library(carData)
library(MASS)
data(WVS)
head(WVS)
## poverty religion degree country age gender
## 1 Too Little yes no USA 44 male
## 2 About Right yes no USA 40 female
## 3 Too Little yes no USA 36 female
## 4 Too Much yes yes USA 25 female
## 5 Too Little yes yes USA 39 male
## 6 About Right yes no USA 80 female
summary(WVS)
## poverty religion degree country age
## Too Little :2708 no : 786 no :4238 Australia:1874 Min. :18.00
## About Right:1862 yes:4595 yes:1143 Norway :1127 1st Qu.:31.00
## Too Much : 811 Sweden :1003 Median :43.00
## USA :1377 Mean :45.04
## 3rd Qu.:58.00
## Max. :92.00
## gender
## female:2725
## male :2656
##
##
##
##
library(ggplot2)
ggplot(WVS, aes(x = poverty, y = age, fill = poverty)) + geom_boxplot(size = .75) + facet_grid(country ~ gender, margins = FALSE) + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
model_fit <- polr(poverty~religion+degree+country+age+gender, data =
WVS, Hess = TRUE)
summary(model_fit)
## Call:
## polr(formula = poverty ~ religion + degree + country + age +
## gender, data = WVS, Hess = TRUE)
##
## Coefficients:
## Value Std. Error t value
## religionyes 0.17973 0.077346 2.324
## degreeyes 0.14092 0.066193 2.129
## countryNorway -0.32235 0.073766 -4.370
## countrySweden -0.60330 0.079494 -7.589
## countryUSA 0.61777 0.070665 8.742
## age 0.01114 0.001561 7.139
## gendermale 0.17637 0.052972 3.329
##
## Intercepts:
## Value Std. Error t value
## Too Little|About Right 0.7298 0.1041 7.0128
## About Right|Too Much 2.5325 0.1103 22.9496
##
## Residual Deviance: 10402.59
## AIC: 10420.59
summary_table <- coef(summary(model_fit))
pval <- pnorm(abs(summary_table[, "t value"]),lower.tail = FALSE)* 2
summary_table <- cbind(summary_table, "p value" = round(pval,3))
summary_table
## Value Std. Error t value p value
## religionyes 0.17973194 0.077346042 2.323738 0.020
## degreeyes 0.14091745 0.066193109 2.128884 0.033
## countryNorway -0.32235359 0.073766034 -4.369946 0.000
## countrySweden -0.60329785 0.079493909 -7.589234 0.000
## countryUSA 0.61777260 0.070664761 8.742301 0.000
## age 0.01114091 0.001560585 7.138933 0.000
## gendermale 0.17636863 0.052972253 3.329453 0.001
## Too Little|About Right 0.72976353 0.104061643 7.012800 0.000
## About Right|Too Much 2.53247870 0.110349780 22.949558 0.000
new_data <-
data.frame("religion"="yes","degree"="no","country"="Norway",
"age"=30,"gender"="male")
round(predict(model_fit,new_data,type = "p"), 3)
## Too Little About Right Too Much
## 0.589 0.308 0.103