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pec

calPlot

Calibration plots for right censored data

Description

Calibration plots for risk prediction models in right censored survival and competing risks data

Usage

calPlot(
  object,
  time,
  formula,
  data,
  splitMethod = "none",
  B = 1,
  M,
  pseudo,
  type,
  showPseudo,
  pseudo.col = NULL,
  pseudo.pch = NULL,
  method = "nne",
  round = TRUE,
  bandwidth = NULL,
  q = 10,
  bars = FALSE,
  hanging = FALSE,
  names = "quantiles",
  showFrequencies = FALSE,
  jack.density = 55,
  plot = TRUE,
  add = FALSE,
  diag = !add,
  legend = !add,
  axes = !add,
  xlim = c(0, 1),
  ylim = c(0, 1),
  xlab,
  ylab,
  col,
  lwd,
  lty,
  pch,
  cause = 1,
  percent = TRUE,
  giveToModel = NULL,
  na.action = na.fail,
  cores = 1,
  verbose = FALSE,
  cex = 1,
  ...
)

Arguments

`object`	A named list of prediction models, where allowed entries are (1) R-objects for which a predictSurvProb method exists (see details), (2) a `call` that evaluates to such an R-object (see examples), (3) a matrix with predicted probabilities having as many rows as `data` and as many columns as `times`. For cross-validation all objects in this list must include their `call`.
`time`	The evaluation time point at predicted event probabilities are plotted against pseudo-observed event status.
`formula`	A survival or event history formula. The left hand side is used to compute the expected event status. If `formula` is `missing`, try to extract a formula from the first element in object.
`data`	A data frame in which to validate the prediction models and to fit the censoring model. If `data` is missing, try to extract a data set from the first element in object.
`splitMethod`	Defines the internal validation design: `none/noPlan`: Assess the models in the give `data`, usually either in the same data where they are fitted, or in independent test data. `BootCv`: Bootstrap cross validation. The prediction models are trained on `B` bootstrap samples, that are either drawn with replacement of the same size as the original data or without replacement from `data` of the size `M`. The models are assessed in the observations that are NOT in the bootstrap sample.
`B`	The number of cross-validation steps.
`M`	The size of the subsamples for cross-validation.
`pseudo`	Logical. Determines the method for estimating expected event status: `TRUE`: Use average pseudo-values. `FALSE`: Use the product-limit estimate, i.e., apply the Kaplan-Meier method for right censored survival and the Aalen-Johansen method for right censored competing risks data.
`type`	Either "risk" or "survival".
`showPseudo`	If `TRUE` the pseudo-values are shown as dots on the plot (only when `pseudo=TRUE`).
`pseudo.col`	Colour for pseudo-values.
`pseudo.pch`	Dot type (see par) for pseudo-values.
`method`	The method for estimating the calibration curve(s): `"nne"`: The expected event status is obtained in the nearest neighborhood around the predicted event probabilities. `"quantile"`: The expected event status is obtained in groups defined by quantiles of the predicted event probabilities.
`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 at the value of the corresponding prediction.
`names`	Barplots only. Names argument passed to `names.arg` of `barplot`.
`showFrequencies`	Barplots only. If `TRUE`, show frequencies above the bars.
`jack.density`	Gray scale for pseudo-observations.
`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`	If `FALSE` no legend is drawn.
`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 `points`.
`cause`	For competing risks models, the cause of failure or event of interest
`percent`	If TRUE axes labels are multiplied by 100 and thus interpretable on a percent scale.
`giveToModel`	List of with exactly one entry for each entry in `object`. Each entry names parts of the value of the fitted models that should be extracted and added to the value.
`na.action`	Passed to `model.frame`
`cores`	Number of cores for parallel computing. Passed as value of argument `mc.cores` to `mclapply`.
`verbose`	if `TRUE` report details of the progress, e.g. count the steps in cross-validation.
`cex`	Default cex used for legend and labels.
`...`	Used to control the subroutines: plot, axis, lines, barplot, legend. See `SmartControl`.

Details

For method "nne" the optimal bandwidth with respect to is obtained with the function dpik from the package KernSmooth for a box kernel function.

Value

list with elements: time, pseudoFrame and bandwidth (NULL for method quantile).

Author(s)

Thomas Alexander Gerds tag@biostat.ku.dk

Examples

library(prodlim)
library(lava)
library(riskRegression)
library(survival)
# survival
dlearn <- SimSurv(40)
dval <- SimSurv(100)
f <- coxph(Surv(time,status)~X1+X2,data=dlearn,x=TRUE,y=TRUE)
cf=calPlot(f,time=3,data=dval)
print(cf)
plot(cf)

g <- coxph(Surv(time,status)~X2,data=dlearn,x=TRUE,y=TRUE)
cf2=calPlot(list("Cox regression X1+X2"=f,"Cox regression X2"=g),
    time=3,
    type="risk",
    data=dval)
print(cf2)
plot(cf2)
calPlot(f,time=3,data=dval,type="survival")
calPlot(f,time=3,data=dval,bars=TRUE,pseudo=FALSE)
calPlot(f,time=3,data=dval,bars=TRUE,type="risk",pseudo=FALSE)

## show a red line which follows the hanging bars
calPlot(f,time=3,data=dval,bars=TRUE,hanging=TRUE)
a <- calPlot(f,time=3,data=dval,bars=TRUE,hanging=TRUE,abline.col=NULL)
lines(c(0,1,ceiling(a$xcoord)),
      c(a$offset[1],a$offset,a$offset[length(a$offset)]),
      col=2,lwd=5,type="s")

calPlot(f,time=3,data=dval,bars=TRUE,type="risk",hanging=TRUE)

set.seed(13)
m <- crModel()
regression(m, from = "X1", to = "eventtime1") <- 1
regression(m, from = "X2", to = "eventtime1") <- 1
m <- addvar(m,c("X3","X4","X5"))
distribution(m, "X1") <- binomial.lvm()
distribution(m, "X4") <- binomial.lvm()
d1 <- sim(m,100)
d2 <- sim(m,100)
csc <- CSC(Hist(time,event)~X1+X2+X3+X4+X5,data=d1)
fgr <- FGR(Hist(time,event)~X1+X2+X3+X4+X5,data=d1,cause=1)
if ((requireNamespace("cmprsk",quietly=TRUE))){
predict.crr <- cmprsk:::predict.crr
cf3=calPlot(list("Cause-specific Cox"=csc,"Fine-Gray"=fgr),
        time=5,
        legend.x=-0.3,
        legend.y=1.35,
        ylab="Observed event status",
        legend.legend=c("Cause-specific Cox regression","Fine-Gray regression"),
        legend.xpd=NA)
print(cf3)
plot(cf3)

b1 <- calPlot(list("Fine-Gray"=fgr),time=5,bars=TRUE,hanging=FALSE)
print(b1)
plot(b1)

calPlot(fgr,time=5,bars=TRUE,hanging=TRUE)
}

pec

Prediction Error Curves for Risk Prediction Models in Survival Analysis

v2020.11.17

GPL (>= 2)

Authors

Thomas A. Gerds

Initial release