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).