EFA & Preliminary Analysis
Heather
6/9/2020
library(dplyr)
library(tidyr)
library(purrr)
library(DT)
surv <- read.csv(file="career_imp.csv", header=T)
surv$Litho <- trimws(surv$Litho, which=c("both"))
# calculate selected factors
surv$engpc <- (surv$Q3Eng_m + surv$Q3Eng_n + surv$Q3Eng_k)/3
surv$engint <- (surv$Q3Eng_i + surv$Q3Eng_h + surv$Q3Eng_j)/3
surv$engrec <- (surv$Q3Eng_f + surv$Q3Eng_e + surv$Q3Eng_d + surv$Q3Eng_g)/4
surv$belong1 <- (surv$Q4a + surv$Q4b + surv$Q4c + surv$Q4g + surv$Q4h)/5
surv$belong2 <- (surv$Q4e + surv$Q4f)/2
surv$engbel <- (surv$Q5d + surv$Q5h + surv$Q5g + surv$Q5e + surv$Q5b)/5
surv$engemp <- (surv$Q5a + surv$Q5c + surv$Q5f)/3
# subset needed columns
names(surv)
surv2 <- subset(surv, select=c(1,185:205,221:228,229:237,319:327,328:335,377:383))
head(surv2)
# calculate demographics
surv2$racenum <- as.numeric(surv2$Q30a) + as.numeric(surv2$Q30b) + as.numeric(surv2$Q30c) + as.numeric(surv2$Q30d) + as.numeric(surv2$Q30e) + as.numeric(surv2$Q30f) + as.numeric(surv2$Q30g) + as.numeric(surv2$Q30h)
surv2$raceeth[surv2$racenum == 0] <- NA
surv2$raceeth[surv2$racenum == 2] <- "Biracial"
surv2$raceeth[surv2$racenum > 2] <- "Multiracial"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30a == 1] <- "Asian"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30b == 1] <- "Black/AA"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30c == 1] <- "Latinx"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30d == 1] <- "MidEast"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30e == 1] <- "NH/PI"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30f == 1] <- "NA/AN"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30g == 1] <- "White"
surv2$raceeth[surv2$racenum == 1 & surv2$Q30h == 1] <- "WriteIn"
table(surv2$raceeth)
surv2$gennum <- as.numeric(surv2$Q31a) + as.numeric(surv2$Q31b) + as.numeric(surv2$Q31c) + as.numeric(surv2$Q31d) + as.numeric(surv2$Q31e) + as.numeric(surv2$Q31f) + as.numeric(surv2$Q31g)
table(surv2$gennum)
surv2$genid[surv2$gennum == 0] <- NA
surv2$genid[surv2$gennum > 1] <- "Multiple Options Selected"
surv2$genid[surv2$gennum == 1 & surv2$Q31a == 1] <- "Female"
surv2$genid[surv2$gennum == 1 & surv2$Q31b == 1] <- "Male"
surv2$genid[surv2$gennum == 1 & surv2$Q31c == 1] <- "Agender"
surv2$genid[surv2$gennum == 1 & surv2$Q31d == 1] <- "Genderqueer"
surv2$genid[surv2$gennum == 1 & surv2$Q31e == 1] <- "Cisgender"
surv2$genid[surv2$gennum == 1 & surv2$Q31f == 1] <- "Transgender"
surv2$genid[surv2$gennum == 1 & surv2$Q31g == 1] <- "Not Listed"
table(surv2$genid, useNA = "always")
names(surv2)
# write.csv(surv2, file="multi.csv", row.names = F)
temp <- read.csv(file="multi.csv", header=T)
gender <- cbind.data.frame(temp$Litho, temp$genid, temp$genid2)
# career priorities
colnames(surv2)[2:39] <- c("cmn1","cmn2","cmn3","cmn4","cmn5","cmn6","cmn7","cmn8","cmn9","cmn10","cmn11","cmn12","cmn13","cmn14","cmn15","cmn16","cmn17","cmn18","cmn19","cmn20","cmn21",
"career_money",
"career_known",
"career_helping",
"career_supervising",
"career_security",
"career_people",
"career_invent",
"career_developing",
"field_academia",
"field_industry",
"field_entre",
"field_govt",
"field_k12",
"field_law",
"field_med",
"field_nonprofit",
"field_other")
head(surv2)
names(surv2)
surv3 <- subset(surv2, select=c(1:39,57:63))
head(surv3)
names(surv3)
library(psych)
table(surv3$cmn3)
recodeme <- c(1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1)
surv4 <- surv3
surv4[2:22] <- reverse.code(recodeme, surv3[2:22])
head(surv4)
data <- surv4
rm(list=setdiff(ls(), c("data","gender")))Factor Analysis
Career Vars only
Field Vars only
CMN Vars only
Final Composites
Career
Q15. How important are the following factors for your future career satisfaction?
- Finances
- Q15e Having job security and opportunity (security)
- Q15a Making money (money)
- Leadership
- Q15b Becoming well known (known)
- Q15d Supervising others (supervising)
- Q15f Working with people (people)
- Innovation
- Q15g Inventing/designing things (invent)
- Q15h Developing new knowledge and skills (developing)
- Q15c Helping others (helping)
CMN
- Thinking about your own actions, feelings and beliefs, please indicate how much you personally agree or disagree with each statement. There are no correct or wrong answers to the items. You should give the responses that most accurately describe your personal actions, feelings and beliefs. It is best if you respond with your first impression when answering.
(Reverse-coded variables recoded before factor analysis.)
- Stoic
- cmn6-Q13f = I like to talk about my feelings. *
- cmn9-Q13i = I tend to share my feelings. *
- Hetero
- cmn4-Q13d = It would be awful if someone thought I was gay.
- cmn7-Q13g = It is important to me that people think I am heterosexual.
- Risks
- cmn3-Q13c = In general, I do not like risky situations. *
- cmn18-Q13r = I enjoy taking risks.
- Help
- cmn21-Q12u = It bothers me when I have to ask for help.
- cmn17-Q13q = I never ask for help.
- Violence
- cmn8-Q13h = I believe that violence is never justified. *
- cmn12-Q13l = Sometimes violent action is necessary.
