CODEWITHSUNDEEP
rroclogistic-regressionconfusion-matrix
Sup
Sup

Reputation: 33

Classification table and ROC curve - logistic regression in R using lrm

I want to have a classification table for logistic regression using lrm function in rms package and then plot the roc curve.I have perfomed this using glm function.Example code 

train<-sample(dim(data)[1],.8*dim(data)[1]) #80-20 training/test
datatrain<-data[train,]
datatest<-data[-train,]
fit<-glm(Target ~ ., data=datatrain,family=binomial()) #Target is 0/1 variable
prob=predict(fit,type=c("response"),datatest)
datatest$prob=prob
library(pROC)
ROC <- roc(Target==1 ~ prob, data = datatest)
plot(ROC)
confusion<-table(prob>0.5,datatest$Target)
errorrate<-sum(diag(confusion))/sum(confusion)
errorrate

How to get the confusion matrix using lrm function?

Upvotes: 2

Views: 3355

Answers (1)

IRTFM
IRTFM

Reputation: 263451

The lrm function returns a fit object that inherits from the glm-class. That is not explicitly stated in the lrm help page, but it's easy enough to verify. After running the setup code in the first example on the ?lrm page

> f <- lrm(ch ~ age)
> class(f)
[1] "lrm" "rms" "glm"

So you should be able to use the ordinary predict method you were using above. Prof Harrell advises against using split-sample validation and the use of ROC curves for model comparison. He provides mechanisms for better methods in his package.

Upvotes: 0

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