Description Usage Arguments Details Value Author(s) References Examples
Calibration plots for risk prediction models in for a binary endpoint
1 2 3 4 5 6 7 | calPlot2(object, formula, data, splitMethod = "none", B = 1, M, showY,
method = "nne", round = TRUE, bandwidth = NULL, q = 10,
density = 55, add = FALSE, diag = !add, legend = !add, axes = !add,
xlim, ylim, xlab = "Predicted event probability",
ylab = "Observed proportion", col, lwd, lty, pch, cause = 1,
percent = TRUE, giveToModel = NULL, na.action = na.fail, cores = 1,
verbose = FALSE, ...)
|
object |
A named list of prediction models, where
allowed entries are (1) R-objects for which a
predictStatusProb method exists (see details), (2)
a |
formula |
A survival or event history formula. The
left hand side is used to compute the expected event
status. If |
data |
A data frame in which to validate the
prediction models and to fit the censoring model. If
|
splitMethod |
Defines the internal validation design:
|
B |
The number of cross-validation steps. |
M |
The size of the subsamples for cross-validation. |
showY |
If |
method |
The method for estimating the calibration curve(s):
|
round |
If |
bandwidth |
The bandwidth for |
q |
The number of quantiles for
|
density |
Gray scale for observations. |
add |
If |
diag |
If |
legend |
If |
axes |
If |
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 |
lwd |
Vector with line widths, one for each element
of object. Passed to |
lty |
lwd Vector with line style, one for each
element of object. Passed to |
pch |
Passed to |
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 |
na.action |
Passed to |
cores |
Number of cores for parallel computing.
Passed as the value of the argument |
verbose |
if |
... |
Used to control the subroutines: plot, axis,
lines, legend. See |
For method "nne" the optimal bandwidth with respect to is
obtained with the function dpik
from the
package KernSmooth
for a box kernel function.
list with elements: time, Frame and bandwidth (NULL for method quantile).
Thomas Alexander Gerds
TA Gerds, PA Andersen, and Kattan MW. Calibration plots for risk prediction models in the presence of competing risks. Statistics in Medicine, page to appear, 2014.
1 2 3 4 5 6 7 8 9 10 11 | set.seed(40)
N=40
Y=rbinom(N,1,.5)
X1=rnorm(N)
X1[Y==1]=rnorm(sum(Y==1),mean=rbinom(sum(Y==1),1,.5))
X2=rnorm(N)
X2[Y==0]=rnorm(sum(Y==0),mean=rbinom(sum(Y==0),3,.5))
dat <- data.frame(Y=Y,X1=X1,X2=X2)
lm1 <- glm(Y~X1,data=dat,family="binomial")
lm2 <- glm(Y~X2,data=dat,family="binomial")
calPlot2(list(lm1,lm2),data=dat)
|
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