- Importance
- cmn11-Q13k = I would hate to be important. *
- cmn16-Q13p = I never do things to be an important person. *
In Progress
Several rows with same Litho number but not same values
rm(list=setdiff(ls(), c("data","gender")))
data$car_fin <- (data$career_security + data$career_money)/2
data$car_lead <- (data$career_known + data$career_supervising + data$career_people)/3
data$car_inno <- (data$career_invent + data$career_developing + data$career_helping)/3
data$cmn_stoic <- (data$cmn6 + data$cmn9)/2
data$cmn_hetero <- (data$cmn4 + data$cmn7)/2
data$cmn_risks <- (data$cmn3 + data$cmn18)/2
data$cmn_help <- (data$cmn21 + data$cmn17)/2
data$cmn_violence <- (data$cmn8 + data$cmn12)/2
data$cmn_importance <- (data$cmn11 + data$cmn16)/2
head(data)## Litho cmn1 cmn2 cmn3 cmn4 cmn5 cmn6 cmn7 cmn8 cmn9 cmn10 cmn11 cmn12 cmn13
## 1 1028 1 3 3 6 4 4 6 4 3 5 1 4 1
## 2 1030 4 2 5 6 3 1 6 1 1 3 1 5 2
## 3 1029 3 6 6 6 3 0 6 3 0 6 0 6 6
## 4 1054 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 1027 0 1 4 6 3 4 6 0 2 NA NA NA NA
## 6 1039 1 1 3 5 1 1 5 4 1 3 3 4 4
## cmn14 cmn15 cmn16 cmn17 cmn18 cmn19 cmn20 cmn21 career_money career_known
## 1 4 0 0 1 1 6 0 0 1 2
## 2 1 NA 2 4 4 2 1 4 4 4
## 3 3 0 0 0 3 6 3 0 NA NA
## 4 NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 2 0 3 1 4 6 1 2 4 3
## career_helping career_supervising career_security career_people career_invent
## 1 5 5 5 5 5
## 2 5 2 4 4 4
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 5 3 5 4 5
## career_developing field_academia field_industry field_entre field_govt
## 1 5 5 5 0 4
## 2 4 2 5 4 4
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 5 5 5 1 1
## field_k12 field_law field_med field_nonprofit field_other engpc engint
## 1 0 3 3 4 5 4.000000 4.666667
## 2 1 4 4 1 5 4.333333 4.333333
## 3 NA NA NA NA NA 6.000000 6.000000
## 4 NA NA NA NA NA 4.666667 5.333333
## 5 NA NA NA NA NA 6.000000 5.333333
## 6 1 1 4 2 3 4.666667 5.333333
## engrec belong1 belong2 engbel engemp car_fin car_lead car_inno cmn_stoic
## 1 5.00 4.4 4.5 4.6 4.666667 3.0 4.000000 5.000000 3.5
## 2 4.00 3.6 4.5 5.8 5.000000 4.0 3.333333 4.333333 1.0
## 3 3.00 5.8 6.0 6.0 6.000000 NA NA NA 0.0
## 4 5.00 4.8 5.0 5.2 5.000000 NA NA NA NA
## 5 3.25 4.8 5.5 4.6 5.333333 NA NA NA 3.0
## 6 3.75 4.6 4.0 4.6 3.666667 4.5 3.333333 5.000000 1.0
## cmn_hetero cmn_risks cmn_help cmn_violence cmn_importance
## 1 6 2.0 0.5 4.0 0.5
## 2 6 4.5 4.0 3.0 1.5
## 3 6 4.5 0.0 4.5 0.0
## 4 NA NA NA NA NA
## 5 6 NA NA NA NA
## 6 5 3.5 1.5 4.0 3.0data2 <- subset(data, Litho != 2564 & Litho != 7551 & Litho !=7713 & Litho !=7714 & Litho !=7717 & Litho !=8068 & Litho !=8090 & Litho !=6748 & Litho !=6798 & Litho !=6986 & Litho !=7111 & Litho !=7112 & Litho !=7113 & Litho !=7137 & Litho !=2360)
## Normality
d <- na.omit(subset(data2, select=c(1,40:55)))
library(psych)
norm <- describe(d)
norm## vars n mean sd median trimmed mad min max
## Litho* 1 2344 4837.64 2155.76 5264.50 4895.19 2467.79 1002 8508
## engpc 2 2344 4.43 1.20 4.67 4.53 0.99 0 6
## engint 3 2344 5.02 1.17 5.33 5.23 0.99 0 6
## engrec 4 2344 3.79 1.30 4.00 3.86 1.48 0 6
## belong1 5 2344 4.53 1.32 4.80 4.70 1.19 0 6
## belong2 6 2344 4.53 1.27 5.00 4.66 1.48 0 6
## engbel 7 2344 5.28 0.85 5.60 5.42 0.59 0 6
## engemp 8 2344 4.96 1.03 5.00 5.10 0.99 0 6
## car_fin 9 2344 4.88 1.00 5.00 5.00 0.74 0 6
## car_lead 10 2344 3.56 1.18 3.67 3.57 0.99 0 6
## car_inno 11 2344 4.80 1.01 5.00 4.91 0.99 0 6
## cmn_stoic 12 2344 2.60 1.69 2.50 2.55 2.22 0 6
## cmn_hetero 13 2344 2.75 1.87 3.00 2.70 2.22 0 6
## cmn_risks 14 2344 3.31 0.77 3.00 3.27 0.74 0 6
## cmn_help 15 2344 2.49 1.45 2.50 2.46 1.48 0 6
## cmn_violence 16 2344 3.00 0.92 3.00 3.01 0.74 0 6
## cmn_importance 17 2344 2.13 1.28 2.00 2.09 1.48 0 6
## range skew kurtosis se
## Litho* 7506 -0.25 -1.17 44.53
## engpc 6 -0.91 1.02 0.02
## engint 6 -1.62 3.00 0.02
## engrec 6 -0.49 0.04 0.03
## belong1 6 -1.07 0.96 0.03
## belong2 6 -0.90 0.84 0.03
## engbel 6 -1.40 2.29 0.02
## engemp 6 -1.22 2.04 0.02
## car_fin 6 -1.29 2.59 0.02
## car_lead 6 -0.18 0.14 0.02
## car_inno 6 -1.13 2.26 0.02
## cmn_stoic 6 0.16 -0.85 0.03
## cmn_hetero 6 0.04 -1.06 0.04
## cmn_risks 6 0.33 1.67 0.02
## cmn_help 6 0.17 -0.54 0.03
## cmn_violence 6 -0.13 1.47 0.02
## cmn_importance 6 0.27 -0.28 0.03datatable(norm) %>%
formatRound(1:13) %>%
formatStyle(11:12, color = styleInterval(c(-2, 2), c('red', 'black', 'red')))## Detect Outliers
d1 <- na.omit(subset(data2, select=c(1,40:55)))
m_dist <- mahalanobis(d1[2:17], colMeans(d1[2:17]), cov(d1[2:17]))
d1$MD <- round(m_dist, 1)
plot(d1$MD)
abline(h = 40, col = "red")
d1$outlier <- "No"
d1$outlier[d1$MD > 40] <- "Yes"
table(d1$outlier)##
## No Yes
## 2264 80d$outlier <- d1$outlier
d4 <- subset(d, outlier == "No", select=c(1:17))
head(d4)## Litho engpc engint engrec belong1 belong2 engbel engemp car_fin
