0.170203
## C4 11.33418 4.48404 2187.27998 2.528 0.011552 *
## ---
## 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
## Naturlnss.c 0.010
## Risk.c -0.009 0.289
## Benefit.c 0.017 -0.220 0.227
## C1 -0.003 -0.211 -0.180 0.156
## C2 -0.008 0.049 -0.086 0.066 0.301
## C3 -0.033 0.351 0.174 -0.143 -0.806 -0.397
## C4 0.002 0.339 0.142 -0.110 -0.648 -0.442 0.913
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.11,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.19 | 0.42 | 56.36 – 58.01 | 136.35 | <0.001 |
| Naturalness c | 0.08 | 0.02 | 0.05 – 0.11 | 4.74 | <0.001 |
| Risk c | -0.05 | 0.01 | -0.07 – -0.02 | -3.34 | 0.001 |
| Benefit c | 0.80 | 0.01 | 0.78 – 0.83 | 55.67 | <0.001 |
| C1 | -1.96 | 1.18 | -4.28 – 0.36 | -1.66 | 0.098 |
| C2 | -0.10 | 1.04 | -2.15 – 1.95 | -0.10 | 0.924 |
| C3 | 6.79 | 4.95 | -2.92 – 16.50 | 1.37 | 0.170 |
| C4 | 11.33 | 4.48 | 2.54 – 20.13 | 2.53 | 0.012 |
| Random Effects | |||||
| σ2 | 247.22 | ||||
| τ00 id | 79.84 | ||||
| ICC | 0.24 | ||||
| N id | 1002 | ||||
| Observations | 2691 | ||||
| Marginal R2 / Conditional R2 | 0.615 / 0.709 | ||||
confint(modA.11)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 7.95059210 9.88523282
## .sigma 15.17361613 16.25401715
## (Intercept) 56.36530807 58.00887571
## Naturalness.c 0.04749344 0.11436746
## Risk.c -0.07526805 -0.01893988
## Benefit.c 0.77400220 0.83476862
## C1 -4.27708363 0.35713889
## C2 -2.14547529 1.94817858
## C3 -2.90537613 16.48536191
## C4 2.55368488 20.11276678Q.4: Does perceived familiarity/understanding predict support, over and above perceived benefit, risk, naturalness, and burger contrasts?
#How does perceived benefit and naturalness predict intent to support?
modA.12 <- lmer(Support ~ Naturalness.c + Risk.c + Benefit.c + FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.12)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ Naturalness.c + Risk.c + Benefit.c + FR.c + C1 + C2 +
## C3 + C4 + C5 + (1 | id)
## Data: L
##
## REML criterion at convergence: 22925.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4367 -0.3665 0.0923 0.4434 4.1568
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 67.4 8.21
## Residual 239.0 15.46
## Number of obs: 2689, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 57.22961 0.40031 966.92904 142.962 < 2e-16 ***
## Naturalness.c 0.05010 0.01686 2491.29684 2.972 0.00299 **
## Risk.c -0.05420 0.01368 2426.32329 -3.961 7.68e-05 ***
## Benefit.c 0.72549 0.01551 2605.20319 46.781 < 2e-16 ***
## FR.c 0.19467 0.01541 2679.18776 12.632 < 2e-16 ***
## C1 0.29870 1.17109 2272.59715 0.255 0.79870
## C2 0.39582 1.02382 2146.93533 0.387 0.69908
## C3 2.62478 4.85808 2161.39929 0.540 0.58905
## C4 5.26186 4.41362 2245.00191 1.192 0.23331
## ---
## 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
## Naturlnss.c 0.008
## Risk.c -0.009 0.295
## Benefit.c 0.013 -0.132 0.224
## FR.c 0.008 -0.146 -0.057 -0.432
## C1 -0.002 -0.227 -0.187 0.073 0.151
## C2 -0.007 0.044 -0.088 0.043 0.035 0.303
## C3 -0.035 0.354 0.177 -0.100 -0.066 -0.805 -0.398
## C4 0.000 0.348 0.148 -0.051 -0.108 -0.653 -0.443 0.913
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.12,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 57.23 | 0.40 | 56.44 – 58.01 | 142.96 | <0.001 |
| Naturalness c | 0.05 | 0.02 | 0.02 – 0.08 | 2.97 | 0.003 |
| Risk c | -0.05 | 0.01 | -0.08 – -0.03 | -3.96 | <0.001 |
| Benefit c | 0.73 | 0.02 | 0.70 – 0.76 | 46.78 | <0.001 |
| FR c | 0.19 | 0.02 | 0.16 – 0.22 | 12.63 | <0.001 |
| C1 | 0.30 | 1.17 | -2.00 – 2.60 | 0.26 | 0.799 |
| C2 | 0.40 | 1.02 | -1.61 – 2.40 | 0.39 | 0.699 |
| C3 | 2.62 | 4.86 | -6.90 – 12.15 | 0.54 | 0.589 |
| C4 | 5.26 | 4.41 | -3.39 – 13.92 | 1.19 | 0.233 |
| Random Effects | |||||
| σ2 | 238.96 | ||||
| τ00 id | 67.40 | ||||
| ICC | 0.22 | ||||
| N id | 1002 | ||||
| Observations | 2689 | ||||
| Marginal R2 / Conditional R2 | 0.644 / 0.722 | ||||
confint(modA.12)## Computing profile confidence intervals ...## 2.5 % 97.5 %
## .sig01 7.23774677 9.13461467
## .sigma 14.91957532 15.97323241
## (Intercept) 56.44523773 58.01351924
## Naturalness.c 0.01709821 0.08311003
## Risk.c -0.08137953 -0.02711238
## Benefit.c 0.69404260 0.75682259
## FR.c 0.16440516 0.22493046
## C1 -1.99375620 2.59211631
## C2 -1.60934790 2.40308381
## C3 -6.88788601 12.13399805
## C4 -3.37923015 13.90097861Naturalness
Q.1: (SIMPLE MODEL) How do burger contrasts predict naturalness perception?
#How do burger contrasts predict naturalness perception?
modA.13 <- lmer(Naturalness ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.13)## 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: 27013.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.87149 -0.65911 -0.06698 0.62588 3.03602
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 1.493 1.222
## Residual 469.304 21.663
## Number of obs: 3006, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.4261 0.3973 1001.9770 126.930 < 2e-16 ***
## C1 8.2019 1.4746 2762.7876 5.562 2.92e-08 ***
## C2 -8.0446 1.3447 2736.2826 -5.982 2.49e-09 ***
## C3 -89.0300 5.9861 2719.6625 -14.873 < 2e-16 ***
## C4 -79.5933 5.4768 2754.3209 -14.533 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3
## C1 -0.004
## C2 0.004 0.304
## C3 -0.013 -0.804 -0.446
## C4 -0.025 -0.632 -0.487 0.915
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.13,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.43 | 0.40 | 49.65 – 51.21 | 126.93 | <0.001 |
| C1 | 8.20 | 1.47 | 5.31 – 11.09 | 5.56 | <0.001 |
| C2 | -8.04 | 1.34 | -10.68 – -5.41 | -5.98 | <0.001 |
| C3 | -89.03 | 5.99 | -100.77 – -77.29 | -14.87 | <0.001 |
| C4 | -79.59 | 5.48 | -90.33 – -68.85 | -14.53 | <0.001 |
| Random Effects | |||||
| σ2 | 469.30 | ||||
| τ00 id | 1.49 | ||||
| ICC | 0.00 | ||||
| N id | 1004 | ||||
| Observations | 3006 | ||||
| Marginal R2 / Conditional R2 | 0.159 / 0.162 | ||||
Q.2: Does understanding/familiarity (mean score) predict naturalness perception, over and above burger contrasts?
#Note: Understanding/familiarity mean score taken from two item measure.
modA.14 <- lmer(Naturalness ~ FR.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.14)## 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: 26791.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2472 -0.6635 -0.0096 0.6312 3.5012
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 20.12 4.486
## Residual 421.05 20.520
## Number of obs: 3003, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.43565 0.40058 960.90676 125.907 < 2e-16 ***
## FR.c 0.22225 0.01518 2534.94148 14.643 < 2e-16 ***
## C1 11.51322 1.43106 2729.53813 8.045 1.27e-15 ***
## C2 -6.35550 1.29280 2662.35766 -4.916 9.37e-07 ***
## C3 -92.36416 5.73682 2643.18788 -16.100 < 2e-16 ***
## C4 -84.71521 5.26025 2685.56083 -16.105 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) FR.c C1 C2 C3
## FR.c 0.000
## C1 -0.004 0.158
## C2 0.005 0.081 0.310
## C3 -0.013 -0.042 -0.799 -0.446
## C4 -0.023 -0.071 -0.633 -0.489 0.915
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.14,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.44 | 0.40 | 49.65 – 51.22 | 125.91 | <0.001 |
| FR c | 0.22 | 0.02 | 0.19 – 0.25 | 14.64 | <0.001 |
| C1 | 11.51 | 1.43 | 8.71 – 14.32 | 8.05 | <0.001 |
| C2 | -6.36 | 1.29 | -8.89 – -3.82 | -4.92 | <0.001 |
| C3 | -92.36 | 5.74 | -103.61 – -81.12 | -16.10 | <0.001 |
| C4 | -84.72 | 5.26 | -95.03 – -74.40 | -16.10 | <0.001 |
| Random Effects | |||||
| σ2 | 421.05 | ||||
| τ00 id | 20.12 | ||||
| ICC | 0.05 | ||||
| N id | 1004 | ||||
| Observations | 3003 | ||||
| Marginal R2 / Conditional R2 | 0.215 / 0.251 | ||||
Q.3: Does familiarity predict naturalness perception, over and above understanding and burger contrasts?
modA.15 <- lmer(Naturalness ~ Familiarity.c + Understanding.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.15)## 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: 26664.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2591 -0.6643 -0.0082 0.6216 3.4792
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 21.66 4.654
## Residual 417.20 20.426
## Number of obs: 2990, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.48154 0.40169 946.04558 125.674 < 2e-16 ***
## Familiarity.c 0.14909 0.01459 2888.16161 10.216 < 2e-16 ***
## Understanding.c 0.06965 0.01573 2688.85763 4.427 9.93e-06 ***
## C1 11.70451 1.42769 2706.94168 8.198 3.72e-16 ***
## C2 -6.29787 1.28972 2639.48475 -4.883 1.11e-06 ***
## C3 -92.82356 5.72053 2620.48637 -16.226 < 2e-16 ***
## C4 -84.92547 5.24412 2663.63340 -16.194 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Fmlrt. Undrs. C1 C2 C3
## Familirty.c -0.001
## Undrstndng. 0.000 -0.495
## C1 -0.003 0.103 0.055
## C2 0.006 0.063 0.019 0.311
## C3 -0.014 -0.013 -0.029 -0.799 -0.445
## C4 -0.024 -0.041 -0.030 -0.633 -0.488 0.914
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.15,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.48 | 0.40 | 49.69 – 51.27 | 125.67 | <0.001 |
| Familiarity c | 0.15 | 0.01 | 0.12 – 0.18 | 10.22 | <0.001 |
| Understanding c | 0.07 | 0.02 | 0.04 – 0.10 | 4.43 | <0.001 |
| C1 | 11.70 | 1.43 | 8.91 – 14.50 | 8.20 | <0.001 |
| C2 | -6.30 | 1.29 | -8.83 – -3.77 | -4.88 | <0.001 |
| C3 | -92.82 | 5.72 | -104.04 – -81.61 | -16.23 | <0.001 |
| C4 | -84.93 | 5.24 | -95.21 – -74.64 | -16.19 | <0.001 |
| Random Effects | |||||
| σ2 | 417.20 | ||||
| τ00 id | 21.66 | ||||
| ICC | 0.05 | ||||
| N id | 1004 | ||||
| Observations | 2990 | ||||
| Marginal R2 / Conditional R2 | 0.219 / 0.258 | ||||
Risk
Q.1: (SIMPLE MODEL) How do burger contrasts predict risk perception?
