Uses bootstrapping or cross-validation to get bias-corrected (overfitting-
corrected) estimates of predicted vs. observed values based on
subsetting predictions into intervals (for survival models) or on
nonparametric smoothers (for other models). There are calibration
functions for Cox (`cph`

), parametric survival models (`psm`

),
binary and ordinal logistic models (`lrm`

) and ordinary least
squares (`ols`

). For survival models,
"predicted" means predicted survival probability at a single
time point, and "observed" refers to the corresponding Kaplan-Meier
survival estimate, stratifying on intervals of predicted survival, or,
if the `polspline`

package is installed, the predicted survival
probability as a function of transformed predicted survival probability
using the flexible hazard regression approach (see the `val.surv`

function for details). For logistic and linear models, a nonparametric
calibration curve is estimated over a sequence of predicted values. The
fit must have specified `x=TRUE, y=TRUE`

. The `print`

and
`plot`

methods for `lrm`

and `ols`

models (which use
`calibrate.default`

) print the mean absolute error in predictions,
the mean squared error, and the 0.9 quantile of the absolute error.
Here, error refers to the difference between the predicted values and
the corresponding bias-corrected calibrated values.

Below, the second, third, and fourth invocations of `calibrate`

are, respectively, for `ols`

and `lrm`

, `cph`

, and
`psm`

. The first and second `plot`

invocation are
respectively for `lrm`

and `ols`

fits or all other fits.

```
calibrate(fit, …)
# S3 method for default
calibrate(fit, predy,
method=c("boot","crossvalidation",".632","randomization"),
B=40, bw=FALSE, rule=c("aic","p"),
type=c("residual","individual"),
sls=.05, aics=0, force=NULL, estimates=TRUE, pr=FALSE, kint,
smoother="lowess", digits=NULL, …)
# S3 method for cph
calibrate(fit, cmethod=c('hare', 'KM'),
method="boot", u, m=150, pred, cuts, B=40,
bw=FALSE, rule="aic", type="residual", sls=0.05, aics=0, force=NULL,
estimates=TRUE,
pr=FALSE, what="observed-predicted", tol=1e-12, maxdim=5, …)
# S3 method for psm
calibrate(fit, cmethod=c('hare', 'KM'),
method="boot", u, m=150, pred, cuts, B=40,
bw=FALSE,rule="aic",
type="residual", sls=.05, aics=0, force=NULL, estimates=TRUE,
pr=FALSE, what="observed-predicted", tol=1e-12, maxiter=15,
rel.tolerance=1e-5, maxdim=5, …)
```# S3 method for calibrate
print(x, B=Inf, …)
# S3 method for calibrate.default
print(x, B=Inf, …)

# S3 method for calibrate
plot(x, xlab, ylab, subtitles=TRUE, conf.int=TRUE,
cex.subtitles=.75, riskdist=TRUE, add=FALSE,
scat1d.opts=list(nhistSpike=200), par.corrected=NULL, …)

# S3 method for calibrate.default
plot(x, xlab, ylab, xlim, ylim,
legend=TRUE, subtitles=TRUE, scat1d.opts=NULL, …)

fit

a fit from `ols`

, `lrm`

, `cph`

or `psm`

x

an object created by `calibrate`

method, B, bw, rule, type, sls, aics, force, estimates

see `validate`

.
For `print.calibrate`

, `B`

is an
upper limit on the number of resamples for which
information is printed about which variables were selected in each
model re-fit. Specify zero to suppress printing. Default is to print
all re-samples.

cmethod

method for validating survival predictions using
right-censored data. The default is `cmethod='hare'`

to use the
`hare`

function in the `polspline`

package. Specify
`cmethod='KM'`

to use less precision stratified Kaplan-Meier
estimates. If the `polspline`

package is not available, the
procedure reverts to `cmethod='KM'`

.