## 1 1028 4.000000 4.666667 5.00 4.4 4.5 4.6 4.666667 3.0
## 2 1030 4.333333 4.333333 4.00 3.6 4.5 5.8 5.000000 4.0
## 6 1039 4.666667 5.333333 3.75 4.6 4.0 4.6 3.666667 4.5
## 7 1040 6.000000 6.000000 5.25 6.0 6.0 6.0 6.000000 6.0
## 17 1034 4.000000 6.000000 6.00 5.0 6.0 5.8 5.333333 6.0
## 19 1047 3.333333 4.333333 5.50 3.6 5.5 4.4 4.666667 0.5
## car_lead car_inno cmn_stoic cmn_hetero cmn_risks cmn_help cmn_violence
## 1 4.000000 5.000000 3.5 6.0 2.0 0.5 4.0
## 2 3.333333 4.333333 1.0 6.0 4.5 4.0 3.0
## 6 3.333333 5.000000 1.0 5.0 3.5 1.5 4.0
## 7 5.000000 5.000000 1.5 6.0 3.0 0.0 3.0
## 17 4.333333 6.000000 0.0 1.5 3.5 5.0 3.0
## 19 1.666667 4.666667 0.5 6.0 3.0 2.5 2.5
## cmn_importance
## 1 0.5
## 2 1.5
## 6 3.0
## 7 0.0
## 17 0.0
## 19 4.5allcor <- corr.test(d4[2:17], use = "pairwise", adjust = "holm")
allcor_r <- data.frame(allcor$r)
datatable(allcor_r) %>%
formatRound(1:16) %>%
formatStyle(1:16, backgroundColor = styleInterval(.75, c(none, 'yellow')))library(corrplot)
col4 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "#7FFF7F",
"cyan", "#007FFF", "blue", "#00007F"))
corrplot(allcor$r, p.mat = allcor$p, insig = "blank",
method = "color", order = "hclust", col = col4(10))
# detach("package:backports", unload=TRUE)
# remove.packages("sjPlot")
# install.packages("backports")
# library(devtools)
# devtools::install_github("strengejacke/strengejacke", force = TRUE)
# devtools::install_github("strengejacke/sjPlot")
library(strengejacke)## # Attaching packages
## <U+2714> ggeffects 0.15.0 <U+2714> sjlabelled 1.1.6
## <U+2714> sjmisc 2.8.5 <U+2714> sjstats 0.18.0
## <U+2714> sjPlot 2.8.4.9000 <U+2714> esc 0.5.1library(car)
d <- d4
d$id <- (d$engint + d$engrec + d$engpc)/3
Y <- d$belong1
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
# diagnostics
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## 1.145039 1.141477 1.056639 1.077697 1.115018
## cmn_stoic
## 1.052574sqrt(vif(fit)) > 2 # problem?## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## FALSE FALSE FALSE FALSE FALSE
## cmn_stoic
## FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7334 -0.6400 0.2090 0.9107 2.1736
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.38865 0.13743 31.933 < 2e-16 ***
## cmn_help -0.10603 0.01893 -5.600 2.40e-08 ***
## cmn_importance -0.13192 0.02146 -6.147 9.31e-10 ***
## cmn_hetero 0.04847 0.01399 3.466 0.000538 ***
## cmn_risks 0.15530 0.03539 4.389 1.19e-05 ***
## cmn_violence 0.04752 0.02969 1.601 0.109624
## cmn_stoic -0.01623 0.01561 -1.040 0.298577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 2257 degrees of freedom
## Multiple R-squared: 0.04917, Adjusted R-squared: 0.04664
## F-statistic: 19.45 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
##########
Y <- d$belong2
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
# diagnostics
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## 1.145039 1.141477 1.056639 1.077697 1.115018
## cmn_stoic
## 1.052574sqrt(vif(fit)) > 2 # problem?## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## FALSE FALSE FALSE FALSE FALSE
## cmn_stoic
## FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6976 -0.7134 0.1594 0.9298 2.4560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.41424 0.13418 32.897 < 2e-16 ***
## cmn_help -0.12715 0.01849 -6.878 7.82e-12 ***
## cmn_importance -0.11941 0.02095 -5.699 1.37e-08 ***
## cmn_hetero 0.04472 0.01366 3.275 0.00107 **
## cmn_risks 0.11010 0.03455 3.187 0.00146 **
## cmn_violence 0.07245 0.02899 2.499 0.01252 *
## cmn_stoic 0.01194 0.01524 0.784 0.43327
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.175 on 2257 degrees of freedom
## Multiple R-squared: 0.05308, Adjusted R-squared: 0.05057
## F-statistic: 21.09 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
##########
Y <- d$id
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
# diagnostics
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## 1.145039 1.141477 1.056639 1.077697 1.115018
## cmn_stoic
## 1.052574sqrt(vif(fit)) > 2 # problem?## cmn_help cmn_importance cmn_hetero cmn_risks cmn_violence
## FALSE FALSE FALSE FALSE FALSE
## cmn_stoic
## FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3844 -0.5391 0.0851 0.6391 2.0671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.23226 0.10276 41.184 < 2e-16 ***
## cmn_help -0.05746 0.01416 -4.059 5.10e-05 ***
## cmn_importance -0.12350 0.01605 -7.696 2.08e-14 ***
## cmn_hetero 0.03211 0.01046 3.070 0.00217 **
## cmn_risks 0.11714 0.02646 4.427 1.00e-05 ***
## cmn_violence 0.04727 0.02220 2.129 0.03335 *
## cmn_stoic 0.00882 0.01167 0.756 0.44990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8997 on 2257 degrees of freedom
## Multiple R-squared: 0.053, Adjusted R-squared: 0.05049
## F-statistic: 21.05 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
##########
Y <- d$engbel
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4154 -0.4042 0.2231 0.5843 1.4716
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.239766 0.090011 58.212 < 2e-16 ***
## cmn_help -0.046949 0.012400 -3.786 0.000157 ***
## cmn_importance -0.135742 0.014056 -9.657 < 2e-16 ***
## cmn_hetero -0.023310 0.009160 -2.545 0.011005 *
## cmn_risks 0.105457 0.023176 4.550 5.64e-06 ***
## cmn_violence 0.058302 0.019446 2.998 0.002747 **
## cmn_stoic -0.002147 0.010223 -0.210 0.833671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.788 on 2257 degrees of freedom
## Multiple R-squared: 0.0678, Adjusted R-squared: 0.06533
## F-statistic: 27.36 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
Y <- d$engemp