modA.16 <- lmer(Risk ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.16)## 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: 30971.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9701 -0.5652 0.0231 0.5181 3.0640
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 332.9 18.25
## Residual 459.9 21.45
## Number of obs: 3319, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.3988 0.6902 1017.4660 64.326 < 2e-16 ***
## C1 12.7431 1.3914 2530.3858 9.158 < 2e-16 ***
## C2 10.7241 1.4212 2603.1813 7.546 6.18e-14 ***
## C3 -25.6314 5.5498 2437.5936 -4.618 4.07e-06 ***
## C4 -10.3949 4.9439 2413.8731 -2.103 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3
## C1 -0.073
## C2 -0.003 0.308
## C3 0.064 -0.745 -0.481
## C4 0.065 -0.509 -0.544 0.887
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.16,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.40 | 0.69 | 43.05 – 45.75 | 64.33 | <0.001 |
| C1 | 12.74 | 1.39 | 10.01 – 15.47 | 9.16 | <0.001 |
| C2 | 10.72 | 1.42 | 7.94 – 13.51 | 7.55 | <0.001 |
| C3 | -25.63 | 5.55 | -36.51 – -14.75 | -4.62 | <0.001 |
| C4 | -10.39 | 4.94 | -20.09 – -0.70 | -2.10 | 0.036 |
| Random Effects | |||||
| σ2 | 459.92 | ||||
| τ00 id | 332.93 | ||||
| ICC | 0.42 | ||||
| N id | 1004 | ||||
| Observations | 3319 | ||||
| Marginal R2 / Conditional R2 | 0.028 / 0.436 | ||||
Q.2: Does naturalness predict risk perception, over and above burger contrasts?
modA.17 <- lmer(Risk ~ Naturalness.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.17)## 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: 27678.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8826 -0.5437 0.0156 0.5354 3.3582
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 248 15.75
## Residual 420 20.49
## Number of obs: 3004, groups: id, 1004
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.84562 0.62221 988.74139 72.075 < 2e-16 ***
## Naturalness.c -0.44683 0.01946 2612.71130 -22.963 < 2e-16 ***
## C1 19.16193 1.49992 2321.89873 12.775 < 2e-16 ***
## C2 7.64490 1.36336 2301.60108 5.607 2.3e-08 ***
## C3 -76.74599 6.26099 2310.79785 -12.258 < 2e-16 ***
## C4 -57.40402 5.73519 2325.84033 -10.009 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. C1 C2 C3
## Naturlnss.c -0.006
## C1 -0.002 -0.105
## C2 0.002 0.107 0.279
## C3 -0.010 0.273 -0.795 -0.391
## C4 -0.017 0.264 -0.627 -0.433 0.920
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.17,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.85 | 0.62 | 43.63 – 46.07 | 72.07 | <0.001 |
| Naturalness c | -0.45 | 0.02 | -0.48 – -0.41 | -22.96 | <0.001 |
| C1 | 19.16 | 1.50 | 16.22 – 22.10 | 12.78 | <0.001 |
| C2 | 7.64 | 1.36 | 4.97 – 10.32 | 5.61 | <0.001 |
| C3 | -76.75 | 6.26 | -89.02 – -64.47 | -12.26 | <0.001 |
| C4 | -57.40 | 5.74 | -68.65 – -46.16 | -10.01 | <0.001 |
| Random Effects | |||||
| σ2 | 420.02 | ||||
| τ00 id | 247.97 | ||||
| ICC | 0.37 | ||||
| N id | 1004 | ||||
| Observations | 3004 | ||||
| Marginal R2 / Conditional R2 | 0.155 / 0.468 | ||||
Q.3: Does benefit predict risk perception, over and above burger contrasts?
modA.18 <- lmer(Risk ~ Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.18)## 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: 27558.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3252 -0.4976 0.0036 0.5124 3.9165
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 418.1 20.45
## Residual 353.7 18.81
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.76024 0.73190 943.77349 61.157 < 2e-16 ***
## Benefit.c -0.43070 0.01715 2797.90737 -25.108 < 2e-16 ***
## C1 9.43473 1.41523 2169.36905 6.667 3.31e-11 ***
## C2 6.63346 1.27829 2150.99869 5.189 2.31e-07 ***
## C3 -23.49518 5.66153 2141.69247 -4.150 3.46e-05 ***
## C4 -13.87464 5.19079 2150.07487 -2.673 0.00758 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Bnft.c C1 C2 C3
## Benefit.c -0.002
## C1 -0.002 0.174
## C2 0.002 0.133 0.307
## C3 -0.006 -0.102 -0.801 -0.446
## C4 -0.013 -0.070 -0.625 -0.485 0.914
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.18,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.76 | 0.73 | 43.33 – 46.20 | 61.16 | <0.001 |
| Benefit c | -0.43 | 0.02 | -0.46 – -0.40 | -25.11 | <0.001 |
| C1 | 9.43 | 1.42 | 6.66 – 12.21 | 6.67 | <0.001 |
| C2 | 6.63 | 1.28 | 4.13 – 9.14 | 5.19 | <0.001 |
| C3 | -23.50 | 5.66 | -34.60 – -12.39 | -4.15 | <0.001 |
| C4 | -13.87 | 5.19 | -24.05 – -3.70 | -2.67 | 0.008 |
| Random Effects | |||||
| σ2 | 353.67 | ||||
| τ00 id | 418.11 | ||||
| ICC | 0.54 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.170 / 0.620 | ||||
Q.4: Does benefit predict risk perception, over and above naturalness and burger contrasts?
modA.19 <- lmer(Risk ~ Naturalness.c + Benefit.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.19)## 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: 27294.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7241 -0.5009 0.0173 0.5157 3.6986
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 336.9 18.36
## Residual 338.9 18.41
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.80019 0.67071 912.36536 66.795 < 2e-16 ***
## Naturalness.c -0.31635 0.01898 2486.37930 -16.664 < 2e-16 ***
## Benefit.c -0.32261 0.01752 2911.36760 -18.414 < 2e-16 ***
## C1 13.55975 1.40083 2196.79128 9.680 < 2e-16 ***
## C2 5.39304 1.24992 2142.57474 4.315 1.67e-05 ***
## C3 -54.98058 5.82991 2190.23911 -9.431 < 2e-16 ***
## C4 -40.78090 5.30883 2185.81262 -7.682 2.35e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Bnft.c C1 C2 C3
## Naturlnss.c -0.005
## Benefit.c 0.000 -0.329
## C1 -0.001 -0.171 0.217
## C2 0.002 0.066 0.102 0.292
## C3 -0.008 0.321 -0.195 -0.802 -0.401
## C4 -0.014 0.301 -0.161 -0.639 -0.442 0.922
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.19,
show.stat = T, show.se = T)| Risk | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 44.80 | 0.67 | 43.49 – 46.12 | 66.79 | <0.001 |
| Naturalness c | -0.32 | 0.02 | -0.35 – -0.28 | -16.66 | <0.001 |
| Benefit c | -0.32 | 0.02 | -0.36 – -0.29 | -18.41 | <0.001 |
| C1 | 13.56 | 1.40 | 10.81 – 16.31 | 9.68 | <0.001 |
| C2 | 5.39 | 1.25 | 2.94 – 7.84 | 4.31 | <0.001 |
| C3 | -54.98 | 5.83 | -66.41 – -43.55 | -9.43 | <0.001 |
| C4 | -40.78 | 5.31 | -51.19 – -30.37 | -7.68 | <0.001 |
| Random Effects | |||||
| σ2 | 338.90 | ||||
| τ00 id | 336.94 | ||||
| ICC | 0.50 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.210 / 0.604 | ||||
Benefit
Q.1: (SIMPLE MODEL) How do burger contrasts predict perceived benefit?
#How do burger contrasts predict perceived benefit?
modA.20 <- lmer(Ben ~ C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.20)## 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: 27857.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2029 -0.4751 0.0694 0.5684 2.5620
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 239.5 15.48
## Residual 472.6 21.74
## Number of obs: 2996, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.4189 0.6301 1000.1541 95.884 < 2e-16 ***
## C1 -14.1417 1.5779 2354.9706 -8.962 < 2e-16 ***
## C2 -9.6241 1.4358 2336.1056 -6.703 2.55e-11 ***
## C3 32.4356 6.3846 2331.6086 5.080 4.07e-07 ***
## C4 20.0843 5.8625 2352.1868 3.426 0.000623 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) C1 C2 C3
## C1 -0.002
## C2 0.003 0.295
## C3 -0.009 -0.801 -0.440
## C4 -0.017 -0.627 -0.482 0.914
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.20,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.42 | 0.63 | 59.18 – 61.65 | 95.88 | <0.001 |
| C1 | -14.14 | 1.58 | -17.24 – -11.05 | -8.96 | <0.001 |
| C2 | -9.62 | 1.44 | -12.44 – -6.81 | -6.70 | <0.001 |
| C3 | 32.44 | 6.38 | 19.92 – 44.95 | 5.08 | <0.001 |
| C4 | 20.08 | 5.86 | 8.59 – 31.58 | 3.43 | 0.001 |
| Random Effects | |||||
| σ2 | 472.61 | ||||
| τ00 id | 239.48 | ||||
| ICC | 0.34 | ||||
| N id | 1003 | ||||
| Observations | 2996 | ||||
| Marginal R2 / Conditional R2 | 0.031 / 0.357 | ||||
Q.2: How does risk perception predict benefit, over and above burger contrasts?
#How does risk perception predict benefit, over and above burger contrasts?
