Plot a calibration curve for a Cox Proportional Hazards model
valProbSurvival(
fit,
valdata,
alpha = 0.05,
timeHorizon = 5,
nk = 3,
plotCal = c("none", "base", "ggplot"),
addCox = FALSE,
addRCS = TRUE,
CL.cox = c("fill", "line"),
CL.rcs = c("fill", "line"),
xlab = "Predicted probability",
ylab = "Observed proportion",
xlim = c(-0.02, 1),
ylim = c(-0.15, 1),
lty.ideal = 1,
col.ideal = "red",
lwd.ideal = 1,
lty.cox = 1,
col.cox = "grey",
lwd.cox = 1,
fill.cox = "lightgrey",
lty.rcs = 1,
col.rcs = "black",
lwd.rcs = 1,
fill.rcs = rgb(177, 177, 177, 177, maxColorValue = 255),
riskdist = "predicted",
d0lab = "0",
d1lab = "1",
size.d01 = 5,
dist.label = 0.01,
line.bins = -0.05,
dist.label2 = 0.04,
length.seg = 0.85,
legendloc = c(0.5, 0.27)
)An object of type SurvivalCalibrationCurves with the following slots:
the matched call.
a list containing performance measures of calibration.
the significance level used.
contains the estimated calibration slope, together with their confidence intervals.
The coordinates for plotting the calibration curves.
the model fit, has to be of type coxph
the validation data set
the significance level
the time point at which the predictions have to be evaluated
the number of knots, for the restricted cubic splines fit
indicates if and how the calibration curve has to be plotted.
plotCal = "none" plots no calibration curve, plotCal = "base" plots
the calibration curve using base R (see plot) and plotCal = "ggplot"
creates a plot using ggplot
logical, indicates if the Cox's estimated calibration curve has to be added to the plot
logical, indicates if the restricted cubic splines' (RCS) estimated calibration curve has to be added to the plot
"fill" shows pointwise 95% confidence limits for the Cox calibration curve with a gray
area between the lower and upper limits and "line" shows the confidence limits with a dotted line
"fill" shows pointwise 95% confidence limits for the RCS calibration curve with a gray
area between the lower and upper limits and "line" shows the confidence limits with a dotted line
x-axis label, default is "Predicted Probability".
y-axis label, default is "Observed proportion".
numeric vectors of length 2, giving the x and y coordinates ranges (see plot.window)
linetype of the ideal line. Default is 1.
controls the color of the ideal line on the plot. Default is "red".
controls the line width of the ideal line on the plot. Default is 1.
if addCox = TRUE, the linetype of the Cox calibration curve
if addCox = TRUE, the color of the Cox calibration curve
if addCox = TRUE, the linewidth of the Cox calibration curve
if addCox = TRUE and CL.cox = "fill", the fill of the Cox calibration curve
if addRCS = TRUE, the linetype of the RCS calibration curve
if addRCS = TRUE, the color of the RCS calibration curve
if addRCS = TRUE, the linewidth of the RCS calibration curve
if addRCS = TRUE and CL.rcs = "fill", the fill of the RCS calibration curve
Use "calibrated" to plot the relative frequency distribution of
calibrated probabilities after dividing into 101 bins from lim[1] to
lim[2].
Set to "predicted" (the default as of rms 4.5-1) to use raw assigned risk, FALSE to omit risk distribution.
Values are scaled so that highest bar is 0.15*(lim[2]-lim[1]).
controls the labels for events and non-events (i.e. outcome y) for the histograms.
Defaults are d1lab="1" for events and d0lab="0" for non-events.
controls the size of the labels for events and non-events. Default is 5 and
this value is multiplied by 0.25 when plotCal = "base".
controls the horizontal position of the labels for events and non-events. Default is 0.04.
controls the horizontal (y-axis) position of the histograms. Default is -0.05.
controls the vertical distance between the labels for events and non-events. Default is 0.03.
controls the length of the histogram lines. Default is 1.
if pl=TRUE, list with components x,y or vector c(x,y) for bottom right corner of legend for
curves and points. Default is c(.50, .27) scaled to lim. Use locator(1) to use the mouse, FALSE to suppress legend.
van Geloven N, Giardiello D, Bonneville E F, Teece L, Ramspek C L, van Smeden M et al. (2022). Validation of prediction models in the presence of competing risks: a guide through modern methods. BMJ, 377:e069249, doi:10.1136/bmj-2021-069249
if (FALSE) {
library(CalibrationCurves)
data(trainDataSurvival)
data(testDataSurvival)
sFit = coxph(Surv(ryear, rfs) ~ csize + cnode + grade3, data = trainDataSurvival,
x = TRUE, y = TRUE)
calPerf = valProbSurvival(sFit, gbsg5, plotCal = "base", nk = 5)
}
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