forest_plot.cumrisk.Rdforest_plot.cumrisk is a function that takes as an argument a list of cumrisk
objects, or the objects provided as independent arguments, each representing a
set of cumulative incidence functions. The
function returns a ggplot2 object containing a forest plot of the risk differences.
This function is
also dispatched by calls to the generic forest_plot function when passed a
cumrisk object.
# S3 method for cumrisk
forest_plot(
...,
risk_time = NULL,
effect_measure_type = NULL,
alpha = 0.05,
legend_title = "Treatment \nGroups",
colors = "Dark2",
ref = 1,
boot_method = "normal"
)cumrisk objects supplied as a list or as seperate arguments
The desired time at which to extract the the cumulative risk and risk difference. If null, uses the maximum time in the ipwrisk object.
The type of effect to plot (default NULL = plots cumulative risk funtions)
The desired nominal significance level of the confidence intervals.
A string used to title the legend, default is "Treatment Groups"
Set colors for resulting ggplot object. Either a vector of strings which are interpreted as RGB codes or a sinlge string that identifies a color brewer palette. If set to NULL, plot.cumrisk will use grey shades. As a default, plot.cumrisk uses the color brewer palette 'Dark2'.
Identifies the treatmet group in the supplied object(s) to be used as a referent category for risk differecence curves. Defaults to one.
The specific bootstrap approach used to compute confidence
intervals, for either the cumulative risk or count estimates or the effect
measures. Options are "normal" (default; normal approximation on the
original scale), "log-normal" (normal approximation on the log scale),
"percentile" (percentile-based approach using quantiles of the bootstrap
distribution), and "basic" (basic bootstrap using the reflection method).
When a given input results object was created without using bootstrap
estimation then the value of boot_method is ignored. Note that when a
given input results object was created using bootstrap estimation and
boot_method is "log-normal", then only effect measures specified via
effect_measure_type with support on the positive real line can be used.
A ggplot2 object that can be further modified by the user before
plotting.