Deviance Residuals
Computes the root of the deviance residuals for evaluation of performance in discrete survival analysis.
devResidShort(dataSet, hazards)
dataSet |
Original data in long format. Must be of class "data.frame". The correct format can be specified with data preparation, see e. g. |
hazards |
Estimated hazard rates of the data in long format. Discrete hazard rates are probabilities and therefore restricted to the interval [0, 1] |
Output: List with objects:
DevResid: Square root of deviance residuals as numeric vector.
Input: A list of given argument input values (saved for reference)
Thomas Welchowski welchow@imbie.meb.uni-bonn.de
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
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 the deviance residuals devResiduals <- devResidShort (dataSet=heartLong, hazards=hazPreds)$Output$DevResid # Compare with estimated normal distribution plot(density(devResiduals), main="Empirical density vs estimated normal distribution", las=1, ylim=c(0, 0.5)) tempFunc <- function (x) dnorm(x, mean=mean(devResiduals), sd=sd(devResiduals)) curve(tempFunc, xlim=c(-10, 10), add=TRUE, col="red") # The empirical density seems like a mixture distribution, # but is not too far off in with values greater than 3 and less than 1
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