I am using the Stata dataset ANES.dta with the information about the 2000 presidential election in the USA. I build two models on this dataset - one logit and one LPM. I want to compare the two models with each other using the following Goodness of fit measures - accuracy, sensitivity and specificity of the both models.
I am new to R, I have mainly used STATA so far and that's why I'm wondering if it is normal to get absolutely the same values in confusion matrices for a logit model and a LPM model, based on the same data? Am I doing something wrong?
rm(list=ls())
library(foreign)
dat <- read.dta("ANES.dta", convert.factors = FALSE)
dat_clear <- na.omit(dat)
head(dat_clear)
#Logit model
m1_logit <- glm(vote ~ gender + income + pro_choice ,
data = dat_clear, family = binomial(link = "logit") ,
na.action = na.omit)
summary(m1_logit)
#LPM
m2_lpm <- lm(vote ~ gender + income + pro_choice,
data = dat_clear, na.action = na.omit)
summary(m2_lpm)
#Confusion matrix for logit model
dat_clear$prediction_log <- predict(m1_logit, newdata = dat_clear, type = "response")
dat_clear$vote_pred_log <- as.numeric(dat_clear$prediction_log > .5)
table(observed = dat_clear$vote, predicted = dat_clear$vote_pred_log)
#Confusion matrix for LPM model
dat_clear$prediction_lpm <- predict(m2_lpm, newdata = dat_clear, type = "response")
dat_clear$vote_pred_lpm <- as.numeric(dat_clear$prediction_lpm > .5)
table(observed = dat_clear$vote, predicted = dat_clear$vote_pred_lpm)