modA.21 <- lmer(Ben ~ Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.21)## 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: 27367.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3515 -0.4412 0.0626 0.5219 3.0345
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 349.0 18.68
## Residual 345.4 18.59
## Number of obs: 2993, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.49668 0.68118 919.16889 88.812 < 2e-16 ***
## Risk.c -0.39287 0.01618 2911.19476 -24.275 < 2e-16 ***
## C1 -8.22383 1.39573 2178.86083 -5.892 4.41e-09 ***
## C2 -5.54352 1.26066 2148.43771 -4.397 1.15e-05 ***
## C3 18.64168 5.58241 2145.33150 3.339 0.000854 ***
## C4 11.90352 5.11396 2153.62588 2.328 0.020023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Risk.c C1 C2 C3
## Risk.c -0.004
## C1 -0.002 -0.181
## C2 0.003 -0.140 0.310
## C3 -0.007 0.110 -0.802 -0.447
## C4 -0.014 0.072 -0.625 -0.486 0.914
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.21,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.50 | 0.68 | 59.16 – 61.83 | 88.81 | <0.001 |
| Risk c | -0.39 | 0.02 | -0.42 – -0.36 | -24.27 | <0.001 |
| C1 | -8.22 | 1.40 | -10.96 – -5.49 | -5.89 | <0.001 |
| C2 | -5.54 | 1.26 | -8.02 – -3.07 | -4.40 | <0.001 |
| C3 | 18.64 | 5.58 | 7.70 – 29.59 | 3.34 | 0.001 |
| C4 | 11.90 | 5.11 | 1.88 – 21.93 | 2.33 | 0.020 |
| Random Effects | |||||
| σ2 | 345.41 | ||||
| τ00 id | 349.04 | ||||
| ICC | 0.50 | ||||
| N id | 1003 | ||||
| Observations | 2993 | ||||
| Marginal R2 / Conditional R2 | 0.174 / 0.589 | ||||
Q.3: 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.22 <- lmer(Ben ~ Naturalness.c + Risk.c + C1 + C2 + C3 + C4 + C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientsummary(modA.22)## 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: 27234.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1424 -0.4417 0.0652 0.5272 2.9894
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 337.0 18.36
## Residual 329.9 18.16
## Number of obs: 2992, groups: id, 1003
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 60.42651 0.66853 922.60144 90.387 < 2e-16 ***
## Naturalness.c 0.21982 0.01919 2361.07915 11.457 < 2e-16 ***
## Risk.c -0.31828 0.01716 2895.70102 -18.551 < 2e-16 ***
## C1 -11.24980 1.38937 2194.18204 -8.097 9.24e-16 ***
## C2 -4.80194 1.23461 2145.50514 -3.889 0.000104 ***
## C3 40.93243 5.79242 2174.94107 7.067 2.13e-12 ***
## C4 30.73463 5.26265 2172.05996 5.840 6.00e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ntrln. Risk.c C1 C2 C3
## Naturlnss.c -0.007
## Risk.c -0.006 0.385
## C1 0.000 -0.188 -0.236
## C2 0.003 0.056 -0.108 0.293
## C3 -0.009 0.335 0.225 -0.805 -0.402
## C4 -0.015 0.313 0.184 -0.642 -0.443 0.923
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficienttab_model(modA.22,
show.stat = T, show.se = T)| Ben | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 60.43 | 0.67 | 59.12 – 61.74 | 90.39 | <0.001 |
| Naturalness c | 0.22 | 0.02 | 0.18 – 0.26 | 11.46 | <0.001 |
| Risk c | -0.32 | 0.02 | -0.35 – -0.28 | -18.55 | <0.001 |
| C1 | -11.25 | 1.39 | -13.97 – -8.53 | -8.10 | <0.001 |
| C2 | -4.80 | 1.23 | -7.22 – -2.38 | -3.89 | <0.001 |
| C3 | 40.93 | 5.79 | 29.57 – 52.29 | 7.07 | <0.001 |
| C4 | 30.73 | 5.26 | 20.42 – 41.05 | 5.84 | <0.001 |
| Random Effects | |||||
| σ2 | 329.87 | ||||
| τ00 id | 337.03 | ||||
| ICC | 0.51 | ||||
| N id | 1003 | ||||
| Observations | 2992 | ||||
| Marginal R2 / Conditional R2 | 0.195 / 0.602 | ||||
Moderators on Support
Aversion to Tampering with Nature
Q.1 How does aversion to tampering with nature predict support, over and above burger contrasts?
modA.23 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 28119.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5128 -0.4050 0.0451 0.4304 3.3989
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 456.2 21.36
## Residual 420.2 20.50
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.53627 0.77702 998.82631 72.760 < 2e-16 ***
## ATNS_Score.c 0.02218 0.04484 1005.86143 0.495 0.620997
## C1 -13.29432 1.52319 2264.76549 -8.728 < 2e-16 ***
## C2 -9.07446 1.38582 2262.54109 -6.548 7.19e-11 ***
## C3 30.38283 6.06821 2209.02964 5.007 5.97e-07 ***
## C4 20.15322 5.68351 2285.28068 3.546 0.000399 ***
## ATNS_Score.c:C1 -0.49293 0.08839 2278.33242 -5.577 2.74e-08 ***
## ATNS_Score.c:C2 -0.35861 0.08182 2289.20340 -4.383 1.22e-05 ***
## ATNS_Score.c:C3 1.14786 0.34987 2210.44756 3.281 0.001051 **
## ATNS_Score.c:C4 1.04743 0.32830 2303.39259 3.190 0.001440 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 ATNS_S.:C1 ATNS_S.:C2
## ATNS_Scor.c 0.001
## C1 -0.005 -0.013
## C2 -0.023 -0.005 0.292
## C3 0.008 0.017 -0.811 -0.428
## C4 0.012 0.021 -0.624 -0.482 0.910
## ATNS_Sc.:C1 -0.013 -0.009 -0.030 0.023 0.029 0.026
## ATNS_Sc.:C2 -0.005 -0.018 0.024 0.035 -0.029 -0.031 0.301
## ATNS_Sc.:C3 0.018 0.014 0.029 -0.030 -0.018 -0.017 -0.805 -0.432
## ATNS_Sc.:C4 0.021 0.018 0.027 -0.032 -0.017 -0.018 -0.610 -0.490
## ATNS_S.:C3
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3
## ATNS_Sc.:C4 0.906
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.23,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.54 | 0.78 | 55.01 – 58.06 | 72.76 | <0.001 |
| ATNS Score c | 0.02 | 0.04 | -0.07 – 0.11 | 0.49 | 0.621 |
| C1 | -13.29 | 1.52 | -16.28 – -10.31 | -8.73 | <0.001 |
| C2 | -9.07 | 1.39 | -11.79 – -6.36 | -6.55 | <0.001 |
| C3 | 30.38 | 6.07 | 18.48 – 42.28 | 5.01 | <0.001 |
| C4 | 20.15 | 5.68 | 9.01 – 31.30 | 3.55 | <0.001 |
| ATNS Score c × C1 | -0.49 | 0.09 | -0.67 – -0.32 | -5.58 | <0.001 |
| ATNS Score c × C2 | -0.36 | 0.08 | -0.52 – -0.20 | -4.38 | <0.001 |
| ATNS Score c × C3 | 1.15 | 0.35 | 0.46 – 1.83 | 3.28 | 0.001 |
| ATNS Score c × C4 | 1.05 | 0.33 | 0.40 – 1.69 | 3.19 | 0.001 |
| Random Effects | |||||
| σ2 | 420.20 | ||||
| τ00 id | 456.20 | ||||
| ICC | 0.52 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.033 / 0.536 | ||||
Q.2 Does aversion to tampering with nature depend on naturalness in predicting support, over and above burger contrasts?
modA.24 <- lmer(Support ~ ATNS_Score.c + Naturalness.c + C1 + C2 + C3 + C4 + C5 + Naturalness.c*ATNS_Score.c + ATNS_Score.c*C1 + ATNS_Score.c*C2 + ATNS_Score.c*C3 + ATNS_Score.c*C4 + ATNS_Score.c*C5 + (1|id), data = L)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.24)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ ATNS_Score.c + Naturalness.c + C1 + C2 + C3 + C4 +
## C5 + Naturalness.c * ATNS_Score.c + 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: 25130
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8146 -0.4356 0.0525 0.4816 3.0701
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 413.8 20.34
## Residual 399.2 19.98
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.682e+01 7.552e-01 1.009e+03 75.238 < 2e-16
## ATNS_Score.c 3.854e-02 4.351e-02 1.009e+03 0.886 0.37594
## Naturalness.c 3.698e-01 2.160e-02 2.132e+03 17.117 < 2e-16
## C1 -1.631e+01 1.501e+00 1.951e+03 -10.867 < 2e-16
## C2 -6.253e+00 1.363e+00 1.929e+03 -4.589 4.75e-06
## C3 6.248e+01 6.277e+00 1.911e+03 9.954 < 2e-16
## C4 5.091e+01 5.803e+00 1.968e+03 8.774 < 2e-16
## ATNS_Score.c:Naturalness.c 2.865e-03 1.044e-03 2.135e+03 2.744 0.00611
## ATNS_Score.c:C1 -5.269e-01 8.657e-02 1.960e+03 -6.086 1.39e-09
## ATNS_Score.c:C2 -2.247e-01 8.111e-02 1.953e+03 -2.770 0.00566
## ATNS_Score.c:C3 1.664e+00 3.542e-01 1.904e+03 4.698 2.81e-06
## ATNS_Score.c:C4 1.688e+00 3.288e-01 1.983e+03 5.134 3.12e-07
##
## (Intercept) ***
## ATNS_Score.c
## Naturalness.c ***
## C1 ***
## C2 ***
## C3 ***
## C4 ***
## ATNS_Score.c:Naturalness.c **
## ATNS_Score.c:C1 ***
## ATNS_Score.c:C2 **
## ATNS_Score.c:C3 ***
## ATNS_Score.c:C4 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. Ntrln. C1 C2 C3 C4 ATNS_S.:N
## ATNS_Scor.c 0.003
## Naturlnss.c 0.017 0.017
## C1 -0.008 -0.013 -0.106
## C2 -0.016 -0.004 0.098 0.275
## C3 -0.014 0.018 0.279 -0.792 -0.371
## C4 0.014 0.021 0.282 -0.625 -0.423 0.907
## ATNS_Sc.:N. 0.004 -0.030 -0.187 -0.006 0.039 0.006 0.017
## ATNS_Sc.:C1 -0.012 -0.011 -0.031 -0.027 0.022 0.020 0.018 0.020
## ATNS_Sc.:C2 -0.002 -0.017 0.036 0.016 0.048 -0.007 -0.007 0.158
## ATNS_Sc.:C3 0.017 -0.015 0.030 0.019 -0.013 0.006 0.008 0.204
## ATNS_Sc.:C4 0.021 0.010 0.044 0.016 -0.013 0.010 0.011 0.206
## ATNS_S.:C1 ATNS_S.:C2 ATNS_S.:C3
## ATNS_Scor.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## ATNS_Sc.:N.
## ATNS_Sc.:C1
## ATNS_Sc.:C2 0.298
## ATNS_Sc.:C3 -0.774 -0.375
## ATNS_Sc.:C4 -0.592 -0.429 0.902
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.24,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.82 | 0.76 | 55.34 – 58.30 | 75.24 | <0.001 |
| ATNS Score c | 0.04 | 0.04 | -0.05 – 0.12 | 0.89 | 0.376 |
| Naturalness c | 0.37 | 0.02 | 0.33 – 0.41 | 17.12 | <0.001 |
| C1 | -16.31 | 1.50 | -19.26 – -13.37 | -10.87 | <0.001 |
| C2 | -6.25 | 1.36 | -8.93 – -3.58 | -4.59 | <0.001 |
| C3 | 62.48 | 6.28 | 50.17 – 74.78 | 9.95 | <0.001 |
| C4 | 50.91 | 5.80 | 39.54 – 62.29 | 8.77 | <0.001 |
|
ATNS Score c × Naturalness c |
0.00 | 0.00 | 0.00 – 0.00 | 2.74 | 0.006 |
| ATNS Score c × C1 | -0.53 | 0.09 | -0.70 – -0.36 | -6.09 | <0.001 |
| ATNS Score c × C2 | -0.22 | 0.08 | -0.38 – -0.07 | -2.77 | 0.006 |
| ATNS Score c × C3 | 1.66 | 0.35 | 0.97 – 2.36 | 4.70 | <0.001 |
| ATNS Score c × C4 | 1.69 | 0.33 | 1.04 – 2.33 | 5.13 | <0.001 |
| Random Effects | |||||
| σ2 | 399.16 | ||||
| τ00 id | 413.78 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.108 / 0.562 | ||||
Q.3 Does aversion to tampering with nature depend on naturalness in predicting support, over and above burger contrasts?
