devResid: Deviance Residuals

Description

Computes the root of the deviance residuals for evaluation of performance in discrete survival analysis.

Usage

devResid(dataLong, hazards)

Arguments

dataLong

Original data in long format ("class data.frame"). The correct format can be specified with data preparation, see e. g. dataLong.

hazards

Estimated discrete hazards of the data in long format("numeric vector"). Discrete discrete hazards are probabilities and therefore restricted to the interval [0, 1].

Value

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

References

tutzModelDiscdiscSurv tutzRegCatdiscSurv

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(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 the deviance residuals
devResiduals <- devResid (dataLong = 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

# }