[R] variable selection using residual difference

Hassan, Nazatulshima Nazatulshima.Hassan at liverpool.ac.uk
Fri Mar 18 17:00:20 CET 2016

I have the following example dataset
n <- 100
Y <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
X1 <- sample(x=c(0,1,2), size=n, replace=TRUE, prob=c(0.1,0.4,0.5))
X2 <- sample(x=c(0,1,2), size=n, replace=TRUE, prob=c(0.5,0.25,0.25))
X3 <- c(0,2,2,2,2,2,2,2,0,2,0,2,2,0,0,0,0,0,2,0,0,2,2,0,0,2,2,2,0,2,0,2,0,2,1,2,1,1,1,1,1,1,1,1,1,1,1,0,1,2,2,2,2,2,2,2,2,2,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0)

dat <- data.frame(Y,X1,X2,X3)

I fit a logistic regression model to each of the variable to rank them based on the residual difference (highest to lowest). To simplify I got the rank as X3,X1 and X2. Then, I fit a second order model as follows and again calculate the res_dif :
mod1 <- glm(Y~X3+X1, family=binomial, data=dat)
mod2 <- glm(Y~X3+X2, family=binomial,data=dat)

Again, I will rank the model based on res_dif (highest to lowest). So here, I choose mod2. From there I will fit the third order model as follows :
mod3 <- glm(Y~X3+X2+X1, family=binomial, data=dat)

Basically, this continues until it fits the maximum number of variables that you have in the data.
My aim is to do variable selection based on res_dif instead of AIC, BIC or R2. Since my actual dataset is dealing with 100 of variables, I wonder how can I apply this using loop function.

Any suggestions would be appreciated.

Kind Regards

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