Description Usage Arguments Author(s) References See Also Examples
Produces plots of conditional probabilities of survival.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ## S3 method for class 'survfit.JMbayes'
plot(x, estimator = c("both", "mean", "median"),
which = NULL, fun = NULL, invlink = NULL, conf.int = FALSE,
fill.area = FALSE, col.area = "grey", col.abline = "black", col.points = "black",
add.last.time.axis.tick = FALSE, include.y = FALSE, main = NULL,
xlab = NULL, ylab = NULL, ylab2 = NULL, lty = NULL, col = NULL,
lwd = NULL, pch = NULL, ask = NULL, legend = FALSE, ...,
cex.axis.z = 1, cex.lab.z = 1, xlim = NULL)
## S3 method for class 'survfit.mvJMbayes'
plot(x, split = c(1, 1), which_subjects = NULL,
which_outcomes = NULL, surv_in_all = TRUE, include.y = TRUE, fun = NULL,
abline = NULL,
main = NULL, xlab = "Time", ylab = NULL, zlab = "EventFree Probability",
include_CI = TRUE, fill_area_CI = TRUE, col_points = "black",
pch_points = 1, col_lines = "red", col_lines_CI = "black",
col_fill_CI = "lightgrey", lwd_lines = 2, lty_lines_CI = 2,
cex_xlab = 1, cex_ylab = 1, cex_zlab = 1, cex_main = 1,
cex_axis = 1, ...)

x 
an object inheriting from class 
estimator 
character string specifying, whether to include in the plot the mean of
the conditional probabilities of survival, the median or both. The mean and median are
taken as estimates of these conditional probabilities over the M replications of the
Monte Carlo scheme described in 
which 
an integer or character vector specifying for which subjects to produce the
plot. If a character vector, then is should contain a subset of the values of the

which_subjects 
an integer vector specifying for which subjects to produce the plot. 
split 
a integer vector of length 2 indicating in how many panels to construct, i.e., number of rows and number of columns. 
which_outcomes 
integer vector indicating which longitudinal outcomes to include in the plot. 
surv_in_all 
logical; should the survival function be included in all panels. 
fun 
a vectorized function defining a transformation of the survival curve. For
example, with 
abline 
a list with arguments to 
invlink 
a function to transform the fitted values of the longitudinal outcome. 
conf.int, include_CI 
logical; if 
fill.area, fill_area_CI 
logical; if 
col.area, col_fill_CI 
the color of the area defined by the confidence interval of the survival function. 
col.abline, col.points, col_points, col_lines, col_lines_CI 
the color for the
vertical line and the points when 
add.last.time.axis.tick 
logical; if 
include.y 
logical; if 
main 
a character string specifying the title in the plot. 
xlab 
a character string specifying the xaxis label in the plot. 
ylab 
a character string specifying the yaxis label in the plot. 
ylab2 
a character string specifying the yaxis label in the plot,
when 
zlab 
a character string specifying the zaxis (vertical righthand side) label in the plot. 
lty, lty_lines_CI 
what types of lines to use. 
col 
which colors to use. 
lwd, lwd_lines 
the thickness of the lines. 
pch, pch_points 
the type of points to use. 
ask 
logical; if 
legend 
logical; if 
cex.axis.z, cex.lab.z 
the par 
cex_xlab, cex_ylab, cex_zlab, cex_main, cex_axis 
the par 
xlim 
the par 
... 
extra graphical parameters passed to 
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
Rizopoulos, D. (2016). The R package JMbayes for fitting joint models for longitudinal and timetoevent data using MCMC. Journal of Statistical Software 72(7), 1–45. doi:10.18637/jss.v072.i07.
Rizopoulos, D. (2012) Joint Models for Longitudinal and TimetoEvent Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.
Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and timetoevent data. Biometrics 67, 819–829.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  ## Not run:
# we construct the composite event indicator (transplantation or death)
pbc2$status2 < as.numeric(pbc2$status != "alive")
pbc2.id$status2 < as.numeric(pbc2.id$status != "alive")
# we fit the joint model using splines for the subjectspecific
# longitudinal trajectories and a splineapproximated baseline
# risk function
lmeFit < lme(log(serBilir) ~ ns(year, 2), data = pbc2,
random = ~ ns(year, 2)  id)
survFit < coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
jointFit < jointModelBayes(lmeFit, survFit, timeVar = "year")
# we will compute survival probabilities for Subject 2 in a dynamic manner,
# i.e., after each longitudinal measurement is recorded
ND < pbc2[pbc2$id == 2, ] # the data of Subject 2
survPreds < vector("list", nrow(ND))
for (i in 1:nrow(ND)) {
survPreds[[i]] < survfitJM(jointFit, newdata = ND[1:i, ])
}
# the default call to the plot method using the first three
# longitudinal measurements
plot(survPreds[[3]])
# we produce the corresponding plot
par(mfrow = c(2, 2), oma = c(0, 2, 0, 2))
for (i in c(1,3,5,7)) {
plot(survPreds[[i]], estimator = "median", conf.int = TRUE,
include.y = TRUE, main = paste("Followup time:",
round(survPreds[[i]]$last.time, 1)), ylab = "", ylab2 = "")
}
mtext("log serum bilirubin", side = 2, line = 1, outer = TRUE)
mtext("Survival Probability", side = 4, line = 1, outer = TRUE)
## End(Not run)

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