devResid: Deviance Residuals

In discSurv: Discrete Time Survival Analysis

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)

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

\insertReftutzRegCatdiscSurv

See Also

adjDevResid, predErrCurve

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 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

discSurv documentation built on March 18, 2022, 7:12 p.m.