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3373 -0.5782 0.1774 0.7117 1.7208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.94424 0.10486 47.149 < 2e-16 ***
## cmn_help -0.07151 0.01445 -4.950 7.98e-07 ***
## cmn_importance -0.15889 0.01638 -9.703 < 2e-16 ***
## cmn_hetero -0.00746 0.01067 -0.699 0.4846
## cmn_risks 0.12092 0.02700 4.478 7.90e-06 ***
## cmn_violence 0.04553 0.02266 2.010 0.0446 *
## cmn_stoic 0.02304 0.01191 1.935 0.0531 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.918 on 2257 degrees of freedom
## Multiple R-squared: 0.07457, Adjusted R-squared: 0.07211
## F-statistic: 30.31 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
##########
Y <- d$belong1
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## car_fin car_inno car_lead
## 1.165696 1.295058 1.340127sqrt(vif(fit)) > 2 # problem?## car_fin car_inno car_lead
## FALSE FALSE FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7896 -0.5635 0.1918 0.7604 3.2457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57589 0.15295 10.303 < 2e-16 ***
## car_fin 0.07127 0.02660 2.679 0.00744 **
## car_inno 0.59277 0.02876 20.610 < 2e-16 ***
## car_lead -0.05701 0.02370 -2.405 0.01625 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 2260 degrees of freedom
## Multiple R-squared: 0.1933, Adjusted R-squared: 0.1922
## F-statistic: 180.5 on 3 and 2260 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $car_fin
##
## $car_inno
##
## $car_lead
Y <- d$belong2
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## car_fin car_inno car_lead
## 1.165696 1.295058 1.340127sqrt(vif(fit)) > 2 # problem?## car_fin car_inno car_lead
## FALSE FALSE FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1075 -0.6653 0.1552 0.8909 2.6937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.446587 0.156352 15.648 <2e-16 ***
## car_fin -0.009334 0.027196 -0.343 0.731
## car_inno 0.466935 0.029400 15.882 <2e-16 ***
## car_lead -0.023102 0.024232 -0.953 0.340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.132 on 2260 degrees of freedom
## Multiple R-squared: 0.1193, Adjusted R-squared: 0.1182
## F-statistic: 102.1 on 3 and 2260 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $car_fin
##
## $car_inno
##
## $car_lead
Y <- d$id
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
# diagnostics
layout(matrix(c(1,2,3,4),2,2))
plot(fit)
layout(matrix(c(1), byrow = TRUE))
# Evaluate Collinearity
vif(fit) # variance inflation factors## car_fin car_inno car_lead
## 1.165696 1.295058 1.340127sqrt(vif(fit)) > 2 # problem?## car_fin car_inno car_lead
## FALSE FALSE FALSEsummary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2209 -0.4910 0.0846 0.5642 2.8682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.25379 0.11450 19.684 <2e-16 ***
## car_fin 0.03279 0.01992 1.646 0.0998 .
## car_inno 0.45001 0.02153 20.901 <2e-16 ***
## car_lead -0.03558 0.01775 -2.005 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8291 on 2260 degrees of freedom
## Multiple R-squared: 0.1947, Adjusted R-squared: 0.1936
## F-statistic: 182.1 on 3 and 2260 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $car_fin
##
## $car_inno
##
## $car_lead
Y <- d$engbel
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4476 -0.3692 0.1582 0.4794 1.9265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.08004 0.10026 30.722 < 2e-16 ***
## car_fin 0.12566 0.01744 7.206 7.81e-13 ***
## car_inno 0.38740 0.01885 20.549 < 2e-16 ***
## car_lead -0.07918 0.01554 -5.096 3.76e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7259 on 2260 degrees of freedom
## Multiple R-squared: 0.2078, Adjusted R-squared: 0.2068
## F-statistic: 197.6 on 3 and 2260 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $car_fin
##
## $car_inno
##
## $car_lead
Y <- d$engemp
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4598 -0.4912 0.1083 0.5283 2.5703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.24104 0.11292 19.847 < 2e-16 ***
## car_fin 0.07022 0.01964 3.575 0.000357 ***
## car_inno 0.52864 0.02123 24.897 < 2e-16 ***
## car_lead -0.03963 0.01750 -2.265 0.023627 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8176 on 2260 degrees of freedom
## Multiple R-squared: 0.265, Adjusted R-squared: 0.264
## F-statistic: 271.6 on 3 and 2260 DF, p-value: < 2.2e-16plot_model(fit, type = "pred") #output## $car_fin
##
## $car_inno
##
## $car_lead
###############################################
describe(d)## vars n mean sd median trimmed mad min max
