Gustavo Arruda 2020-11-17
library(tidyverse)
library(rcfss)
library(randomForest)
library(knitr)
library(caret)
library(partykit)
theme_set(theme_minimal())
set.seed(1234)gss_colrac <- gss_colrac
gss_colrac_transformed <- gss_colrac %>%
mutate(colrac = if_else(colrac == FALSE, "NO",
if_else(colrac == TRUE, "YES", NA_character_))) %>%
mutate_if(is.character, as.factor)
train_control <- trainControl(method = "oob")
# Train the model
random_forest_model <- train(colrac ~., data = gss_colrac_transformed,
method = "rf",
ntree = 200,
trControl = train_control,
na.action = na.omit
)
random_forest_model$finalModel##
## Call:
## randomForest(x = x, y = y, ntree = 200, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 200
## No. of variables tried at each split: 34
##
## OOB estimate of error rate: 23.56%
## Confusion matrix:
## NO YES class.error
## NO 850 156 0.1550696
## YES 309 659 0.3192149
varImpPlot(random_forest_model$finalModel)
colrac_selected <- ctree(colrac ~ tolerance + age + egalit_scale + wordsum + authoritarianism, data = gss_colrac_transformed)
plot(colrac_selected,
ip_args = list(
pval = TRUE,
id = FALSE),
tp_args = list(
id = FALSE)
)
I choose a random forest algorithm to build this model. Such algorithm iteratively select the best variables to construct a forest tree model, which does a good work to elicit relationships between categorical variables in a data set. The random_forest_model$finalModel chart shows that ‘tolerance’, ‘age’, ‘egalit_scale’, ‘wordsum’ and ‘authoritarianism’ are the most important predicting variables.
devtools::session_info()## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.0.2 (2020-06-22)
## os Windows 8.1 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Chicago
## date 2020-11-17
##
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##
## [1] C:/Users/Gustavo/OneDrive - The University of Chicago/Documents/R/win-library/4.0
## [2] C:/Program Files/R/R-4.0.2/library