How do I ignore errors and continue processing list items?

Question

I\'m running several hundred datasets through glm.nb using a wrapper function. Nothing fancy, I just pass on each list item via llply, then fit using <

Answer 1

I have an nls() which I run that has the same challenge. I'm applying the regression to every group in a data.frame, but this logic should work for you as well (I think):

 dlply(myData, c("group1", "group2"), function(df){  
       tryCatch(nls(depen ~ exp(a1 + b1 * year) , data=df, start = list(a1 = -1, b1 = 0), na.action=na.omit), error=function(e) NULL)

so if I were to guess how to apply that to your situation, it would be something like:

res <- trycatch(glm.nb(x~y, data=x), error=function(e) NULL )

The way I use this, I'm throwing NA values any time the regression does not converge. You may want to do something different.

Answer 2

you can also use the failwith function in plyr. if f is the function you are passing to plyr, you can instead pass the function

safef = failwith(NA, f)

of course, you can replace NA with whatever return value you need when the function fails. this code is lifted directly from the examples for failwith.