## Litho* 1 2264 4823.86 2153.81 5255.50 4880.11 2460.37 1002.00 8508
## engpc 2 2264 4.48 1.13 4.67 4.56 0.99 0.00 6
## engint 3 2264 5.09 1.05 5.33 5.27 0.99 0.00 6
## engrec 4 2264 3.84 1.25 4.00 3.89 1.48 0.00 6
## belong1 5 2264 4.59 1.23 4.80 4.74 1.19 0.00 6
## belong2 6 2264 4.58 1.21 5.00 4.69 1.48 0.00 6
## engbel 7 2264 5.29 0.82 5.60 5.42 0.59 1.00 6
## engemp 8 2264 5.01 0.95 5.00 5.12 0.99 0.67 6
## car_fin 9 2264 4.90 0.94 5.00 5.01 0.74 0.00 6
## car_lead 10 2264 3.59 1.14 3.67 3.60 0.99 0.00 6
## car_inno 11 2264 4.85 0.92 5.00 4.93 0.99 0.33 6
## cmn_stoic 12 2264 2.58 1.66 2.50 2.54 2.22 0.00 6
## cmn_hetero 13 2264 2.75 1.86 3.00 2.71 2.22 0.00 6
## cmn_risks 14 2264 3.31 0.74 3.00 3.28 0.74 0.50 6
## cmn_help 15 2264 2.47 1.43 2.50 2.45 1.48 0.00 6
## cmn_violence 16 2264 2.99 0.90 3.00 3.01 0.74 0.00 6
## cmn_importance 17 2264 2.12 1.26 2.00 2.08 1.48 0.00 6
## id 18 2264 4.47 0.92 4.58 4.52 0.95 0.00 6
## range skew kurtosis se
## Litho* 7506.00 -0.25 -1.17 45.27
## engpc 6.00 -0.73 0.50 0.02
## engint 6.00 -1.39 2.01 0.02
## engrec 6.00 -0.42 -0.01 0.03
## belong1 6.00 -0.98 0.76 0.03
## belong2 6.00 -0.77 0.48 0.03
## engbel 5.00 -1.24 1.27 0.02
## engemp 5.33 -0.91 0.52 0.02
## car_fin 6.00 -1.07 1.54 0.02
## car_lead 6.00 -0.09 -0.02 0.02
## car_inno 5.67 -0.70 0.19 0.02
## cmn_stoic 6.00 0.16 -0.84 0.03
## cmn_hetero 6.00 0.04 -1.05 0.04
## cmn_risks 5.50 0.31 1.40 0.02
## cmn_help 6.00 0.15 -0.55 0.03
## cmn_violence 6.00 -0.16 1.54 0.02
## cmn_importance 6.00 0.26 -0.31 0.03
## id 6.00 -0.69 0.71 0.02Y <- d$car_fin
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7251 -0.5084 0.1307 0.6859 1.7976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.51642 0.10568 42.737 < 2e-16 ***
## cmn_help 0.02163 0.01456 1.486 0.1374
## cmn_importance -0.12773 0.01650 -7.740 1.49e-14 ***
## cmn_hetero 0.02386 0.01075 2.219 0.0266 *
## cmn_risks 0.15081 0.02721 5.542 3.33e-08 ***
## cmn_violence 0.03441 0.02283 1.507 0.1319
## cmn_stoic -0.02653 0.01200 -2.210 0.0272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9252 on 2257 degrees of freedom
## Multiple R-squared: 0.0437, Adjusted R-squared: 0.04115
## F-statistic: 17.19 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred")## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
Y <- d$car_inno
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5013 -0.5753 0.1009 0.6696 1.7824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.562415 0.101220 45.074 < 2e-16 ***
## cmn_help -0.089168 0.013945 -6.394 1.95e-10 ***
## cmn_importance -0.100911 0.015806 -6.384 2.08e-10 ***
## cmn_hetero -0.009925 0.010301 -0.964 0.335
## cmn_risks 0.157212 0.026063 6.032 1.89e-09 ***
## cmn_violence 0.025517 0.021868 1.167 0.243
## cmn_stoic 0.058759 0.011496 5.111 3.47e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8861 on 2257 degrees of freedom
## Multiple R-squared: 0.07708, Adjusted R-squared: 0.07462
## F-statistic: 31.42 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred")## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
Y <- d$car_lead
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4881 -0.6687 -0.0074 0.6968 2.9761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.78883 0.12170 22.916 < 2e-16 ***
## cmn_help -0.02499 0.01677 -1.491 0.136
## cmn_importance -0.20497 0.01900 -10.785 < 2e-16 ***
## cmn_hetero 0.07368 0.01239 5.949 3.12e-09 ***
## cmn_risks 0.21088 0.03134 6.730 2.15e-11 ***
## cmn_violence 0.02639 0.02629 1.004 0.316
## cmn_stoic 0.12288 0.01382 8.890 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.065 on 2257 degrees of freedom
## Multiple R-squared: 0.1242, Adjusted R-squared: 0.1219
## F-statistic: 53.34 on 6 and 2257 DF, p-value: < 2.2e-16plot_model(fit, type = "pred")## $cmn_help
##
## $cmn_importance
##
## $cmn_hetero
##
## $cmn_risks
##
## $cmn_violence
##
## $cmn_stoic
Y <- d$belong1
fit <- lm(Y ~ d$car_fin + d$car_inno + d$car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ d$car_fin + d$car_inno + d$car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7896 -0.5635 0.1918 0.7604 3.2457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57589 0.15295 10.303 < 2e-16 ***
## d$car_fin 0.07127 0.02660 2.679 0.00744 **
## d$car_inno 0.59277 0.02876 20.610 < 2e-16 ***
## d$car_lead -0.05701 0.02370 -2.405 0.01625 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 2260 degrees of freedom
## Multiple R-squared: 0.1933, Adjusted R-squared: 0.1922
## F-statistic: 180.5 on 3 and 2260 DF, p-value: < 2.2e-16Y <- d$belong2
fit <- lm(Y ~ d$car_fin + d$car_inno + d$car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ d$car_fin + d$car_inno + d$car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1075 -0.6653 0.1552 0.8909 2.6937
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.446587 0.156352 15.648 <2e-16 ***
## d$car_fin -0.009334 0.027196 -0.343 0.731
## d$car_inno 0.466935 0.029400 15.882 <2e-16 ***
## d$car_lead -0.023102 0.024232 -0.953 0.340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.132 on 2260 degrees of freedom
## Multiple R-squared: 0.1193, Adjusted R-squared: 0.1182
## F-statistic: 102.1 on 3 and 2260 DF, p-value: < 2.2e-16Y <- d$id
fit <- lm(Y ~ d$car_fin + d$car_inno + d$car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ d$car_fin + d$car_inno + d$car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2209 -0.4910 0.0846 0.5642 2.8682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.25379 0.11450 19.684 <2e-16 ***
