Display ROC curve and related AUC statistic, or sensitivityspecificity plot of glm with binomial family.
Description
Provides two options following the glm() function with binomial family. 1: Senstivityspecificity plot with optimal cut point statistic 2: ROC plot with Area Under Curve (AUC) statistic
Usage
1 
Arguments
model 
model name 
fold 
number of kfolds 
type 
type of plot 
Format
 x

The function has three arguments: modelname, folds, type of plot
Details
ROCtest is a postestimation function for logistic regression, following the use of glm(). Options to display a sensitivityspecificity plot or ROC curve are available.
Value
plot
Note
ROCtest() must be loaded into memory in order to be effectve. As a function in LOGIT, it is immediately available to a user.
Author(s)
Rafael de Souza, ELTE, Hungary, Joseph M. Hilbe, Arizona State University.
References
Hilbe, Joseph M. (2016), Practical Guide to Logistic Regression, Chapman & Hall/CRC. Hilbe, Joseph M. (2009), Logistic Regression Models, Chapman & Hall/CRC.
See Also
glm
Examples
1 2 3 4 5 6 7 8 9 10  library(MASS)
library(LOGIT)
data(R84)
R84$cage < R84$age  mean(R84$age)
R84$cdoc < R84$docvis  mean(R84$docvis)
mylogit < glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
family=binomial, data=R84)
summary(mylogit)
ROCtest(mylogit, fold=10, type="Sensitivity")
ROCtest(mylogit, fold=10, type="ROC")
