First of all I am fairly new to R. So please do not be so harsh in your comments. Thanks in advance for any help/link/how-to/example; those will be much appreciated.
I have a model like this:
b <- lmer(metric1~a+b+c+d+e+f+g+h+i+j+k+l+(1|X) + (1|Y), data = dataset)
And by using
drop1 (b, test="Chisq")
I have to check AIC and p values in each step. I eliminate the variable with the lowest AIC in each step.
However, I have more than 100 metrics. Doing this with 100 metrics and 12 variables is a big mess.
Although I am not a statistics guru, I already know that AIC and q values are not the best way for model decision (I already read a few documents, discussing with AIC/p values). However, I will not use this models to formulate anything. They will just help me to judge which metrics respond to variables.
So, I wonder if it is possible to this drop1 thing automatically?