modA.117 <- 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.117)## 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: 26944.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1449 -0.6563 -0.0494 0.6097 3.5515
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 4.624 2.15
## Residual 459.307 21.43
## Number of obs: 3002, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 50.35170 0.39754 997.99665 126.659 < 2e-16 ***
## ATNS_Score.c -0.01980 0.02292 999.58911 -0.864 0.387675
## C1 7.92463 1.46501 2743.21203 5.409 6.87e-08 ***
## C2 -8.06670 1.33422 2717.93557 -6.046 1.69e-09 ***
## C3 -88.16781 5.94525 2700.58563 -14.830 < 2e-16 ***
## C4 -78.62007 5.44128 2734.54651 -14.449 < 2e-16 ***
## ATNS_Score.c:C1 0.11682 0.08472 2755.43037 1.379 0.168010
## ATNS_Score.c:C2 -0.22852 0.07834 2754.69723 -2.917 0.003563 **
## ATNS_Score.c:C3 -1.20353 0.34247 2690.57895 -3.514 0.000448 ***
## ATNS_Score.c:C4 -1.29729 0.31318 2753.69554 -4.142 3.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ATNS_Sc. C1 C2 C3 C4 ATNS_S.:C1 ATNS_S.:C2
## ATNS_Scor.c -0.002
## C1 -0.002 -0.019
## C2 0.005 -0.005 0.303
## C3 -0.014 0.028 -0.804 -0.445
## C4 -0.026 0.033 -0.633 -0.486 0.915
## ATNS_Sc.:C1 -0.019 -0.015 -0.034 0.010 0.028 0.028
## ATNS_Sc.:C2 -0.005 0.014 0.010 0.031 -0.019 -0.020 0.308
## ATNS_Sc.:C3 0.028 0.007 0.028 -0.019 -0.015 -0.016 -0.801 -0.446
## ATNS_Sc.:C4 0.033 -0.013 0.029 -0.021 -0.016 -0.017 -0.623 -0.488
## ATNS_S.:C3
## ATNS_Scor.c
## C1
## C2
## C3
## C4
## ATNS_Sc.:C1
## ATNS_Sc.:C2
## ATNS_Sc.:C3
## ATNS_Sc.:C4 0.914
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.117,
show.stat = T, show.se = T)| Naturalness | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 50.35 | 0.40 | 49.57 – 51.13 | 126.66 | <0.001 |
| ATNS Score c | -0.02 | 0.02 | -0.06 – 0.03 | -0.86 | 0.388 |
| C1 | 7.92 | 1.47 | 5.05 – 10.80 | 5.41 | <0.001 |
| C2 | -8.07 | 1.33 | -10.68 – -5.45 | -6.05 | <0.001 |
| C3 | -88.17 | 5.95 | -99.82 – -76.51 | -14.83 | <0.001 |
| C4 | -78.62 | 5.44 | -89.29 – -67.95 | -14.45 | <0.001 |
| ATNS Score c × C1 | 0.12 | 0.08 | -0.05 – 0.28 | 1.38 | 0.168 |
| ATNS Score c × C2 | -0.23 | 0.08 | -0.38 – -0.07 | -2.92 | 0.004 |
| ATNS Score c × C3 | -1.20 | 0.34 | -1.88 – -0.53 | -3.51 | <0.001 |
| ATNS Score c × C4 | -1.30 | 0.31 | -1.91 – -0.68 | -4.14 | <0.001 |
| Random Effects | |||||
| σ2 | 459.31 | ||||
| τ00 id | 4.62 | ||||
| ICC | 0.01 | ||||
| N id | 1002 | ||||
| Observations | 3002 | ||||
| Marginal R2 / Conditional R2 | 0.173 / 0.182 | ||||
Animal Welfare
Q.1 How does concern for animal welfare predict support, over and above burger contrasts?
modA.25 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.25)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 28057.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5234 -0.4111 0.0609 0.4508 3.2627
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 407.0 20.17
## Residual 427.5 20.68
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.51064 0.74643 993.32834 75.708 < 2e-16 ***
## AW_Score.c 0.26547 0.03052 1003.88125 8.700 < 2e-16 ***
## C1 -13.43483 1.52949 2285.19364 -8.784 < 2e-16 ***
## C2 -9.02521 1.39446 2282.82163 -6.472 1.18e-10 ***
## C3 31.69406 6.10135 2224.40286 5.195 2.24e-07 ***
## C4 21.45645 5.70887 2306.97761 3.758 0.000175 ***
## AW_Score.c:C1 -0.21777 0.06284 2308.56469 -3.465 0.000539 ***
## AW_Score.c:C2 -0.12778 0.05743 2301.89339 -2.225 0.026169 *
## AW_Score.c:C3 0.74000 0.24914 2233.50101 2.970 0.003008 **
## AW_Score.c:C4 0.22440 0.23189 2303.46368 0.968 0.333283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. C1 C2 C3 C4 AW_S.:C1 AW_S.:C2
## AW_Score.c 0.002
## C1 -0.006 -0.004
## C2 -0.023 -0.008 0.293
## C3 0.009 0.011 -0.810 -0.430
## C4 0.013 0.011 -0.624 -0.484 0.910
## AW_Scr.c:C1 -0.004 -0.010 0.001 0.021 -0.004 -0.007
## AW_Scr.c:C2 -0.008 -0.023 0.021 0.047 -0.035 -0.036 0.291
## AW_Scr.c:C3 0.010 0.012 -0.004 -0.034 0.017 0.022 -0.809 -0.424
## AW_Scr.c:C4 0.011 0.017 -0.007 -0.036 0.022 0.025 -0.617 -0.481
## AW_S.:C3
## AW_Score.c
## C1
## C2
## C3
## C4
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3
## AW_Scr.c:C4 0.906
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.25,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.51 | 0.75 | 55.05 – 57.97 | 75.71 | <0.001 |
| AW Score c | 0.27 | 0.03 | 0.21 – 0.33 | 8.70 | <0.001 |
| C1 | -13.43 | 1.53 | -16.43 – -10.44 | -8.78 | <0.001 |
| C2 | -9.03 | 1.39 | -11.76 – -6.29 | -6.47 | <0.001 |
| C3 | 31.69 | 6.10 | 19.73 – 43.66 | 5.19 | <0.001 |
| C4 | 21.46 | 5.71 | 10.26 – 32.65 | 3.76 | <0.001 |
| AW Score c × C1 | -0.22 | 0.06 | -0.34 – -0.09 | -3.47 | 0.001 |
| AW Score c × C2 | -0.13 | 0.06 | -0.24 – -0.02 | -2.23 | 0.026 |
| AW Score c × C3 | 0.74 | 0.25 | 0.25 – 1.23 | 2.97 | 0.003 |
| AW Score c × C4 | 0.22 | 0.23 | -0.23 – 0.68 | 0.97 | 0.333 |
| Random Effects | |||||
| σ2 | 427.47 | ||||
| τ00 id | 406.96 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.073 / 0.525 | ||||
Q.2 Does animal welfare depend on naturalness in predicting support, over and above burger contrasts?
modA.26 <- lmer(Support ~ AW_Score.c + Naturalness.c + 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.26)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ AW_Score.c + Naturalness.c + 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: 25089.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7152 -0.4262 0.0652 0.4952 3.0894
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 366.4 19.14
## Residual 409.9 20.25
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.676e+01 7.262e-01 1.008e+03 78.165 < 2e-16 ***
## AW_Score.c 2.359e-01 2.970e-02 1.020e+03 7.941 5.26e-15 ***
## Naturalness.c 3.680e-01 2.156e-02 2.189e+03 17.068 < 2e-16 ***
## C1 -1.647e+01 1.514e+00 1.977e+03 -10.877 < 2e-16 ***
## C2 -6.381e+00 1.377e+00 1.951e+03 -4.634 3.82e-06 ***
## C3 6.306e+01 6.342e+00 1.936e+03 9.942 < 2e-16 ***
## C4 5.160e+01 5.854e+00 1.998e+03 8.814 < 2e-16 ***
## AW_Score.c:Naturalness.c 3.391e-04 7.942e-04 2.260e+03 0.427 0.6694
## AW_Score.c:C1 -2.452e-01 6.196e-02 1.993e+03 -3.957 7.85e-05 ***
## AW_Score.c:C2 -7.894e-02 5.663e-02 1.972e+03 -1.394 0.1635
## AW_Score.c:C3 1.049e+00 2.597e-01 1.940e+03 4.039 5.57e-05 ***
## AW_Score.c:C4 4.793e-01 2.364e-01 1.988e+03 2.027 0.0428 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) AW_Sc. Ntrln. C1 C2 C3 C4 AW_S.:N AW_S.:C1
## AW_Score.c -0.002
## Naturlnss.c 0.017 -0.011
## C1 -0.009 -0.003 -0.113
## C2 -0.017 -0.007 0.102 0.276
## C3 -0.015 0.009 0.288 -0.791 -0.374
## C4 0.014 0.006 0.289 -0.626 -0.425 0.907
## AW_Scr.c:N. -0.012 -0.006 -0.153 0.002 0.038 -0.026 0.005
## AW_Scr.c:C1 -0.003 -0.008 -0.002 0.002 0.017 -0.005 -0.011 -0.078
## AW_Scr.c:C2 -0.005 -0.020 0.052 0.014 0.056 -0.017 -0.015 0.075 0.282
## AW_Scr.c:C3 0.009 -0.021 -0.028 -0.005 -0.020 0.010 0.026 0.294 -0.783
## AW_Scr.c:C4 0.007 0.011 0.010 -0.011 -0.018 0.027 0.038 0.268 -0.613
## AW_S.:C2 AW_S.:C3
## AW_Score.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## AW_Scr.c:N.
## AW_Scr.c:C1
## AW_Scr.c:C2
## AW_Scr.c:C3 -0.376
## AW_Scr.c:C4 -0.433 0.902
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.26,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.76 | 0.73 | 55.34 – 58.18 | 78.17 | <0.001 |
| AW Score c | 0.24 | 0.03 | 0.18 – 0.29 | 7.94 | <0.001 |
| Naturalness c | 0.37 | 0.02 | 0.33 – 0.41 | 17.07 | <0.001 |
| C1 | -16.47 | 1.51 | -19.44 – -13.50 | -10.88 | <0.001 |
| C2 | -6.38 | 1.38 | -9.08 – -3.68 | -4.63 | <0.001 |
| C3 | 63.06 | 6.34 | 50.62 – 75.49 | 9.94 | <0.001 |
| C4 | 51.60 | 5.85 | 40.12 – 63.08 | 8.81 | <0.001 |
|
AW Score c × Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.43 | 0.669 |
| AW Score c × C1 | -0.25 | 0.06 | -0.37 – -0.12 | -3.96 | <0.001 |
| AW Score c × C2 | -0.08 | 0.06 | -0.19 – 0.03 | -1.39 | 0.163 |
| AW Score c × C3 | 1.05 | 0.26 | 0.54 – 1.56 | 4.04 | <0.001 |
| AW Score c × C4 | 0.48 | 0.24 | 0.02 – 0.94 | 2.03 | 0.043 |
| Random Effects | |||||
| σ2 | 409.90 | ||||
| τ00 id | 366.45 | ||||
| ICC | 0.47 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.144 / 0.548 | ||||
Connectedness to Nature
Q.1 How does connectedness to nature predict support, over and above burger contrasts?
