In R, using binomial() and lapply to constrain predict() values between 0 and 1

Question

I have a GLM, family=binomial(link=logit) model that I apply within a predict() function, seen below. The predict values go beyond zero and 1, but I would like to keep them as probabilities. So I use the binomial()$inverse command that can be then be used in the apply function.

This worked just fine the first time I ran it, but after closing R down and starting again, I now get this error:

     Error in get(as.character(FUN), mode = "function", envir = envir) : 
     object 'ilogit' of mode 'function' was not found"

I've been struggling with this for hours, as this code normally worked. Does anyone have an idea about what I am doing wrong? Is there a better way of doing this?

My code is below. I've also tried other variation but can't get it to work.

    ## predicted probabilities 
    pp <- predict(logit_model,
            newdata=data,
            type="link",
            se.fit=T)

   ilogit <- binomial()$inverse
   yhat_prob <- lapply(pp,ilogit) #converts to probabilities

Answer 1

If you want the probabilities, you can have them directly with type="response", as explained in the documentation, ?pregict.glm.

For the error message you get, you probably need binomial()$linkinv.

> str( binomial() )
List of 12
 $ family    : chr "binomial"
 $ link      : chr "logit"
 $ linkfun   :function (mu)  
 $ linkinv   :function (eta)  
 $ variance  :function (mu)  
 ...

The lack of error was probably due to some package you had loaded, which defined an ilogit function.