plotCalibration: Plot Calibration curve

In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

Plot Calibration curve

Description

Plot Calibration curves for risk prediction models

Usage

plotCalibration(
  x,
  models,
  times,
  method = "nne",
  cens.method,
  round = TRUE,
  bandwidth = NULL,
  q = 10,
  bars = FALSE,
  hanging = FALSE,
  names = "quantiles",
  pseudo = FALSE,
  rug,
  show.frequencies = FALSE,
  plot = TRUE,
  add = FALSE,
  diag = !add,
  legend = !add,
  auc.in.legend,
  brier.in.legend,
  axes = !add,
  xlim = c(0, 1),
  ylim = c(0, 1),
  xlab = ifelse(bars, "Risk groups", "Predicted risk"),
  ylab,
  col,
  lwd,
  lty,
  pch,
  type,
  percent = TRUE,
  na.action = na.fail,
  cex = 1,
  ...
)

Arguments

x	Object obtained with function Score
models	Choice of models to plot
times	Time point specifying the prediction horizon.
method	The method for estimating the calibration curve(s): "quantile"The observed proportion at predicted risk value 'p' is obtained in groups defined by quantiles of the predicted event probabilities of all subjects. The number of groups is controlled by argument q. "nne": The observed proportion at predicted risk value 'p' is obtained based on the subjects whose predicted risk is inside a nearest neighborhood around the value 'p'. The larger the bandwidth the more subjects are included in the current neighborhood.
cens.method	For right censored data only. How observed proportions are calculated. Either "jackknife" or "local": "jackknife"Compute a running mean of the jackknife pseudovalues across neighborhoods/groups of the predicted risks. Here we rely on the assumption that censoring is independent of the event time and the covariates, see References. "local"Compute the Kaplan-Meier estimator in absence of competing risks and the Aalen-Johansen estimator in presence of competing risks locally like a running mean in neighborhoods of the predicted risks. The widths of the neighborhoods are defined according to method.
round	If TRUE predicted probabilities are rounded to two digits before smoothing. This may have a considerable effect on computing efficiency in large data sets.
bandwidth	The bandwidth for method="nne"
q	The number of quantiles for method="quantile" and bars=TRUE.
bars	If TRUE, use barplots to show calibration.
hanging	Barplots only. If TRUE, hang bars corresponding to observed frequencies (estimated actual risk) at the value of the corresponding prediction.
names	Barplots only. Names argument passed to names.arg of barplot.
pseudo	If TRUE show pseudo values (only for right censored data).
rug	If TRUE show rug plot at the predictions
show.frequencies	Barplots only. If TRUE, show frequencies above the bars.
plot	If FALSE, do not plot the results, just return a plottable object.
add	If TRUE the line(s) are added to an existing plot.
diag	If FALSE no diagonal line is drawn.
legend	Logical. If TRUE draw legend.
auc.in.legend	Logical. If TRUE add AUC to legend.
brier.in.legend	Logical. If TRUE add Brier score to legend.
axes	If FALSE no axes are drawn.
xlim	Limits of x-axis.
ylim	Limits of y-axis.
xlab	Label for y-axis.
ylab	Label for x-axis.
col	Vector with colors, one for each element of object. Passed to lines.
lwd	Vector with line widths, one for each element of object. Passed to lines.
lty	lwd Vector with line style, one for each element of object. Passed to lines.
pch	Passed to lines.
type	Passed to lines.
percent	If TRUE axes labels are multiplied by 100 and thus interpretable on a percent scale.
na.action	what to do with NA values. Passed to model.frame
cex	Default cex used for legend and labels.
...	Used to control the subroutines: plot, axis, lines, barplot, legend, addtable2plot, points (pseudo values), rug. See SmartControl.

Details

In uncensored data, the observed frequency of the outcome event is calculated locally at the predicted risk. In right censored data with and without competing risks, the actual risk is calculated using the Kaplan-Meier and the Aalen-Johansen method, respectively, locally at the predicted risk.

Examples

library(prodlim)
# binary
set.seed(10)
db=sampleData(100,outcome="binary")
fb1=glm(Y~X1+X5+X7,data=db,family="binomial")
fb2=glm(Y~X1+X3+X6+X7,data=db,family="binomial")
xb=Score(list(model1=fb1,model2=fb2),Y~1,data=db,
          plots="cal")
plotCalibration(xb,brier.in.legend=TRUE)
plotCalibration(xb,bars=TRUE,model="model1")
plotCalibration(xb,models=1,bars=TRUE,names.cex=1.3)

# survival
library(survival)
library(prodlim)
dslearn=sampleData(56,outcome="survival")
dstest=sampleData(100,outcome="survival")
fs1=coxph(Surv(time,event)~X1+X5+X7,data=dslearn,x=1)
fs2=coxph(Surv(time,event)~strata(X1)+X3+X6+X7,data=dslearn,x=1)
xs=Score(list(Cox1=fs1,Cox2=fs2),Surv(time,event)~1,data=dstest,
          plots="cal",metrics=NULL)
plotCalibration(xs)
plotCalibration(xs,cens.method="local",pseudo=1)
plotCalibration(xs,method="quantile")


# competing risks

## Not run: 
data(Melanoma)
f1 <- CSC(Hist(time,status)~age+sex+epicel+ulcer,data=Melanoma)
f2 <- CSC(Hist(time,status)~age+sex+logthick+epicel+ulcer,data=Melanoma)
x <- Score(list(model1=f1,model2=f2),Hist(time,status)~1,data=Melanoma,
           cause= 2,times=5*365.25,plots="cal")
plotCalibration(x)

## End(Not run)

riskRegression documentation built on Sept. 8, 2023, 6:12 p.m.