plot: Plot Diagnostics for Joint Models

Description

Produces a variety of plots for fitted joint models.

Usage

# S3 method for jointModel
plot(x, which = 1:4, caption = c("Residuals vs Fitted", 
  "Normal Q-Q", "Marginal Survival", "Marginal Cumulative Hazard", 
  "Marginal log Cumulative Hazard", "Baseline Hazard", 
  "Cumulative Baseline Hazard", "Subject-specific Survival", 
  "Subject-specific Cumulative Hazard", 
  "Subject-specific log Cumulative Hazard"), survTimes = NULL, 
  main = "", 
  ask = prod(par("mfcol")) < length(which) && dev.interactive(), 
  ..., ids = NULL, add.smooth = getOption("add.smooth"), 
  add.qqline = TRUE, add.KM = FALSE, cex.caption = 1, return = FALSE)

Arguments

x: an object inheriting from class jointModel.
which: which types of plots to produce, specify a subset of the numbers 1:10.
caption: captions to appear above the plots defined by argument which.
survTimes: a vector of survival times for which the survival, cumulative hazard or log cumulative hazard will be computed. Default is seq(minT, maxT, length = 15), where minT and maxT are the minimum and maximum observed survival times, respectively.
main: a character string specifying the title in the plot.
ask: logical; if TRUE, the user is asked before each plot, see par(ask=.).
...: other parameters to be passed through to plotting functions.
ids: a numeric vector specifying which subjects, the subject-specific plots will include; default is all subjects.
add.smooth: logical; if TRUE a smooth line is superimposed in the "Residuals vs Fitted" plot.
add.qqline: logical; if TRUE a qq-line is superimposed in the "Normal Q-Q" plot.
add.KM: logical; if TRUE the Kaplan-Meier estimate of the survival function is superimposed in the "Marginal Survival" plot.
cex.caption: magnification of captions.
return: logical; if TRUE and which takes in values in c(3:5, 8:10), then the values used to create the plot are returned.

Author

Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

References

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2010) JM: An R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software 35 (9), 1--33. tools:::Rd_expr_doi("10.18637/jss.v035.i09")

Examples

Run this code

if (FALSE) {
# linear mixed model fit
fitLME <- lme(log(serBilir) ~ drug * year, random = ~ 1 | id, data = pbc2)
# survival regression fit
fitSURV <- survreg(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
# joint model fit, under the (default) Weibull model
fitJOINT <- jointModel(fitLME, fitSURV, timeVar = "year")

plot(fitJOINT, 3, add.KM = TRUE, col = "red", lwd = 2)

par(mfrow = c(2, 2))
plot(fitJOINT)
}