plot multiple fit and predictions for logistic regression - r

Question

I am running multiple times a logistic regression over more than 1000 samples taken from a dataset. My question is what is the best way to show my results ? how can I plot my outputs for both the fit and the prediction curve?
This is an example of what I am doing, using the baseball dataset from R. For example I want to fit and predict the model 5 times. Each time I take one sample out (for the prediction) and use another for the fit.
library(corrgram)
data(baseball)
#Exclude rows with NA values
dataset=baseball[complete.cases(baseball),]
#Create vector replacing the Leage (A our N) by 1 or 0.
PA=rep(0,dim(dataset)[1])
PA[which(dataset[,2]=="A")]=1
#Model the player be league A in function of the Hits,Runs,Errors and Salary
fit_glm_list=list()
prd_glm_list=list()
for (k in 1:5){
sp=sample(seq(1:length(PA)),30,replace=FALSE)
fit_glm<-glm(PA[sp[1:15]]~baseball$Hits[sp[1:15]]+baseball$Runs[sp[1:15]]+baseball$Errors[sp[1:15]]+baseball$Salary[sp[1:15]])
prd_glm<-predict(fit_glm,baseball[sp[16:30],c(6,8,20,21)])
fit_glm_list[[k]]=fit_glm;prd_glm_list[[k]]=fit_glm
}

Answer 1

There are a number of issues here.
PA is a subset of baseball$League but the model is constructed on columns from the whole baseball data frame, i.e. they do not match.
PA is treated as a continuous response when using the default family (gaussian), it should be changed to a factor and binomial family.
prd_glm_list[[k]]=fit_glm should probably be prd_glm_list[[k]]=prd_glm
You must save the true class labels for the predictions otherwise you have nothing to compare to.
My take on your code looks like this.
library(corrgram)
data(baseball)
dataset <- baseball[complete.cases(baseball),]
fits <- preds <- truths <- vector("list", 5)
for (k in 1:5){
sp <- sample(nrow(dataset), 30, replace=FALSE)
fits[[k]] <- glm(League ~ Hits + Runs + Errors + Salary,
family="binomial", data=dataset[sp[1:15],])
preds[[k]] <- predict(fits[[k]], dataset[sp[16:30],], type="response")
truths[[k]] <- dataset$League[sp[1:15]]
}
plot(unlist(truths), unlist(preds))
The model performs poorly but at least the code runs without problems. The y-axis in the plot shows the estimated probabilities that the examples belong to league N, i.e. ideally the left box should be close to 0 and the right close to 1.