## d$car_fin 0.03279 0.01992 1.646 0.0998 .
## d$car_inno 0.45001 0.02153 20.901 <2e-16 ***
## d$car_lead -0.03558 0.01775 -2.005 0.0451 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8291 on 2260 degrees of freedom
## Multiple R-squared: 0.1947, Adjusted R-squared: 0.1936
## F-statistic: 182.1 on 3 and 2260 DF, p-value: < 2.2e-16colnames(gender) <- c("Litho","genid","genid2")
dm <- merge(d, gender, by = "Litho", all.x = T)
table(dm$genid)##
## Agender Cisgender Female
## 2 1 550
## Genderqueer Male Multiple Options Selected
## 3 1526 14
## Not Listed Transgender
## 26 3dm$genfin[dm$genid == "Male"] <- 0 #male
dm$genfin[dm$genid == "Agender" | dm$genid == "Genderqueer" | dm$genid == "Multiple Options Selected" | dm$genid == "Not Listed" | dm$genid == "Female"] <- 1 #women and non-binary
dm$genfin <- as.factor(dm$genfin)
table(dm$genfin, useNA = "always")##
## 0 1 <NA>
## 1526 595 143Y <- dm$belong1
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7858 -0.6571 0.2196 0.9089 2.1414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.518286 0.144159 31.342 < 2e-16 ***
## cmn_help -0.107886 0.019428 -5.553 3.16e-08 ***
## cmn_hetero 0.028563 0.014796 1.931 0.0537 .
## cmn_importance -0.136660 0.022145 -6.171 8.10e-10 ***
## cmn_violence 0.047022 0.030672 1.533 0.1254
## cmn_risks 0.151853 0.036323 4.181 3.03e-05 ***
## cmn_stoic -0.005414 0.016044 -0.337 0.7358
## genfin1 -0.260713 0.060318 -4.322 1.62e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.196 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.05622, Adjusted R-squared: 0.05309
## F-statistic: 17.98 on 7 and 2113 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8188 -0.6463 0.2127 0.9083 2.2244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.42137 0.16616 26.609 < 2e-16 ***
## cmn_help -0.12040 0.02302 -5.230 1.87e-07 ***
## genfin1 0.07180 0.32495 0.221 0.825155
## cmn_hetero 0.01653 0.01761 0.939 0.347913
## cmn_importance -0.12093 0.02649 -4.565 5.27e-06 ***
## cmn_violence 0.09403 0.03743 2.513 0.012060 *
## cmn_risks 0.15937 0.04211 3.784 0.000158 ***
## cmn_stoic -0.02032 0.01926 -1.055 0.291602
## cmn_help:genfin1 0.04286 0.04300 0.997 0.319088
## genfin1:cmn_hetero 0.03636 0.03253 1.118 0.263801
## genfin1:cmn_importance -0.05637 0.04857 -1.161 0.245971
## genfin1:cmn_violence -0.14220 0.06549 -2.171 0.030024 *
## genfin1:cmn_risks -0.02799 0.08328 -0.336 0.736803
## genfin1:cmn_stoic 0.03901 0.03508 1.112 0.266263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.195 on 2107 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.06043, Adjusted R-squared: 0.05463
## F-statistic: 10.42 on 13 and 2107 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
Y <- dm$belong2
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7304 -0.7137 0.1595 0.9333 2.3728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.54517 0.14154 32.112 < 2e-16 ***
## cmn_help -0.12706 0.01908 -6.661 3.46e-11 ***
## cmn_hetero 0.03229 0.01453 2.223 0.026317 *
## cmn_importance -0.12214 0.02174 -5.618 2.19e-08 ***
## cmn_violence 0.06315 0.03011 2.097 0.036102 *
## cmn_risks 0.10130 0.03566 2.840 0.004550 **
## cmn_stoic 0.02408 0.01575 1.529 0.126434
## genfin1 -0.20371 0.05922 -3.440 0.000594 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.174 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.05719, Adjusted R-squared: 0.05406
## F-statistic: 18.31 on 7 and 2113 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7922 -0.7131 0.1509 0.9233 2.4218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4694654 0.1627987 27.454 < 2e-16 ***
## cmn_help -0.1361137 0.0225575 -6.034 1.88e-09 ***
## genfin1 -0.0131085 0.3183785 -0.041 0.967162
## cmn_hetero 0.0167258 0.0172544 0.969 0.332474
## cmn_importance -0.1097277 0.0259514 -4.228 2.46e-05 ***
## cmn_violence 0.1333569 0.0366683 3.637 0.000283 ***
## cmn_risks 0.0916255 0.0412615 2.221 0.026484 *
## cmn_stoic -0.0009974 0.0188700 -0.053 0.957851
## cmn_help:genfin1 0.0342446 0.0421347 0.813 0.416458
## genfin1:cmn_hetero 0.0465312 0.0318729 1.460 0.144468
## genfin1:cmn_importance -0.0438108 0.0475910 -0.921 0.357382
## genfin1:cmn_violence -0.2086403 0.0641662 -3.252 0.001166 **
## genfin1:cmn_risks 0.0401736 0.0815933 0.492 0.622513
## genfin1:cmn_stoic 0.0696919 0.0343747 2.027 0.042745 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.171 on 2107 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.06533, Adjusted R-squared: 0.05956
## F-statistic: 11.33 on 13 and 2107 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
Y <- dm$id
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4245 -0.5402 0.0820 0.6555 2.0771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.30490 0.10845 39.694 < 2e-16 ***
