Hi There,
I am trying to fit a logit model to some data in a CSV file in R.
Here is my code:
Prepared_Data = read.csv("Prepared_Data.csv", header=TRUE)
Prepared_Data
attach(Prepared_Data)
lrfit<-glm(C3~A1*B2*D4*E5,family = binomial)
anova(lrfit, test="Chisq")
write.csv(anova(lrfit, test="Chisq"), file="CWModelA.csv")
shell.exec("CWModelA.csv")
I am unsure as to how many methods there are of choosing a suitable model,
however, I was hoping to fit the full/saturated model and choose the
significant terms only as my final model.
My first question therefore: is there a better way to fit a model to some data?
Is there a function or way of getting R to print the optimum model?
My CSV file, when opened in excel, contains approximately 3500 rows x 27
columns. I can only seem to run 'anova()' on the saturated/full model including
the first four columns/factors. If I take any more into consideration (e.g. if
I did C3~A1*B2*D4*E5*F6*G7), R stops responding/I have to force quit. Why is
this? How can I get around it as I need to include all 27 columns?
Any advice or constructive criticism is appreciated - even if it means I have
to start from scratch.
Many Thanks,
AJC
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