Plot effects from dynamic path analysis along with bootstrap confidence bands

plotting method for class "effect"

Usage

ggplot.effect(
  object,
  relative = FALSE,
  titles = NULL,
  x_label = "Time",
  y_label = NULL
)

Arguments

object: object of class "effect", or list of objects of class "effect"
relative: should the effect be plotted on a relative survival scale (i.e. `y=exp(-effect)`)?. Defaults to FALSE.
titles: If NULL, function will automatically generate. Otherwise character vector of length equal to number of elements in object list
x_label: Label for x-axis. Defaults to "Time"
y_label: Label for y-axis. Default when object scale is "cumulative" will be "Cumulative Effect" (relative=FALSE) and "Relative survival" (relative=TRUE). If object scale is "identity" then the default y_label will be "Effect".

Value

ggplot object

Examples

library(dpasurv)

data(simdata)

set.seed(1)

# Perform dynamic path analysis:
# We set boot.n=30 for the example to run fast, should be set large enough
# so that results don't change meaningfully for different seeds.
s <- dpa(Surv(start,stop,event)~M+x, list(M~x), id="subject", data=simdata, boot.n=30)

direct <- effect(x ~ outcome, s)
indirect <- effect(x ~ M ~ outcome, s)
total <- sum(direct, indirect)

ggplot.effect(direct)

ggplot.effect(list(direct, indirect, total))