modA.27 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.27)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 28151.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5274 -0.4186 0.0541 0.4378 3.5355
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 447.3 21.15
## Residual 429.7 20.73
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.4739 0.7734 1000.0222 73.024 < 2e-16 ***
## CNS_Score.c 0.1539 0.0622 998.2892 2.474 0.013516 *
## C1 -13.3662 1.5368 2273.3804 -8.697 < 2e-16 ***
## C2 -8.8013 1.4008 2270.6087 -6.283 3.97e-10 ***
## C3 30.5829 6.1235 2216.6196 4.994 6.36e-07 ***
## C4 20.2565 5.7330 2295.4178 3.533 0.000418 ***
## CNS_Score.c:C1 -0.2999 0.1211 2277.4845 -2.475 0.013382 *
## CNS_Score.c:C2 -0.2132 0.1166 2279.9119 -1.828 0.067670 .
## CNS_Score.c:C3 1.0484 0.4820 2215.4439 2.175 0.029717 *
## CNS_Score.c:C4 0.4354 0.4487 2288.9172 0.970 0.331946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. C1 C2 C3 C4 CNS_S.:C1 CNS_S.:C2
## CNS_Score.c -0.003
## C1 -0.005 0.006
## C2 -0.022 0.016 0.293
## C3 0.009 -0.005 -0.810 -0.428
## C4 0.013 -0.009 -0.623 -0.483 0.909
## CNS_Scr.:C1 0.006 -0.021 0.003 0.008 0.007 0.009
## CNS_Scr.:C2 0.015 -0.006 0.006 0.024 0.015 0.018 0.325
## CNS_Scr.:C3 -0.005 0.032 0.007 0.014 -0.021 -0.022 -0.798 -0.440
## CNS_Scr.:C4 -0.008 0.036 0.010 0.017 -0.022 -0.025 -0.602 -0.492
## CNS_S.:C3
## CNS_Score.c
## C1
## C2
## C3
## C4
## CNS_Scr.:C1
## CNS_Scr.:C2
## CNS_Scr.:C3
## CNS_Scr.:C4 0.906
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.27,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.47 | 0.77 | 54.96 – 57.99 | 73.02 | <0.001 |
| CNS Score c | 0.15 | 0.06 | 0.03 – 0.28 | 2.47 | 0.013 |
| C1 | -13.37 | 1.54 | -16.38 – -10.35 | -8.70 | <0.001 |
| C2 | -8.80 | 1.40 | -11.55 – -6.05 | -6.28 | <0.001 |
| C3 | 30.58 | 6.12 | 18.58 – 42.59 | 4.99 | <0.001 |
| C4 | 20.26 | 5.73 | 9.02 – 31.50 | 3.53 | <0.001 |
| CNS Score c × C1 | -0.30 | 0.12 | -0.54 – -0.06 | -2.48 | 0.013 |
| CNS Score c × C2 | -0.21 | 0.12 | -0.44 – 0.02 | -1.83 | 0.068 |
| CNS Score c × C3 | 1.05 | 0.48 | 0.10 – 1.99 | 2.18 | 0.030 |
| CNS Score c × C4 | 0.44 | 0.45 | -0.44 – 1.32 | 0.97 | 0.332 |
| Random Effects | |||||
| σ2 | 429.70 | ||||
| τ00 id | 447.35 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.029 / 0.524 | ||||
Q.2 Does connectedness to nature depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.28 <- lmer(Support ~ CNS_Score.c + Naturalness.c + 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.28)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CNS_Score.c + Naturalness.c + 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: 25156.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7021 -0.4437 0.0492 0.4784 3.1728
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 400.7 20.02
## Residual 410.2 20.25
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.670e+01 7.495e-01 1.013e+03 75.642 < 2e-16 ***
## CNS_Score.c 8.825e-02 6.033e-02 1.014e+03 1.463 0.143839
## Naturalness.c 3.622e-01 2.190e-02 2.173e+03 16.542 < 2e-16 ***
## C1 -1.621e+01 1.520e+00 1.966e+03 -10.662 < 2e-16 ***
## C2 -5.983e+00 1.380e+00 1.939e+03 -4.337 1.52e-05 ***
## C3 6.142e+01 6.364e+00 1.928e+03 9.651 < 2e-16 ***
## C4 5.003e+01 5.879e+00 1.987e+03 8.510 < 2e-16 ***
## CNS_Score.c:Naturalness.c 4.556e-03 1.420e-03 2.127e+03 3.209 0.001352 **
## CNS_Score.c:C1 -2.167e-01 1.190e-01 1.966e+03 -1.822 0.068682 .
## CNS_Score.c:C2 -7.662e-02 1.160e-01 1.964e+03 -0.661 0.508868
## CNS_Score.c:C3 1.728e+00 4.956e-01 1.915e+03 3.488 0.000498 ***
## CNS_Score.c:C4 9.285e-01 4.530e-01 1.966e+03 2.050 0.040530 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CNS_Sc. Ntrln. C1 C2 C3 C4 CNS_S.:N
## CNS_Score.c -0.005
## Naturlnss.c 0.022 -0.016
## C1 -0.010 0.007 -0.123
## C2 -0.015 0.014 0.105 0.276
## C3 -0.013 -0.008 0.294 -0.792 -0.372
## C4 0.016 -0.014 0.299 -0.626 -0.423 0.908
## CNS_Scr.:N. -0.023 -0.033 -0.238 0.054 0.008 -0.065 -0.060
## CNS_Scr.:C1 0.007 -0.020 0.034 -0.001 0.012 0.017 0.019 -0.013
## CNS_Scr.:C2 0.013 -0.008 -0.005 0.012 0.029 0.013 0.018 0.170
## CNS_Scr.:C3 -0.007 -0.010 -0.050 0.019 0.017 -0.035 -0.032 0.243
## CNS_Scr.:C4 -0.014 0.021 -0.045 0.021 0.021 -0.033 -0.035 0.246
## CNS_S.:C1 CNS_S.:C2 CNS_S.:C3
## CNS_Score.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## CNS_Scr.:N.
## CNS_Scr.:C1
## CNS_Scr.:C2 0.318
## CNS_Scr.:C3 -0.765 -0.372
## CNS_Scr.:C4 -0.586 -0.424 0.901
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.28,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.70 | 0.75 | 55.23 – 58.17 | 75.64 | <0.001 |
| CNS Score c | 0.09 | 0.06 | -0.03 – 0.21 | 1.46 | 0.144 |
| Naturalness c | 0.36 | 0.02 | 0.32 – 0.41 | 16.54 | <0.001 |
| C1 | -16.21 | 1.52 | -19.19 – -13.23 | -10.66 | <0.001 |
| C2 | -5.98 | 1.38 | -8.69 – -3.28 | -4.34 | <0.001 |
| C3 | 61.42 | 6.36 | 48.94 – 73.89 | 9.65 | <0.001 |
| C4 | 50.03 | 5.88 | 38.51 – 61.56 | 8.51 | <0.001 |
|
CNS Score c × Naturalness c |
0.00 | 0.00 | 0.00 – 0.01 | 3.21 | 0.001 |
| CNS Score c × C1 | -0.22 | 0.12 | -0.45 – 0.02 | -1.82 | 0.069 |
| CNS Score c × C2 | -0.08 | 0.12 | -0.30 – 0.15 | -0.66 | 0.509 |
| CNS Score c × C3 | 1.73 | 0.50 | 0.76 – 2.70 | 3.49 | <0.001 |
| CNS Score c × C4 | 0.93 | 0.45 | 0.04 – 1.82 | 2.05 | 0.040 |
| Random Effects | |||||
| σ2 | 410.18 | ||||
| τ00 id | 400.70 | ||||
| ICC | 0.49 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.104 / 0.547 | ||||
Climate Change Belief
Q.1 How does climate change belief predict support, over and above burger contrasts?
modA.29 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.29)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Support ~ 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: 28036.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5488 -0.4103 0.0682 0.4447 3.3408
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 391.5 19.79
## Residual 428.9 20.71
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.48089 0.73635 995.20362 76.704 < 2e-16 ***
## CCBelief_Score.c 0.31881 0.03156 990.43833 10.103 < 2e-16 ***
## C1 -13.45161 1.53065 2292.79616 -8.788 < 2e-16 ***
## C2 -8.99792 1.39489 2290.77203 -6.451 1.35e-10 ***
## C3 31.96045 6.10662 2231.75452 5.234 1.82e-07 ***
## C4 21.67007 5.71335 2317.70975 3.793 0.000153 ***
## CCBelief_Score.c:C1 -0.11128 0.06623 2301.83365 -1.680 0.093072 .
## CCBelief_Score.c:C2 -0.14863 0.06042 2305.68186 -2.460 0.013966 *
## CCBelief_Score.c:C3 0.47072 0.25930 2246.34100 1.815 0.069610 .
## CCBelief_Score.c:C4 0.05288 0.24027 2312.73701 0.220 0.825818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. C1 C2 C3 C4 CCB_S.:C1 CCB_S.:C2
## CCBlf_Scr.c 0.006
## C1 -0.005 -0.004
## C2 -0.022 -0.013 0.293
## C3 0.008 0.008 -0.810 -0.429
## C4 0.012 0.013 -0.624 -0.483 0.910
## CCBlf_S.:C1 -0.004 -0.016 -0.005 0.008 -0.002 -0.006
## CCBlf_S.:C2 -0.014 -0.002 0.008 0.025 -0.029 -0.034 0.302
## CCBlf_S.:C3 0.008 0.014 -0.002 -0.029 0.020 0.025 -0.812 -0.415
## CCBlf_S.:C4 0.013 0.009 -0.006 -0.034 0.025 0.033 -0.616 -0.467
## CCB_S.:C3
## CCBlf_Scr.c
## C1
## C2
## C3
## C4
## CCBlf_S.:C1
## CCBlf_S.:C2
## CCBlf_S.:C3
## CCBlf_S.:C4 0.906
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.29,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.48 | 0.74 | 55.04 – 57.92 | 76.70 | <0.001 |
| CCBelief Score c | 0.32 | 0.03 | 0.26 – 0.38 | 10.10 | <0.001 |
| C1 | -13.45 | 1.53 | -16.45 – -10.45 | -8.79 | <0.001 |
| C2 | -9.00 | 1.39 | -11.73 – -6.26 | -6.45 | <0.001 |
| C3 | 31.96 | 6.11 | 19.99 – 43.93 | 5.23 | <0.001 |
| C4 | 21.67 | 5.71 | 10.47 – 32.87 | 3.79 | <0.001 |
| CCBelief Score c × C1 | -0.11 | 0.07 | -0.24 – 0.02 | -1.68 | 0.093 |
| CCBelief Score c × C2 | -0.15 | 0.06 | -0.27 – -0.03 | -2.46 | 0.014 |
| CCBelief Score c × C3 | 0.47 | 0.26 | -0.04 – 0.98 | 1.82 | 0.070 |
| CCBelief Score c × C4 | 0.05 | 0.24 | -0.42 – 0.52 | 0.22 | 0.826 |
| Random Effects | |||||
| σ2 | 428.91 | ||||
| τ00 id | 391.53 | ||||
| ICC | 0.48 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.087 / 0.523 | ||||
Q.2 Does climate change belief depend on perception sof naturalness in predicting support, over and above burger contrasts?
