vdplot: Plot variable dependence using an oblique random survival...

In obliqueRSF: Oblique Random Forests for Right-Censored Time-to-Event Data

Plot variable dependence using an oblique random survival forest

Description

Plot variable dependence using an oblique random survival forest

Usage

vdplot(
  object,
  xvar,
  include.hist = TRUE,
  include.points = FALSE,
  ptsize = 0.75,
  ytype = "nonevent",
  event_lab = "death",
  nonevent_lab = "survival",
  fvar = NULL,
  flab = NULL,
  time_units = "years",
  xlab = xvar,
  xvar_units = NULL,
  xlvls = NULL,
  sub_times = NULL,
  se.show = FALSE
)

Arguments

object	an ORSF object (i.e. object returned from the ORSF function)
xvar	a string giving the name of the x-axis variable
include.hist	if true, a histogram showing the distribution of values for the x-axis variable will be included at the bottom of the plot.
include.points	if true, the predictions for each observation are plotted along with a smoothed population estimate. Note that points are always included if xvar is categorical.
ptsize	only relevant if include.points = TRUE. The size of the points in the plot are determined by this numeric value.
ytype	String. Use 'event' if you would like to plot the probability of the event, and 'nonevent' if you prefer to plot the probability of a non-event.
event_lab	string that describes the event
nonevent_lab	string that describes a non-event.
fvar	(optional) a string indicating a variable to facet the plot with
flab	the labels to be printed describing the facet variable. For a facet variable with k categories, flab should be a vector with k labels, given in the same order as the levels of the facet variable.
time_units	the unit of time, e.g. days, since baseline.
xlab	the label to be printed describing the x-axis variable
xvar_units	the unit of measurement for the x-axis variable. For example, age is usually measured in years.
xlvls	a character vector giving the labels that correspond to categorical xvar. This does not need to be specified if xvar is continuous.
sub_times	the times you would like to plot predicted values for. If left unspecified, the ORSF function will use all of the times in oob_times.
se.show	if true, standard errors of the population estimate will be included in the plot.

Value

A ggplot2 object

Examples

## Not run: 
data("pbc",package='survival')
pbc$status[pbc$status>=1]=pbc$status[pbc$status>=1]-1
pbc$time=pbc$time/365.25
pbc$id=NULL
fctrs<-c('trt','ascites','spiders','edema','hepato','stage')
for(f in fctrs)pbc[[f]]=as.factor(pbc[[f]])
pbc=na.omit(pbc)

orsf=ORSF(data=pbc, eval_time=5, ntree=30)

vdplot(object=orsf, xvar='bili', xlab='Bilirubin', xvar_units='mg/dl')

## End(Not run)

obliqueRSF documentation built on Aug. 29, 2022, 1:07 a.m.