## cmn_help -0.05683 0.01462 -3.888 0.000104 ***
## cmn_hetero 0.01978 0.01113 1.777 0.075779 .
## cmn_importance -0.13100 0.01666 -7.863 5.91e-15 ***
## cmn_violence 0.04538 0.02307 1.967 0.049363 *
## cmn_risks 0.11811 0.02733 4.322 1.62e-05 ***
## cmn_stoic 0.01570 0.01207 1.300 0.193577
## genfin1 -0.14427 0.04538 -3.179 0.001498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8999 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.05803, Adjusted R-squared: 0.05491
## F-statistic: 18.6 on 7 and 2113 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3977 -0.5386 0.0945 0.6461 2.0345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.221539 0.124937 33.789 < 2e-16 ***
## cmn_help -0.070147 0.017311 -4.052 5.26e-05 ***
## genfin1 0.108984 0.244334 0.446 0.655611
## cmn_hetero 0.016882 0.013242 1.275 0.202466
## cmn_importance -0.121944 0.019916 -6.123 1.09e-09 ***
## cmn_violence 0.088404 0.028140 3.142 0.001704 **
## cmn_risks 0.121193 0.031665 3.827 0.000133 ***
## cmn_stoic 0.001622 0.014481 0.112 0.910808
## cmn_help:genfin1 0.048664 0.032336 1.505 0.132485
## genfin1:cmn_hetero 0.004842 0.024460 0.198 0.843105
## genfin1:cmn_importance -0.031406 0.036523 -0.860 0.389944
## genfin1:cmn_violence -0.131752 0.049243 -2.676 0.007519 **
## genfin1:cmn_risks -0.011768 0.062617 -0.188 0.850946
## genfin1:cmn_stoic 0.040171 0.026380 1.523 0.127967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8987 on 2107 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.06321, Adjusted R-squared: 0.05743
## F-statistic: 10.94 on 13 and 2107 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
Y <- dm$belong1
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8818 -0.5569 0.1913 0.7437 2.6469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.57718 0.15893 9.924 < 2e-16 ***
## car_fin 0.07653 0.02737 2.796 0.00522 **
## car_inno 0.60550 0.02977 20.342 < 2e-16 ***
## car_lead -0.05764 0.02419 -2.383 0.01727 *
## genfin1 -0.31487 0.05312 -5.928 3.57e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.097 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2057, Adjusted R-squared: 0.2042
## F-statistic: 137 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8156 -0.5530 0.1863 0.7581 2.5884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.804640 0.184592 9.776 < 2e-16 ***
## car_fin 0.074190 0.031777 2.335 0.019652 *
## genfin1 -1.192298 0.360765 -3.305 0.000966 ***
## car_inno 0.583891 0.034705 16.824 < 2e-16 ***
## car_lead -0.088894 0.028789 -3.088 0.002043 **
## car_fin:genfin1 -0.009596 0.062433 -0.154 0.877858
## genfin1:car_inno 0.106804 0.067629 1.579 0.114427
## genfin1:car_lead 0.110785 0.053108 2.086 0.037094 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2102, Adjusted R-squared: 0.2076
## F-statistic: 80.32 on 7 and 2113 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
Y <- dm$belong2
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9675 -0.6848 0.1291 0.8332 2.6357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4100754 0.1634888 14.742 < 2e-16 ***
## car_fin -0.0002179 0.0281588 -0.008 0.994
## car_inno 0.4819622 0.0306205 15.740 < 2e-16 ***
## car_lead -0.0250846 0.0248819 -1.008 0.313
## genfin1 -0.2410332 0.0546400 -4.411 1.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.128 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.129, Adjusted R-squared: 0.1274
## F-statistic: 78.36 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9675 -0.6698 0.1344 0.8652 2.5372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.44286 0.19018 12.845 <2e-16 ***
## car_fin 0.02558 0.03274 0.781 0.4348
## genfin1 -0.41352 0.37169 -1.113 0.2660
## car_inno 0.45631 0.03576 12.762 <2e-16 ***
## car_lead -0.03498 0.02966 -1.179 0.2384
## car_fin:genfin1 -0.10707 0.06432 -1.665 0.0961 .
## genfin1:car_inno 0.10861 0.06968 1.559 0.1192
## genfin1:car_lead 0.04525 0.05471 0.827 0.4083
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.1312, Adjusted R-squared: 0.1283
## F-statistic: 45.59 on 7 and 2113 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
Y <- dm$id
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2549 -0.4935 0.0651 0.5664 2.3597
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.20087 0.11964 18.395 < 2e-16 ***
## car_fin 0.04022 0.02061 1.952 0.0511 .
## car_inno 0.46656 0.02241 20.821 < 2e-16 ***
## car_lead -0.04055 0.01821 -2.227 0.0261 *
## genfin1 -0.17824 0.03999 -4.458 8.72e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8255 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2062, Adjusted R-squared: 0.2047
## F-statistic: 137.4 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2512 -0.4999 0.0744 0.5707 2.2784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.28722 0.13908 16.445 <2e-16 ***
## car_fin 0.04453 0.02394 1.860 0.0631 .
## genfin1 -0.50896 0.27182 -1.872 0.0613 .
## car_inno 0.46533 0.02615 17.796 <2e-16 ***
## car_lead -0.06894 0.02169 -3.178 0.0015 **
## car_fin:genfin1 -0.02799 0.04704 -0.595 0.5519
## genfin1:car_inno 0.02270 0.05096 0.445 0.6560
## genfin1:car_lead 0.09888 0.04001 2.471 0.0135 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8245 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2093, Adjusted R-squared: 0.2067
## F-statistic: 79.9 on 7 and 2113 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
Y <- dm$engpc
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3046 -0.6491 0.1531 0.7854 2.4226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.42331 0.15252 15.888 < 2e-16 ***
## car_fin 0.04839 0.02627 1.842 0.06559 .
## car_inno 0.44361 0.02857 15.529 < 2e-16 ***
## car_lead -0.07059 0.02321 -3.041 0.00239 **
## genfin1 -0.28460 0.05097 -5.583 2.67e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.052 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.1268, Adjusted R-squared: 0.1252
## F-statistic: 76.83 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3500 -0.6573 0.1573 0.7865 2.3324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.50734 0.17731 14.141 < 2e-16 ***
