plot.predict.stanjm {rstanarm} | R Documentation |
Plot the estimated subject-specific or marginal longitudinal trajectory
Description
This generic plot
method for predict.stanjm
objects will
plot the estimated subject-specific or marginal longitudinal trajectory
using the data frame returned by a call to posterior_traj
.
To ensure that enough data points are available to plot the longitudinal
trajectory, it is assumed that the call to posterior_traj
would have used the default interpolate = TRUE
, and perhaps also
extrapolate = TRUE
(the latter being optional, depending on
whether or not the user wants to see extrapolation of the longitudinal
trajectory beyond the last observation time).
Usage
## S3 method for class 'predict.stanjm'
plot(
x,
ids = NULL,
limits = c("ci", "pi", "none"),
xlab = NULL,
ylab = NULL,
vline = FALSE,
plot_observed = FALSE,
facet_scales = "free_x",
ci_geom_args = NULL,
grp_overlay = FALSE,
...
)
Arguments
x |
A data frame and object of class |
ids |
An optional vector providing a subset of subject IDs for whom the predicted curves should be plotted. |
limits |
A quoted character string specifying the type of limits to
include in the plot. Can be one of: |
xlab , ylab |
An optional axis label passed to
|
vline |
A logical. If |
plot_observed |
A logical. If |
facet_scales |
A character string passed to the |
ci_geom_args |
Optional arguments passed to
|
grp_overlay |
Only relevant if the model had lower level units
clustered within an individual. If |
... |
Optional arguments passed to
|
Value
A ggplot
object, also of class plot.predict.stanjm
.
This object can be further customised using the ggplot2 package.
It can also be passed to the function plot_stack_jm
.
See Also
posterior_traj
, plot_stack_jm
,
posterior_survfit
, plot.survfit.stanjm
Examples
if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
# Run example model if not already loaded
if (!exists("example_jm")) example(example_jm)
# For a subset of individuals in the estimation dataset we will
# obtain subject-specific predictions for the longitudinal submodel
# at evenly spaced times between 0 and their event or censoring time.
pt1 <- posterior_traj(example_jm, ids = c(7,13,15), interpolate = TRUE)
plot(pt1) # credible interval for mean response
plot(pt1, limits = "pi") # prediction interval for raw response
plot(pt1, limits = "none") # no uncertainty interval
# We can also extrapolate the longitudinal trajectories.
pt2 <- posterior_traj(example_jm, ids = c(7,13,15), interpolate = TRUE,
extrapolate = TRUE)
plot(pt2)
plot(pt2, vline = TRUE) # add line indicating event or censoring time
plot(pt2, vline = TRUE, plot_observed = TRUE) # overlay observed longitudinal data
# We can change or add attributes to the plot
plot1 <- plot(pt2, ids = c(7,13,15), xlab = "Follow up time",
vline = TRUE, plot_observed = TRUE,
facet_scales = "fixed", color = "blue", linetype = 2,
ci_geom_args = list(fill = "red"))
plot1
# Since the returned plot is also a ggplot object, we can
# modify some of its attributes after it has been returned
plot1 +
ggplot2::theme(strip.background = ggplot2::element_blank()) +
ggplot2::labs(title = "Some plotted longitudinal trajectories")
}