plot.dynSurv  R Documentation 
dynSurv
objectPlots the conditional timetoevent distribution for a
new subject calculated using the dynSurv
function.
## S3 method for class 'dynSurv' plot( x, main = NULL, xlab = NULL, ylab1 = NULL, ylab2 = NULL, grid = TRUE, estimator, smooth = FALSE, ... )
x 
an object of class 
main 
an overall title for the plot: see 
xlab 
a title for the x [time] axis: see 
ylab1 
a character vector of the titles for the K longitudinal
outcomes yaxes: see 
ylab2 
a title for the eventtime outcome axis: see

grid 
adds a rectangular grid to an existing plot: see

estimator 
a character string that can take values 
smooth 
logical: whether to overlay a smooth survival curve (see
Details). Defaults to 
... 
additional plotting arguments; currently limited to 
The joineRML
package is based on a semiparametric model,
such that the baseline hazards function is left unspecified. For
prediction, it might be preferable to have a smooth survival curve. Rather
than changing modelling framework a prior, a constrained Bsplines
nonparametric median quantile curve is estimated using
cobs
, with a penalty function of λ=1, and
subject to constraints of monotonicity and S(t)=1.
A dynamic prediction plot.
Graeme L. Hickey (graemeleehickey@gmail.com)
Ng P, Maechler M. A fast and efficient implementation of qualitatively constrained quantile smoothing splines. Statistical Modelling. 2007; 7(4): 315328.
Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and timetoevent data. Biometrics. 2011; 67: 819–829.
dynSurv
## Not run: # Fit a joint model with bivariate longitudinal outcomes data(heart.valve) hvd < heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ] fit2 < mjoint( formLongFixed = list("grad" = log.grad ~ time + sex + hs, "lvmi" = log.lvmi ~ time + sex), formLongRandom = list("grad" = ~ 1  num, "lvmi" = ~ time  num), formSurv = Surv(fuyrs, status) ~ age, data = list(hvd, hvd), inits = list("gamma" = c(0.11, 1.51, 0.80)), timeVar = "time", verbose = TRUE) hvd2 < droplevels(hvd[hvd$num == 1, ]) out1 < dynSurv(fit2, hvd2) plot(out1, main = "Patient 1") ## End(Not run) ## Not run: # Monte Carlo simulation with 95% confidence intervals on plot out2 < dynSurv(fit2, hvd2, type = "simulated", M = 200) plot(out2, main = "Patient 1") ## End(Not run)
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