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Decision Tree in R, with Graphs: Predicting State Politics from Big Five Traits

This was a continuation of prior explorations, logistic regression predicting Red/Blue state dichotomy by income or by personality. This uses the same five personality dimensions, but instead builds a decision tree. Of the Big Five traits, only two were found to useful in the decision tree, conscientiousness and openness.

Links to sample data, as well as to source references, are at the end of this entry.

Example Code

# Decision Tree - Big Five and Politics library("rpart") # grow tree input.dat <- read.table("BigFiveScoresByState.csv", header = TRUE, sep = ",") fit <- rpart(Liberal ~ Openness + Conscientiousness + Neuroticism + Extraversion + Agreeableness, data = input.dat, method="poisson") # display the results printcp(fit) # visualize cross-validation results plotcp(fit) # detailed summary of splits summary(fit) # plot tree plot(fit, uniform = TRUE, main = "Classific…

Chi-Square in R on by State Politics (Red/Blue) and Income (Higher/Lower)

This is a significant result, but instead of a logistic regression looking at the income average per state and the likelihood of being a Democratic state, it uses Chi-Square. Interpreting this is pretty straightforward, in that liberal states typically have cities and people that earn more money. When using adjusted incomes, by cost of living, this difference disappears.

Example Code
# R - Chi Square rm(list = ls()) stateData <- read.table("CostByStateAndSalary.csv", header = TRUE, sep = ",") # Create vectors affluence.median <- median(stateData$Y2014, na.rm = TRUE) affluence.v <- ifelse(stateData$Y2014 > affluence.median, 1, 0) liberal.v <- stateData$Liberal # Solve pol.Data = table(liberal.v, affluence.v) result <- chisq.test(pol.Data) print(result) print(pol.Data)
Example Results
Pearson's Chi-squared test with Yates' continuity correction data: pol.Data X-squared = 12.672, df …

Logistic Regression in R on State Voting in National Elections and Income

This data set - a link is at the bottom - is slightly different, income by state and political group, red or blue. Generally, residents of blue states have higher incomes, although not when adjusted for cost of living:

Example Code
# R - Logistic Regression stateData <- read.table("CostByStateAndSalary.csv", header = TRUE, sep = ",") am.data = glm(formula = Liberal ~ Y2014, data = stateData, family = binomial) print(summary(am.data))
Example Results
Call: glm(formula = Liberal ~ Y2014, family = binomial, data = stateData) Deviance Residuals: Min 1Q Median 3Q Max -2.4347 -0.7297 -0.4880 0.6327 1.8722 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.325e+00 2.671e+00 -3.491 0.000481 *** Y2014 1.752e-04 5.208e-05 3.365 0.000765 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for bi…