riskRegression (version 2023.12.21)

plotCalibration: 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

if (FALSE) {
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)
}