ggcalibrate_original plots the stats::predicted events against the actual event rate using the "old" form.
Usage
ggcalibrate_original(
x1,
x2 = NULL,
y = NULL,
n_cut = 5,
cut_type = c("interval", "number", "width"),
include_margin = FALSE
)Arguments
- x1
Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the baseline model. Must be between 0 & 1
- x2
Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the new (alternative) model. Must be between 0 & 1
- y
Binary of outcome of interest. Must be 0 or 1 (if fitted models are provided this is extracted from the fit which for an rms fit must have x = TRUE, y = TRUE).
- n_cut
An integer indicating either the number of intervals of the same width, the number of intervals of the same number of subjects, or the width (as a percentage) of the intervals.
- cut_type
One of three strings: "interval", "number", or "width". - "interval": uses cut_interval() to get n_cut intervals of approximately equal width. - "number": uses cut_number() to get n_cut intervals with approximately equal counts. - "width": uses cut_width() to get intervals of a fixed width (approximately 100/n_cut).
- include_margin
TRUE for including producing a bar plot of the counts of in each of the intervals. Default is FALSE. Note if the output is saved to my_graphs then using the library gridExtra the function grid.arrange(graphs$g, graphs$g_marg , nrow = 2, heights = c(2,1)) will produce a plot with both the calibration plot and the marginal plot.
Examples
if (FALSE) { # \dontrun{
data(data_risk)
y<-data_risk$outcome
x1<-data_risk$baseline
x2<-data_risk$new
#e.g.
output <- ggcalibrate(
x1, x2 = NULL, y = NULL,
n_cut = 5, cut_type = "interval",
include_margin = FALSE
)
} # }