[R] variable selection using residual difference

Bert Gunter bgunter.4567 at gmail.com
Fri Mar 18 19:52:19 CET 2016


Don't do this!

I suggest that you consult with a local statistician or post to a
statistical website like stats.stackexchange.com for what might be
sensible procedures for variable selection (a complex and
controversial topic!) and why what you propose is or is not a good
idea (don't trust me!).


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Fri, Mar 18, 2016 at 9:00 AM, Hassan, Nazatulshima
<Nazatulshima.Hassan at liverpool.ac.uk> wrote:
> I have the following example dataset
> set.seed(2001)
> 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)
> mod1$null.deviance-mod1$deviance
> mod2 <- glm(Y~X3+X2, family=binomial,data=dat)
> mod2$null.deviance-mod2$deviance
> 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)
> mod3$null.deviance-mod3$deviance
> 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
> Shima
>         [[alternative HTML version deleted]]
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