modelValidity: Return discriminative and calibration measures for predictive...
In mechkar: Useful Tools for Scientific Research

Description Usage Arguments Details Value Author(s) Examples

This function returns common statistical validity metrics used for testing the discriminative power and calibration of a predictive model.

1	modelValidity(data,model,class, train = FALSE, calib.graph=FALSE)

`data`	a data frame contining the data to be tested on the model
`model`	the model to be tested
`class`	the name (as a string) of the outcome variable
`train`	a logical value indicating if the data comes from the same dataset used to train the model. Defaults is FALSE
`calib.graph`	a logical value indicating if the calibration graph has to be generated. Deafault is FALSE

The modelValidity function returns a summary table with the validity metrics most commonly used in predictive modeling.

A character matrix containing the following statistical metrics:

`auc`	the area under the curve of the model
`cimin`	the lower 95% confidence interval of the auc
`cimax`	the higher 95% confidence interval of the auc
`SRME`	the squre root mean error
`precision`	the precision of the model. This is also known in epidemiology as the positive predictive value (PPV)
`recall`	the recall of the model. Also known in epidemilogy as the sensitivity
`fscore`	the armonic mean of precision and recall (F1-score).
`NPV`	the negative predictive value
`D`	the Tjur\'s discriminative measure
`TN`	the true negative value
`mmce`	the mean missclassification error
`Hosmer_Lemeshow`	the Hosmer-Lemeshow index

Tomas Karpati M.D.

set.seed(123)
n <- 100
x <- rnorm(n)
xb <- x
pr <- exp(xb)/(1+exp(xb))
y <- 1*(runif(n) < pr)
mod <- glm(y~x, family="binomial")
vt <- modelValidity(data.frame(x=x,y=y),mod,"y")