givitiCalibrationTestComp {givitiR}

Computation of the Calibration Test

Description

givitiCalibrationTestComp implements the computations necessary to perform the calibration test associated to the calibration belt.

Usage

givitiCalibrationTestComp(o, e, devel, thres, maxDeg)

Arguments

o

A numeric vector representing the binary outcomes. The elements must assume only the values 0 or 1. The predictions in e must represent the probability of the event coded as 1.

e

A numeric vector containing the probabilities of the model under evaluation. The elements must be numeric and between 0 and 1. The lenght of the vector must be equal to the length of the vector o.

devel

A character string specifying if the model has been fit on the same dataset under evaluation (internal) or if the model has been developed on an external sample (external). See also the 'Details' sections.

thres

A numeric scalar between 0 and 1 representing 1 - the significance level adopted in the forward selection.

maxDeg

The maximum degree considered in the forward selection.

Details

The calibration belt and the associated test can be used both to evaluate the calibration of the model in external samples or in the development dataset. However, the two cases have different requirements. When a model is evaluated on independent samples, the calibration belt and the related test can be applied whatever is the method used to fit the model. Conversely, they can be used on the development set only if the model is fitted with logistic regression.

Value

A list containing the following components:

data

A data.frame object with the numeric variables "o", "e" provided in the input and the variable "logite", the logit of the probabilities.

nrowOrigData

The size of the original sample, i.e. the length of the vectors e and o.

calibrationStat

The value of the test's statistic.

calibrationP

The p-value of the test.

m

The degree of the polynomial at the end of the forward selection.

fit

An object of class glm containig the output of the fit of the logistic regression model at the end of the iterative forward selection.

See Also

givitiCalibrationBelt and plot.givitiCalibrationBelt to compute and plot the calibaration belt, and givitiCalibrationTest to perform the associated calibration test.

Examples

e <- runif(100)
o <- rbinom(100, size = 1, prob = e)
givitiCalibrationTestComp(o, e, "external", .95, 4)