## car_fin 0.06328 0.03052 2.073 0.038290 *
## genfin1 -0.62358 0.34653 -1.800 0.072082 .
## car_inno 0.43486 0.03334 13.045 < 2e-16 ***
## car_lead -0.10266 0.02765 -3.712 0.000211 ***
## car_fin:genfin1 -0.07097 0.05997 -1.184 0.236738
## genfin1:car_inno 0.05495 0.06496 0.846 0.397736
## genfin1:car_lead 0.11562 0.05101 2.267 0.023520 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.1301, Adjusted R-squared: 0.1273
## F-statistic: 45.16 on 7 and 2113 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
Y <- dm$engint
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7655 -0.4462 0.2248 0.5694 2.5256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.27070 0.13331 17.034 < 2e-16 ***
## car_fin 0.09006 0.02296 3.923 9.04e-05 ***
## car_inno 0.57673 0.02497 23.099 < 2e-16 ***
## car_lead -0.11173 0.02029 -5.507 4.09e-08 ***
## genfin1 -0.07702 0.04455 -1.729 0.084 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9198 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2325, Adjusted R-squared: 0.231
## F-statistic: 160.2 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7683 -0.4343 0.2291 0.5568 2.4986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.371802 0.155146 15.288 < 2e-16 ***
## car_fin 0.086199 0.026708 3.227 0.00127 **
## genfin1 -0.460260 0.303217 -1.518 0.12918
## car_inno 0.571343 0.029169 19.587 < 2e-16 ***
## car_lead -0.127433 0.024196 -5.267 1.53e-07 ***
## car_fin:genfin1 0.006643 0.052474 0.127 0.89927
## genfin1:car_inno 0.031756 0.056841 0.559 0.57644
## genfin1:car_lead 0.053983 0.044636 1.209 0.22664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9197 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2337, Adjusted R-squared: 0.2311
## F-statistic: 92.04 on 7 and 2113 DF, p-value: < 2.2e-16Y <- dm$engrec
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4418 -0.7369 0.0748 0.8346 3.1289
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.90860 0.17273 11.049 < 2e-16 ***
## car_fin -0.01780 0.02975 -0.598 0.54977
## car_inno 0.37933 0.03235 11.725 < 2e-16 ***
## car_lead 0.06067 0.02629 2.308 0.02111 *
## genfin1 -0.17311 0.05773 -2.999 0.00274 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.192 on 2116 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.09266, Adjusted R-squared: 0.09094
## F-statistic: 54.02 on 4 and 2116 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4552 -0.7284 0.0687 0.8419 3.0600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.98252 0.20093 9.867 <2e-16 ***
## car_fin -0.01590 0.03459 -0.460 0.6458
## genfin1 -0.44304 0.39270 -1.128 0.2594
## car_inno 0.38979 0.03778 10.318 <2e-16 ***
## car_lead 0.02329 0.03134 0.743 0.4575
## car_fin:genfin1 -0.01964 0.06796 -0.289 0.7726
## genfin1:car_inno -0.01860 0.07362 -0.253 0.8005
## genfin1:car_lead 0.12704 0.05781 2.198 0.0281 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.191 on 2113 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.09497, Adjusted R-squared: 0.09197
## F-statistic: 31.68 on 7 and 2113 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
Y <- dm$belong1
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8450 -0.5205 0.1801 0.6795 2.9054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.86866 0.14048 6.183 7.42e-10 ***
## engbel 0.04108 0.04593 0.894 0.371
## engemp 0.70089 0.03929 17.841 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 2261 degrees of freedom
## Multiple R-squared: 0.3188, Adjusted R-squared: 0.3182
## F-statistic: 529 on 2 and 2261 DF, p-value: < 2.2e-16plot_model(fit1, type="pred")## $engbel
##
## $engemp
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5884 -0.5130 0.2116 0.6158 3.2477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.95143 0.16800 5.663 1.69e-08 ***
## engbel 0.11386 0.05512 2.066 0.0390 *
## genfin1 -0.63179 0.31835 -1.985 0.0473 *
## engemp 0.62564 0.04679 13.372 < 2e-16 ***
## engbel:genfin1 -0.14098 0.10477 -1.346 0.1786
## genfin1:engemp 0.21236 0.09017 2.355 0.0186 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.001 on 2115 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.3384, Adjusted R-squared: 0.3368
## F-statistic: 216.4 on 5 and 2115 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
Y <- dm$belong2
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1916 -0.5346 0.1484 0.8084 2.8185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.17147 0.14527 8.064 1.18e-15 ***
## engbel 0.19890 0.04750 4.187 2.93e-05 ***
## engemp 0.47112 0.04062 11.597 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.052 on 2261 degrees of freedom
## Multiple R-squared: 0.239, Adjusted R-squared: 0.2383
## F-statistic: 355 on 2 and 2261 DF, p-value: < 2.2e-16plot_model(fit1, type="pred")## $engbel
##
## $engemp
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1059 -0.5381 0.1536 0.7366 3.0409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.17458 0.17457 6.728 2.20e-11 ***
## engbel 0.26114 0.05728 4.559 5.43e-06 ***
## genfin1 -0.36217 0.33081 -1.095 0.2737
## engemp 0.42033 0.04862 8.646 < 2e-16 ***
## engbel:genfin1 -0.18283 0.10887 -1.679 0.0932 .
## genfin1:engemp 0.21694 0.09369 2.315 0.0207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.04 on 2115 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.2597, Adjusted R-squared: 0.258
## F-statistic: 148.4 on 5 and 2115 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
Y <- dm$id
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6420 -0.4213 0.0719 0.5209 2.3605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.41539 0.10341 13.687 < 2e-16 ***
## engbel 0.13151 0.03381 3.889 0.000103 ***
## engemp 0.47097 0.02892 16.287 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.749 on 2261 degrees of freedom
## Multiple R-squared: 0.3425, Adjusted R-squared: 0.3419
## F-statistic: 588.8 on 2 and 2261 DF, p-value: < 2.2e-16plot_model(fit1, type="pred")## $engbel
##
## $engemp
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7013 -0.4303 0.0698 0.5204 2.4427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.51458 0.12486 12.130 < 2e-16 ***
## engbel 0.17431 0.04097 4.255 2.18e-05 ***
## genfin1 -0.54998 0.23661 -2.324 0.02020 *
## engemp 0.41572 0.03477 11.955 < 2e-16 ***
## engbel:genfin1 -0.09656 0.07787 -1.240 0.21513
## genfin1:engemp 0.17632 0.06702 2.631 0.00858 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.744 on 2115 degrees of freedom
## (143 observations deleted due to missingness)
## Multiple R-squared: 0.3555, Adjusted R-squared: 0.354
## F-statistic: 233.3 on 5 and 2115 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]