modA.30 <- lmer(Support ~ CCBelief_Score.c + Naturalness.c + 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.30)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ CCBelief_Score.c + Naturalness.c + 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: 25059.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7334 -0.4297 0.0647 0.5044 3.0859
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 352.6 18.78
## Residual 411.1 20.28
## Number of obs: 2694, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.672e+01 7.170e-01 1.009e+03 79.100
## CCBelief_Score.c 2.860e-01 3.068e-02 9.982e+02 9.322
## Naturalness.c 3.681e-01 2.169e-02 2.201e+03 16.972
## C1 -1.655e+01 1.515e+00 1.984e+03 -10.926
## C2 -6.414e+00 1.376e+00 1.956e+03 -4.662
## C3 6.394e+01 6.347e+00 1.945e+03 10.073
## C4 5.234e+01 5.868e+00 2.012e+03 8.920
## CCBelief_Score.c:Naturalness.c 6.810e-04 8.502e-04 2.278e+03 0.801
## CCBelief_Score.c:C1 -1.434e-01 6.521e-02 1.986e+03 -2.198
## CCBelief_Score.c:C2 -1.119e-01 5.954e-02 1.974e+03 -1.879
## CCBelief_Score.c:C3 9.834e-01 2.672e-01 1.925e+03 3.681
## CCBelief_Score.c:C4 5.081e-01 2.426e-01 1.970e+03 2.094
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## CCBelief_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 3.34e-06 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## CCBelief_Score.c:Naturalness.c 0.423215
## CCBelief_Score.c:C1 0.028033 *
## CCBelief_Score.c:C2 0.060416 .
## CCBelief_Score.c:C3 0.000239 ***
## CCBelief_Score.c:C4 0.036363 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) CCBl_S. Ntrln. C1 C2 C3 C4 CCB_S.:N
## CCBlf_Scr.c 0.004
## Naturlnss.c 0.022 -0.019
## C1 -0.008 -0.001 -0.112
## C2 -0.016 -0.015 0.101 0.276
## C3 -0.016 0.002 0.284 -0.792 -0.373
## C4 0.013 0.006 0.285 -0.626 -0.424 0.908
## CCBlf_S.:N. -0.021 0.004 -0.175 -0.009 0.016 0.009 0.038
## CCBlf_S.:C1 -0.002 -0.013 -0.015 -0.002 0.006 -0.010 -0.015 -0.051
## CCBlf_S.:C2 -0.014 0.002 0.027 0.003 0.030 -0.014 -0.016 0.085
## CCBlf_S.:C3 0.003 -0.016 0.017 -0.011 -0.016 0.038 0.050 0.250
## CCBlf_S.:C4 0.009 0.003 0.058 -0.019 -0.019 0.053 0.067 0.219
## CCB_S.:C1 CCB_S.:C2 CCB_S.:C3
## CCBlf_Scr.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## CCBlf_S.:N.
## CCBlf_S.:C1
## CCBlf_S.:C2 0.295
## CCBlf_S.:C3 -0.788 -0.370
## CCBlf_S.:C4 -0.610 -0.425 0.901
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.30,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.72 | 0.72 | 55.31 – 58.12 | 79.10 | <0.001 |
| CCBelief Score c | 0.29 | 0.03 | 0.23 – 0.35 | 9.32 | <0.001 |
| Naturalness c | 0.37 | 0.02 | 0.33 – 0.41 | 16.97 | <0.001 |
| C1 | -16.55 | 1.51 | -19.52 – -13.58 | -10.93 | <0.001 |
| C2 | -6.41 | 1.38 | -9.11 – -3.72 | -4.66 | <0.001 |
| C3 | 63.94 | 6.35 | 51.49 – 76.38 | 10.07 | <0.001 |
| C4 | 52.34 | 5.87 | 40.84 – 63.85 | 8.92 | <0.001 |
|
CCBelief Score c × Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 0.80 | 0.423 |
| CCBelief Score c × C1 | -0.14 | 0.07 | -0.27 – -0.02 | -2.20 | 0.028 |
| CCBelief Score c × C2 | -0.11 | 0.06 | -0.23 – 0.00 | -1.88 | 0.060 |
| CCBelief Score c × C3 | 0.98 | 0.27 | 0.46 – 1.51 | 3.68 | <0.001 |
| CCBelief Score c × C4 | 0.51 | 0.24 | 0.03 – 0.98 | 2.09 | 0.036 |
| Random Effects | |||||
| σ2 | 411.08 | ||||
| τ00 id | 352.62 | ||||
| ICC | 0.46 | ||||
| N id | 1001 | ||||
| Observations | 2694 | ||||
| Marginal R2 / Conditional R2 | 0.157 / 0.546 | ||||
Disgust Sensitivity
Q.1 How does sensitivity to disgust predict support, over and above burger contrasts?
modA.31 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.31)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 28142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5711 -0.4217 0.0507 0.4374 3.3671
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 450.9 21.23
## Residual 429.4 20.72
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.47786 0.77585 998.42323 72.795 < 2e-16 ***
## DS_Score.c 0.03671 0.03743 1012.51446 0.981 0.326910
## C1 -13.34128 1.53854 2274.16935 -8.671 < 2e-16 ***
## C2 -8.90906 1.39967 2267.15915 -6.365 2.35e-10 ***
## C3 30.92662 6.12765 2215.33487 5.047 4.85e-07 ***
## C4 20.67222 5.73483 2291.78138 3.605 0.000319 ***
## DS_Score.c:C1 -0.17393 0.07379 2288.14050 -2.357 0.018506 *
## DS_Score.c:C2 -0.21122 0.06851 2287.34060 -3.083 0.002074 **
## DS_Score.c:C3 0.41542 0.29907 2221.10227 1.389 0.164966
## DS_Score.c:C4 0.35129 0.28006 2295.91595 1.254 0.209857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. C1 C2 C3 C4 DS_S.:C1 DS_S.:C2
## DS_Score.c -0.001
## C1 -0.006 -0.003
## C2 -0.023 -0.001 0.292
## C3 0.009 0.009 -0.810 -0.428
## C4 0.013 0.011 -0.624 -0.483 0.909
## DS_Scr.c:C1 -0.003 -0.015 -0.042 0.019 0.028 0.016
## DS_Scr.c:C2 -0.001 -0.046 0.019 0.022 -0.020 -0.019 0.295
## DS_Scr.c:C3 0.008 0.023 0.027 -0.019 -0.014 -0.004 -0.805 -0.459
## DS_Scr.c:C4 0.011 0.035 0.014 -0.019 -0.004 0.003 -0.618 -0.522
## DS_S.:C3
## DS_Score.c
## C1
## C2
## C3
## C4
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3
## DS_Scr.c:C4 0.909
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.31,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.48 | 0.78 | 54.96 – 58.00 | 72.79 | <0.001 |
| DS Score c | 0.04 | 0.04 | -0.04 – 0.11 | 0.98 | 0.327 |
| C1 | -13.34 | 1.54 | -16.36 – -10.32 | -8.67 | <0.001 |
| C2 | -8.91 | 1.40 | -11.65 – -6.16 | -6.37 | <0.001 |
| C3 | 30.93 | 6.13 | 18.91 – 42.94 | 5.05 | <0.001 |
| C4 | 20.67 | 5.73 | 9.43 – 31.92 | 3.60 | <0.001 |
| DS Score c × C1 | -0.17 | 0.07 | -0.32 – -0.03 | -2.36 | 0.018 |
| DS Score c × C2 | -0.21 | 0.07 | -0.35 – -0.08 | -3.08 | 0.002 |
| DS Score c × C3 | 0.42 | 0.30 | -0.17 – 1.00 | 1.39 | 0.165 |
| DS Score c × C4 | 0.35 | 0.28 | -0.20 – 0.90 | 1.25 | 0.210 |
| Random Effects | |||||
| σ2 | 429.42 | ||||
| τ00 id | 450.85 | ||||
| ICC | 0.51 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.025 / 0.525 | ||||
Q.2 Does sensitivity to disgust depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.32 <- lmer(Support ~ DS_Score.c + Naturalness.c + 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.32)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ DS_Score.c + Naturalness.c + 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: 25166.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7812 -0.4468 0.0502 0.4815 2.9716
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 401.9 20.05
## Residual 412.6 20.31
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.671e+01 7.509e-01 1.010e+03 75.517 < 2e-16 ***
## DS_Score.c 4.959e-02 3.624e-02 1.025e+03 1.368 0.1715
## Naturalness.c 3.812e-01 2.136e-02 2.142e+03 17.843 < 2e-16 ***
## C1 -1.644e+01 1.524e+00 1.967e+03 -10.786 < 2e-16 ***
## C2 -6.319e+00 1.384e+00 1.941e+03 -4.566 5.27e-06 ***
## C3 6.352e+01 6.373e+00 1.924e+03 9.967 < 2e-16 ***
## C4 5.166e+01 5.887e+00 1.983e+03 8.777 < 2e-16 ***
## DS_Score.c:Naturalness.c -1.448e-03 9.082e-04 2.125e+03 -1.594 0.1110
## DS_Score.c:C1 -1.772e-01 7.262e-02 1.977e+03 -2.440 0.0148 *
## DS_Score.c:C2 -1.537e-01 6.771e-02 1.956e+03 -2.270 0.0233 *
## DS_Score.c:C3 1.437e-01 3.100e-01 1.927e+03 0.464 0.6429
## DS_Score.c:C4 2.013e-01 2.862e-01 1.975e+03 0.704 0.4818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) DS_Sc. Ntrln. C1 C2 C3 C4 DS_S.:N DS_S.:C1
## DS_Score.c 0.000
## Naturlnss.c 0.016 0.013
## C1 -0.009 -0.004 -0.112
## C2 -0.016 0.000 0.105 0.274
## C3 -0.015 0.012 0.287 -0.792 -0.373
## C4 0.014 0.015 0.293 -0.625 -0.425 0.907
## DS_Scr.c:N. 0.011 -0.024 -0.047 -0.006 0.048 -0.020 -0.010
## DS_Scr.c:C1 -0.003 -0.014 -0.006 -0.041 0.018 0.026 0.014 -0.011
## DS_Scr.c:C2 0.002 -0.044 0.056 0.012 0.035 -0.005 -0.003 0.091 0.291
## DS_Scr.c:C3 0.010 -0.013 -0.028 0.026 -0.009 -0.022 -0.010 0.267 -0.766
## DS_Scr.c:C4 0.013 0.023 -0.016 0.013 -0.007 -0.008 0.001 0.274 -0.596
## DS_S.:C2 DS_S.:C3
## DS_Score.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## DS_Scr.c:N.
## DS_Scr.c:C1
## DS_Scr.c:C2
## DS_Scr.c:C3 -0.414
## DS_Scr.c:C4 -0.473 0.906
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.32,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.71 | 0.75 | 55.24 – 58.18 | 75.52 | <0.001 |
| DS Score c | 0.05 | 0.04 | -0.02 – 0.12 | 1.37 | 0.171 |
| Naturalness c | 0.38 | 0.02 | 0.34 – 0.42 | 17.84 | <0.001 |
| C1 | -16.44 | 1.52 | -19.43 – -13.45 | -10.79 | <0.001 |
| C2 | -6.32 | 1.38 | -9.03 – -3.61 | -4.57 | <0.001 |
| C3 | 63.52 | 6.37 | 51.02 – 76.01 | 9.97 | <0.001 |
| C4 | 51.66 | 5.89 | 40.12 – 63.21 | 8.78 | <0.001 |
|
DS Score c × Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.59 | 0.111 |
| DS Score c × C1 | -0.18 | 0.07 | -0.32 – -0.03 | -2.44 | 0.015 |
| DS Score c × C2 | -0.15 | 0.07 | -0.29 – -0.02 | -2.27 | 0.023 |
| DS Score c × C3 | 0.14 | 0.31 | -0.46 – 0.75 | 0.46 | 0.643 |
| DS Score c × C4 | 0.20 | 0.29 | -0.36 – 0.76 | 0.70 | 0.482 |
| Random Effects | |||||
| σ2 | 412.65 | ||||
| τ00 id | 401.92 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.100 / 0.544 | ||||
Collectivism
Q.1 How does collectivism predict support, over and above burger contrasts?
