| adjDevResid {discSurv} | R Documentation |
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
adjDevResid(dataLong, hazards)
Arguments
dataLong |
Data set in long format ("class data.frame"). |
hazards |
Estimated discrete hazards of the data in long format("numeric vector"). 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)
Author(s)
Thomas Welchowski welchow@imbie.meb.uni-bonn.de
References
Tutz G, Schmid M (2016).
Modeling discrete time-to-event data.
Springer Series in Statistics.
Tutz G (2012).
Regression for Categorical Data.
Cambridge University Press.
See Also
Examples
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(dataSemiLong = randSample,
timeColumn = "stop", eventColumn = "event", idColumn = "id", timeAsFactor = FALSE)
# Fit a generalized, additive model and predict discrete hazards 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 <- adjDevResid(dataLong = heartLong, hazards = hazPreds)$Output$AdjDevResid
devResiduals