[R] Variable Selection for Logistic Regression

Yixuan Qiu yixuan.qiu at cos.name
Thu Dec 17 16:42:50 CET 2015

Hello Manish,

In my point of view, the "marginal" selection of variables can be
misleading since it ignores the correlation between independent
variables. I would suggest using some modern variable selection
techniques such as Lasso-typed methods.

One method that directly fits your need is the L1-regularized logistic
regression implemented by the glmnet package. Here is a vignette that
should be helpful for you:


2015-12-17 4:36 GMT-05:00 Manish MAHESHWARI <manishm at dbs.com>:
> Hi,
> I have a dataset with approx 400K Rows and 900 columns with a single dependent variable of 0/1 flag. The independent variables are both categorical and numerical. I have looked as SO/Cross Validated Posts but couldn't get an answer for this.
> Since I cannot try all possible combinations of variables or even attempt single model with all 900 columns, I am planning to create independent models of each variable using something like below -
> out = NULL
> xnames = colnames(train)[!colnames(train) %in% ignoredcols]
> for (f in xnames) {
>     glmm = glm(train$conversion_flag ~ train[,f] - 1 , family = binomial)
>     out = rbind.fill(out,as.data.frame(cbind(f,fmsb::NagelkerkeR2(glmm)[2]$R2)))
>     out = rbind.fill(out,as.data.frame(cbind(f,'AIC',summary(glmm)$aic)))
> }
> This will give me the individual AIC and pseudo R2 for each of the variables. Post that I plan to select the variables with the best scores for both AIC and pseudoR2. Does this approach make sense?
> I obviously will use a nfold cross validation in the final model to ensure accuracy and avoid over fitting. However before I reach that I plan to use the above to select which variables to use.
> Thanks,
> Manish
> The information contained in this email is intended on...{{dropped:15}}

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