modA.33 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.33)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Support ~ 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: 28096
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4248 -0.4227 0.0511 0.4310 3.4426
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 415.5 20.38
## Residual 428.6 20.70
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.52874 0.75212 1001.82712 75.160 < 2e-16 ***
## Collectivism_Score.c 0.30444 0.03684 1004.39521 8.264 4.41e-16 ***
## C1 -13.36646 1.53220 2288.29036 -8.724 < 2e-16 ***
## C2 -8.86268 1.39533 2285.32713 -6.352 2.56e-10 ***
## C3 31.03290 6.10881 2228.96298 5.080 4.09e-07 ***
## C4 20.99592 5.71606 2310.60531 3.673 0.000245 ***
## Collectivism_Score.c:C1 -0.19818 0.07576 2296.57087 -2.616 0.008957 **
## Collectivism_Score.c:C2 -0.11978 0.07004 2312.51320 -1.710 0.087355 .
## Collectivism_Score.c:C3 0.38451 0.29873 2232.52701 1.287 0.198181
## Collectivism_Score.c:C4 0.37023 0.27781 2305.20156 1.333 0.182768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. C1 C2 C3 C4 C_S.:C1 C_S.:C2 C_S.:C3
## Cllctvsm_S. 0.005
## C1 -0.006 -0.004
## C2 -0.023 -0.009 0.292
## C3 0.009 0.008 -0.810 -0.429
## C4 0.012 0.013 -0.624 -0.483 0.910
## Cllct_S.:C1 -0.004 -0.023 -0.015 0.012 0.008 0.004
## Cllct_S.:C2 -0.009 -0.018 0.012 0.016 -0.026 -0.028 0.295
## Cllct_S.:C3 0.008 0.027 0.008 -0.026 0.008 0.011 -0.806 -0.429
## Cllct_S.:C4 0.013 0.023 0.003 -0.028 0.012 0.016 -0.605 -0.488 0.905
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.33,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.53 | 0.75 | 55.05 – 58.00 | 75.16 | <0.001 |
| Collectivism Score c | 0.30 | 0.04 | 0.23 – 0.38 | 8.26 | <0.001 |
| C1 | -13.37 | 1.53 | -16.37 – -10.36 | -8.72 | <0.001 |
| C2 | -8.86 | 1.40 | -11.60 – -6.13 | -6.35 | <0.001 |
| C3 | 31.03 | 6.11 | 19.06 – 43.01 | 5.08 | <0.001 |
| C4 | 21.00 | 5.72 | 9.79 – 32.20 | 3.67 | <0.001 |
| Collectivism Score c × C1 | -0.20 | 0.08 | -0.35 – -0.05 | -2.62 | 0.009 |
| Collectivism Score c × C2 | -0.12 | 0.07 | -0.26 – 0.02 | -1.71 | 0.087 |
| Collectivism Score c × C3 | 0.38 | 0.30 | -0.20 – 0.97 | 1.29 | 0.198 |
| Collectivism Score c × C4 | 0.37 | 0.28 | -0.17 – 0.91 | 1.33 | 0.183 |
| Random Effects | |||||
| σ2 | 428.60 | ||||
| τ00 id | 415.51 | ||||
| ICC | 0.49 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.067 / 0.526 | ||||
Q.2 Does collectivism depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.34 <- lmer(Support ~ Collectivism_Score.c + Naturalness.c + 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.34)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Support ~ Collectivism_Score.c + Naturalness.c + 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: 25098.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9643 -0.4214 0.0630 0.4809 3.2117
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 366.4 19.14
## Residual 410.1 20.25
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.678e+01 7.259e-01 1.014e+03 78.217
## Collectivism_Score.c 3.074e-01 3.551e-02 1.011e+03 8.658
## Naturalness.c 3.904e-01 2.133e-02 2.165e+03 18.307
## C1 -1.640e+01 1.518e+00 1.986e+03 -10.803
## C2 -6.181e+00 1.374e+00 1.955e+03 -4.498
## C3 6.371e+01 6.344e+00 1.942e+03 10.043
## C4 5.229e+01 5.858e+00 2.004e+03 8.927
## Collectivism_Score.c:Naturalness.c -1.115e-03 9.080e-04 2.109e+03 -1.229
## Collectivism_Score.c:C1 -3.274e-01 7.482e-02 1.993e+03 -4.376
## Collectivism_Score.c:C2 -1.062e-01 6.905e-02 1.973e+03 -1.537
## Collectivism_Score.c:C3 7.319e-01 3.078e-01 1.934e+03 2.378
## Collectivism_Score.c:C4 6.506e-01 2.839e-01 1.990e+03 2.292
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Collectivism_Score.c < 2e-16 ***
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 7.27e-06 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## Collectivism_Score.c:Naturalness.c 0.2194
## Collectivism_Score.c:C1 1.27e-05 ***
## Collectivism_Score.c:C2 0.1243
## Collectivism_Score.c:C3 0.0175 *
## Collectivism_Score.c:C4 0.0220 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Cll_S. Ntrln. C1 C2 C3 C4 C_S.:N C_S.:C1
## Cllctvsm_S. 0.004
## Naturlnss.c 0.017 -0.002
## C1 -0.009 -0.007 -0.106
## C2 -0.016 -0.009 0.105 0.275
## C3 -0.016 0.009 0.281 -0.792 -0.374
## C4 0.014 0.014 0.288 -0.626 -0.425 0.908
## Cllct_S.:N. -0.002 0.032 -0.088 -0.074 0.001 0.049 0.041
## Cllct_S.:C1 -0.006 -0.022 -0.090 -0.001 0.002 -0.020 -0.025 -0.048
## Cllct_S.:C2 -0.009 -0.009 0.005 0.003 0.018 -0.014 -0.017 0.114 0.284
## Cllct_S.:C3 0.009 0.005 0.062 -0.018 -0.015 0.042 0.044 0.246 -0.784
## Cllct_S.:C4 0.013 0.026 0.048 -0.025 -0.018 0.045 0.046 0.278 -0.596
## C_S.:C2 C_S.:C3
## Cllctvsm_S.
## Naturlnss.c
## C1
## C2
## C3
## C4
## Cllct_S.:N.
## Cllct_S.:C1
## Cllct_S.:C2
## Cllct_S.:C3 -0.378
## Cllct_S.:C4 -0.428 0.902
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.34,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.78 | 0.73 | 55.35 – 58.20 | 78.22 | <0.001 |
| Collectivism Score c | 0.31 | 0.04 | 0.24 – 0.38 | 8.66 | <0.001 |
| Naturalness c | 0.39 | 0.02 | 0.35 – 0.43 | 18.31 | <0.001 |
| C1 | -16.40 | 1.52 | -19.38 – -13.43 | -10.80 | <0.001 |
| C2 | -6.18 | 1.37 | -8.88 – -3.49 | -4.50 | <0.001 |
| C3 | 63.71 | 6.34 | 51.27 – 76.15 | 10.04 | <0.001 |
| C4 | 52.29 | 5.86 | 40.81 – 63.78 | 8.93 | <0.001 |
|
Collectivism Score c × Naturalness c |
-0.00 | 0.00 | -0.00 – 0.00 | -1.23 | 0.219 |
| Collectivism Score c × C1 | -0.33 | 0.07 | -0.47 – -0.18 | -4.38 | <0.001 |
| Collectivism Score c × C2 | -0.11 | 0.07 | -0.24 – 0.03 | -1.54 | 0.124 |
| Collectivism Score c × C3 | 0.73 | 0.31 | 0.13 – 1.34 | 2.38 | 0.017 |
| Collectivism Score c × C4 | 0.65 | 0.28 | 0.09 – 1.21 | 2.29 | 0.022 |
| Random Effects | |||||
| σ2 | 410.08 | ||||
| τ00 id | 366.44 | ||||
| ICC | 0.47 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.143 / 0.547 | ||||
Individualism
Q.1 How does individualism predict support, over and above burger contrasts?
modA.35 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.35)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Support ~ 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: 28123.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5498 -0.4178 0.0586 0.4235 3.3111
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 436.4 20.89
## Residual 430.9 20.76
## Number of obs: 3005, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.51446 0.76674 998.86051 73.708 < 2e-16 ***
## Individualism_Score.c 0.20009 0.04279 1007.87004 4.676 3.33e-06 ***
## C1 -13.37535 1.53837 2277.46222 -8.695 < 2e-16 ***
## C2 -8.82186 1.40069 2273.62254 -6.298 3.60e-10 ***
## C3 30.82665 6.13154 2218.88976 5.028 5.37e-07 ***
## C4 20.35931 5.73924 2298.20649 3.547 0.000397 ***
## Individualism_Score.c:C1 -0.28012 0.08708 2293.02961 -3.217 0.001314 **
## Individualism_Score.c:C2 -0.02137 0.07921 2308.45517 -0.270 0.787330
## Individualism_Score.c:C3 0.65955 0.34725 2226.49952 1.899 0.057648 .
## Individualism_Score.c:C4 0.41349 0.32429 2297.62398 1.275 0.202407
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. C1 C2 C3 C4 I_S.:C1 I_S.:C2 I_S.:C3
## Indvdlsm_S. 0.002
## C1 -0.006 -0.010
## C2 -0.023 -0.002 0.292
## C3 0.009 0.012 -0.810 -0.429
## C4 0.012 0.012 -0.623 -0.483 0.909
## Indvd_S.:C1 -0.010 -0.017 -0.008 0.014 0.012 0.017
## Indvd_S.:C2 -0.002 -0.040 0.013 0.020 -0.018 -0.016 0.287
## Indvd_S.:C3 0.012 0.019 0.012 -0.018 -0.006 -0.006 -0.810 -0.444
## Indvd_S.:C4 0.012 0.020 0.016 -0.016 -0.006 -0.005 -0.617 -0.505 0.908
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.35,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.51 | 0.77 | 55.01 – 58.02 | 73.71 | <0.001 |
| Individualism Score c | 0.20 | 0.04 | 0.12 – 0.28 | 4.68 | <0.001 |
| C1 | -13.38 | 1.54 | -16.39 – -10.36 | -8.69 | <0.001 |
| C2 | -8.82 | 1.40 | -11.57 – -6.08 | -6.30 | <0.001 |
| C3 | 30.83 | 6.13 | 18.80 – 42.85 | 5.03 | <0.001 |
| C4 | 20.36 | 5.74 | 9.11 – 31.61 | 3.55 | <0.001 |
|
Individualism Score c × C1 |
-0.28 | 0.09 | -0.45 – -0.11 | -3.22 | 0.001 |
|
Individualism Score c × C2 |
-0.02 | 0.08 | -0.18 – 0.13 | -0.27 | 0.787 |
|
Individualism Score c × C3 |
0.66 | 0.35 | -0.02 – 1.34 | 1.90 | 0.058 |
|
Individualism Score c × C4 |
0.41 | 0.32 | -0.22 – 1.05 | 1.28 | 0.202 |
| Random Effects | |||||
| σ2 | 430.87 | ||||
| τ00 id | 436.35 | ||||
| ICC | 0.50 | ||||
| N id | 1001 | ||||
| Observations | 3005 | ||||
| Marginal R2 / Conditional R2 | 0.038 / 0.522 | ||||
Q.2 Does individualism depend on perceptions of naturalness in predicting support, over and above burger contrasts?
