adjDevResidShort: Adjusted Deviance Residuals in short format

Description

Calculates the adjusted deviance residuals for arbitrary prediction models. The adjusted deviance residuals should be approximately normal distributed, in the case of a well fitting model.

Usage

adjDevResidShort(dataSet, hazards)

Arguments

dataSet

Data set in long format. Must be of class "data.frame".

hazards

Estimated hazard rates of the data in long format. Hazard rates are probabilities and therefore restricted to the interval [0, 1]

Value

Output: List with objects:
- AdjDevResid: Adjusted deviance residuals as numeric vector
Input: A list of given argument input values (saved for reference)

References

Gerhard Tutz and Matthias Schmid, (2016), Modeling discrete time-to-event data, Springer series in statistics, Doi: 10.1007/978-3-319-28158-2

Gerhard Tutz, (2012), Regression for Categorical Data, Cambridge University Press

Examples

Run this code

# NOT RUN {
library(survival)

# Transform data to long format
heart[, "stop"] <- ceiling(heart[, "stop"])
set.seed(0)
Indizes <- sample(unique(heart$id), 25)
randSample <- heart[unlist(sapply(1:length(Indizes), 
function(x) which(heart$id==Indizes[x]))),]
heartLong <- dataLongTimeDep(dataSet=randSample, 
timeColumn="stop", censColumn="event", idColumn="id", timeAsFactor=FALSE)

# Fit a generalized, additive model and predict hazard rates on data in long format
library(mgcv)
gamFit <- gam(y ~ timeInt + surgery + transplant + s(age), data=heartLong, family="binomial")
hazPreds <- predict(gamFit, type="response")

# Calculate adjusted deviance residuals
devResiduals <- adjDevResidShort (dataSet=heartLong, hazards=hazPreds)$Output$AdjDevResid
devResiduals
# }