ROCtest: Display ROC curve and related AUC statistic, or...

Description Usage Arguments Format Details Value Note Author(s) References See Also Examples

Description

Provides two options following the glm() function with binomial family. 1: Senstivity-specificity plot with optimal cut point statistic 2: ROC plot with Area Under Curve (AUC) statistic

Usage

1
ROCtest(model= model, fold=10, type=c("ROC","Sensitivity"))

Arguments

model

model name

fold

number of k-folds

type

type of plot

Format

x

The function has three arguments: modelname, folds, type of plot

Details

ROCtest is a post-estimation function for logistic regression, following the use of glm(). Options to display a sensitivity-specificity 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

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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")


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