modA.36 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescalingsummary(modA.36)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 25139.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6250 -0.4256 0.0641 0.4853 3.1107
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 390.9 19.77
## Residual 411.5 20.29
## Number of obs: 2695, groups: id, 1001
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.676e+01 7.433e-01 1.011e+03 76.365
## Individualism_Score.c 1.905e-01 4.140e-02 1.013e+03 4.601
## Naturalness.c 3.788e-01 2.261e-02 2.205e+03 16.753
## C1 -1.650e+01 1.520e+00 1.969e+03 -10.855
## C2 -6.102e+00 1.379e+00 1.942e+03 -4.424
## C3 6.398e+01 6.360e+00 1.928e+03 10.059
## C4 5.206e+01 5.878e+00 1.989e+03 8.857
## Individualism_Score.c:Naturalness.c 1.656e-03 1.143e-03 2.242e+03 1.449
## Individualism_Score.c:C1 -3.512e-01 8.558e-02 1.977e+03 -4.103
## Individualism_Score.c:C2 3.123e-02 7.781e-02 1.971e+03 0.401
## Individualism_Score.c:C3 1.409e+00 3.525e-01 1.913e+03 3.998
## Individualism_Score.c:C4 1.144e+00 3.252e-01 1.963e+03 3.518
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Individualism_Score.c 4.73e-06 ***
## Naturalness.c < 2e-16 ***
## C1 < 2e-16 ***
## C2 1.02e-05 ***
## C3 < 2e-16 ***
## C4 < 2e-16 ***
## Individualism_Score.c:Naturalness.c 0.147562
## Individualism_Score.c:C1 4.24e-05 ***
## Individualism_Score.c:C2 0.688189
## Individualism_Score.c:C3 6.64e-05 ***
## Individualism_Score.c:C4 0.000444 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ind_S. Ntrln. C1 C2 C3 C4 I_S.:N I_S.:C1
## Indvdlsm_S. 0.002
## Naturlnss.c 0.021 -0.008
## C1 -0.009 -0.007 -0.105
## C2 -0.016 -0.001 0.102 0.276
## C3 -0.014 0.008 0.275 -0.791 -0.373
## C4 0.015 0.008 0.279 -0.625 -0.424 0.907
## Indvd_S.:N. -0.013 0.004 -0.315 -0.003 -0.001 -0.003 0.003
## Indvd_S.:C1 -0.008 -0.017 -0.024 -0.003 0.011 -0.001 0.004 -0.045
## Indvd_S.:C2 -0.001 -0.034 0.005 0.011 0.024 -0.009 -0.006 0.079 0.280
## Indvd_S.:C3 0.009 -0.007 0.032 -0.001 -0.009 0.024 0.024 0.189 -0.792
## Indvd_S.:C4 0.009 0.016 0.040 0.004 -0.004 0.025 0.027 0.192 -0.613
## I_S.:C2 I_S.:C3
## Indvdlsm_S.
## Naturlnss.c
## C1
## C2
## C3
## C4
## Indvd_S.:N.
## Indvd_S.:C1
## Indvd_S.:C2
## Indvd_S.:C3 -0.411
## Indvd_S.:C4 -0.468 0.903
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## Some predictor variables are on very different scales: consider rescalingtab_model(modA.36,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.76 | 0.74 | 55.30 – 58.22 | 76.37 | <0.001 |
| Individualism Score c | 0.19 | 0.04 | 0.11 – 0.27 | 4.60 | <0.001 |
| Naturalness c | 0.38 | 0.02 | 0.33 – 0.42 | 16.75 | <0.001 |
| C1 | -16.50 | 1.52 | -19.48 – -13.52 | -10.85 | <0.001 |
| C2 | -6.10 | 1.38 | -8.81 – -3.40 | -4.42 | <0.001 |
| C3 | 63.98 | 6.36 | 51.50 – 76.45 | 10.06 | <0.001 |
| C4 | 52.06 | 5.88 | 40.54 – 63.59 | 8.86 | <0.001 |
|
Individualism Score c × Naturalness c |
0.00 | 0.00 | -0.00 – 0.00 | 1.45 | 0.148 |
|
Individualism Score c × C1 |
-0.35 | 0.09 | -0.52 – -0.18 | -4.10 | <0.001 |
|
Individualism Score c × C2 |
0.03 | 0.08 | -0.12 – 0.18 | 0.40 | 0.688 |
|
Individualism Score c × C3 |
1.41 | 0.35 | 0.72 – 2.10 | 4.00 | <0.001 |
|
Individualism Score c × C4 |
1.14 | 0.33 | 0.51 – 1.78 | 3.52 | <0.001 |
| Random Effects | |||||
| σ2 | 411.49 | ||||
| τ00 id | 390.94 | ||||
| ICC | 0.49 | ||||
| N id | 1001 | ||||
| Observations | 2695 | ||||
| Marginal R2 / Conditional R2 | 0.114 / 0.545 | ||||
Political Ideology
Q.1 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.37 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.37)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 28139.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5578 -0.4210 0.0542 0.4282 3.3556
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 447.6 21.16
## Residual 432.0 20.78
## Number of obs: 3007, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.491 0.774 999.687 72.983 < 2e-16 ***
## Ideology.c -1.488 0.711 992.398 -2.093 0.036571 *
## C1 -13.317 1.541 2273.744 -8.644 < 2e-16 ***
## C2 -8.745 1.403 2271.016 -6.231 5.50e-10 ***
## C3 30.847 6.140 2216.644 5.024 5.46e-07 ***
## C4 20.437 5.748 2295.261 3.556 0.000385 ***
## Ideology.c:C1 -1.688 1.426 2244.083 -1.184 0.236705
## Ideology.c:C2 -1.283 1.261 2256.539 -1.017 0.309049
## Ideology.c:C3 7.054 5.584 2191.732 1.263 0.206666
## Ideology.c:C4 5.890 5.251 2299.910 1.122 0.262165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. C1 C2 C3 C4 Id.:C1 Id.:C2 Id.:C3
## Ideology.c 0.002
## C1 -0.005 -0.006
## C2 -0.022 -0.012 0.292
## C3 0.009 0.007 -0.810 -0.429
## C4 0.012 0.011 -0.623 -0.483 0.909
## Idelgy.c:C1 -0.006 -0.007 -0.011 -0.004 0.005 0.005
## Idelgy.c:C2 -0.012 -0.015 -0.004 -0.006 -0.015 -0.017 0.277
## Idelgy.c:C3 0.007 0.001 0.004 -0.015 0.009 0.008 -0.809 -0.418
## Idelgy.c:C4 0.012 -0.002 0.005 -0.017 0.008 0.009 -0.606 -0.476 0.903
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.37,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.49 | 0.77 | 54.97 – 58.01 | 72.98 | <0.001 |
| Ideology c | -1.49 | 0.71 | -2.88 – -0.09 | -2.09 | 0.036 |
| C1 | -13.32 | 1.54 | -16.34 – -10.30 | -8.64 | <0.001 |
| C2 | -8.75 | 1.40 | -11.50 – -5.99 | -6.23 | <0.001 |
| C3 | 30.85 | 6.14 | 18.81 – 42.88 | 5.02 | <0.001 |
| C4 | 20.44 | 5.75 | 9.17 – 31.71 | 3.56 | <0.001 |
| Ideology c × C1 | -1.69 | 1.43 | -4.48 – 1.11 | -1.18 | 0.237 |
| Ideology c × C2 | -1.28 | 1.26 | -3.75 – 1.19 | -1.02 | 0.309 |
| Ideology c × C3 | 7.05 | 5.58 | -3.90 – 18.00 | 1.26 | 0.207 |
| Ideology c × C4 | 5.89 | 5.25 | -4.41 – 16.19 | 1.12 | 0.262 |
| Random Effects | |||||
| σ2 | 432.00 | ||||
| τ00 id | 447.65 | ||||
| ICC | 0.51 | ||||
| N id | 1002 | ||||
| Observations | 3007 | ||||
| Marginal R2 / Conditional R2 | 0.025 / 0.521 | ||||
Q.2 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.38 <- lmer(Support ~ 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)## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientssummary(modA.38)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Support ~ 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: 25148.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7094 -0.4418 0.0444 0.4833 3.1677
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 398.5 19.96
## Residual 415.5 20.38
## Number of obs: 2696, groups: id, 1002
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 56.70661 0.74931 1012.07761 75.679 < 2e-16 ***
## Ideology.c -1.90144 0.68968 1011.29151 -2.757 0.00594 **
## Naturalness.c 0.37913 0.02155 2147.57729 17.594 < 2e-16 ***
## C1 -16.45467 1.53086 1969.63716 -10.749 < 2e-16 ***
## C2 -6.08895 1.38629 1940.90224 -4.392 1.18e-05 ***
## C3 63.43831 6.40322 1928.89338 9.907 < 2e-16 ***
## C4 51.47130 5.91721 1988.84802 8.699 < 2e-16 ***
## Ideology.c:Naturalness.c 0.01296 0.02194 2133.44545 0.591 0.55483
## Ideology.c:C1 -0.73017 1.44268 1951.46505 -0.506 0.61283
## Ideology.c:C2 -0.92520 1.24226 1932.68968 -0.745 0.45650
## Ideology.c:C3 7.85738 5.86504 1907.58178 1.340 0.18050
## Ideology.c:C4 5.44894 5.47209 1991.84756 0.996 0.31948
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Idlgy. Ntrln. C1 C2 C3 C4 Id.:N. Id.:C1
## Ideology.c 0.001
## Naturlnss.c 0.018 -0.029
## C1 -0.010 0.001 -0.123
## C2 -0.016 -0.013 0.104 0.276
## C3 -0.014 -0.004 0.294 -0.793 -0.373
## C4 0.015 0.000 0.300 -0.627 -0.425 0.908
## Idlgy.c:Nt. -0.022 0.067 -0.134 0.066 0.006 -0.062 -0.065
## Idelgy.c:C1 0.000 -0.026 0.073 -0.031 -0.001 0.031 0.033 -0.229
## Idelgy.c:C2 -0.014 -0.005 0.002 0.000 -0.005 -0.016 -0.018 0.086 0.249
## Idelgy.c:C3 -0.001 -0.003 -0.061 0.028 -0.014 -0.016 -0.019 0.322 -0.808
## Idelgy.c:C4 0.003 0.019 -0.064 0.029 -0.017 -0.019 -0.020 0.334 -0.632
## Id.:C2 Id.:C3
## Ideology.c
## Naturlnss.c
## C1
## C2
## C3
## C4
## Idlgy.c:Nt.
## Idelgy.c:C1
## Idelgy.c:C2
## Idelgy.c:C3 -0.363
## Idelgy.c:C4 -0.414 0.904
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientstab_model(modA.38,
show.stat = T, show.se = T)| Support | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | std. Error | CI | Statistic | p |
| (Intercept) | 56.71 | 0.75 | 55.24 – 58.18 | 75.68 | <0.001 |
| Ideology c | -1.90 | 0.69 | -3.25 – -0.55 | -2.76 | 0.006 |
| Naturalness c | 0.38 | 0.02 | 0.34 – 0.42 | 17.59 | <0.001 |
| C1 | -16.45 | 1.53 | -19.46 – -13.45 | -10.75 | <0.001 |
| C2 | -6.09 | 1.39 | -8.81 – -3.37 | -4.39 | <0.001 |
| C3 | 63.44 | 6.40 | 50.88 – 75.99 | 9.91 | <0.001 |
| C4 | 51.47 | 5.92 | 39.87 – 63.07 | 8.70 | <0.001 |
|
Ideology c × Naturalness c |
0.01 | 0.02 | -0.03 – 0.06 | 0.59 | 0.555 |
| Ideology c × C1 | -0.73 | 1.44 | -3.56 – 2.10 | -0.51 | 0.613 |
| Ideology c × C2 | -0.93 | 1.24 | -3.36 – 1.51 | -0.74 | 0.456 |
| Ideology c × C3 | 7.86 | 5.87 | -3.64 – 19.36 | 1.34 | 0.180 |
| Ideology c × C4 | 5.45 | 5.47 | -5.28 – 16.18 | 1.00 | 0.319 |
| Random Effects | |||||
| σ2 | 415.54 | ||||
| τ00 id | 398.48 | ||||
| ICC | 0.49 | ||||
| N id | 1002 | ||||
| Observations | 2696 | ||||
| Marginal R2 / Conditional R2 | 0.101 / 0.541 | ||||