u

the time point for which to validate predictions for survival
models. For `cph`

fits, you must have specified ```
surv=TRUE,
time.inc=u
```

, where `u`

is the constant specifying the time to
predict.

m

group predicted `u`

-time units survival into intervals containing
`m`

subjects on the average (for survival models only)

pred

vector of predicted survival probabilities at which to evaluate the
calibration curve. By default, the low and high prediction values
from `datadist`

are used, which for large sample size is the 10th
smallest to the 10th largest predicted probability.

cuts

actual cut points for predicted survival probabilities. You may
specify only one of `m`

and `cuts`

(for survival models only)

pr

set to `TRUE`

to print intermediate results for each re-sample

what

The default is `"observed-predicted"`

, meaning to estimate optimism
in this difference. This is preferred as it accounts for skewed
distributions of predicted probabilities in outer intervals. You can
also specify `"observed"`

. This argument applies to survival models only.

tol

criterion for matrix singularity (default is `1e-12`

)

maxdim

see `hare`

maxiter

for `psm`

, this is passed to
`survreg.control`

(default is 15 iterations)

rel.tolerance

parameter passed to
`survreg.control`

for `psm`

(default is 1e-5).

predy

a scalar or vector of predicted values to calibrate (for `lrm`

,
`ols`

). Default is 50 equally spaced points between the 5th
smallest and the 5th largest predicted values. For `lrm`

the
predicted values are probabilities (see `kint`

).

kint

For an ordinal logistic model the default predicted
probability that \(Y\geq\) the middle level. Specify `kint`

to specify the
intercept to use, e.g., `kint=2`

means to calibrate \(Prob(Y\geq
b)\), where \(b\) is the second level of \(Y\).

smoother

a function in two variables which produces \(x\)- and
\(y\)-coordinates by smoothing the input `y`

. The default is to
use `lowess(x, y, iter=0)`

.

digits

If specified, predicted values are rounded to
`digits`

digits before passing to the smoother. Occasionally,
large predicted values on the logit scale will lead to predicted
probabilities very near 1 that should be treated as 1, and the
`round`

function will fix that. Applies to `calibrate.default`

.

…

other arguments to pass to `predab.resample`

, such as `group`

,
`cluster`

, and `subset`

.
Also, other arguments for `plot`

.

xlab

defaults to "Predicted x-units Survival" or to a suitable label for other models

ylab

defaults to "Fraction Surviving x-units" or to a suitable label for other models

xlim,ylim

2-vectors specifying x- and y-axis limits, if not using defaults

subtitles

set to `FALSE`

to suppress subtitles in plot describing method and for `lrm`

and `ols`

the mean absolute error and original sample size

conf.int

set to `FALSE`

to suppress plotting 0.95 confidence intervals for
Kaplan-Meier estimates

cex.subtitles

character size for plotting subtitles

riskdist

set to `FALSE`

to suppress the distribution of
predicted risks (survival probabilities) from being plotted

add

set to `TRUE`

to add the calibration plot to an existing
plot

scat1d.opts

a list specifying options to send to `scat1d`

if
`riskdist=TRUE`

. See `scat1d`

.

par.corrected

a list specifying graphics parameters `col`

,
`lty`

, `lwd`

, `pch`

to be used in drawing
overfitting-corrected estimates. Default is `col="blue"`

,
`lty=1`

, `lwd=1`

, `pch=4`

.

legend

set to `FALSE`

to suppress legends (for `lrm`

, `ols`

only) on the calibration plot, or specify a list with elements `x`

and `y`

containing the coordinates of the upper left corner of the
legend. By default, a legend will be drawn in the lower right 1/16th of
the plot.

matrix specifying mean predicted survival in each interval, the
corresponding estimated bias-corrected Kaplan-Meier estimates,
number of subjects, and other statistics. For linear and logistic models,
the matrix instead has rows corresponding to the prediction points, and
the vector of predicted values being validated is returned as an attribute.
The returned object has class `"calibrate"`

or
`"calibrate.default"`

.
`plot.calibrate.default`

invisibly returns the vector of estimated
prediction errors corresponding to the dataset used to fit the model.

prints, and stores an object `pred.obs`

or `.orig.cal`

If the fit was created using penalized maximum likelihood estimation,
the same `penalty`

and `penalty.scale`

parameters are used during
validation.

`validate`

, `predab.resample`

,
`groupkm`

, `errbar`

,
`scat1d`

, `cph`

, `psm`

,
`lowess`

```
# NOT RUN {
set.seed(1)
n <- 200
d.time <- rexp(n)
x1 <- runif(n)
x2 <- factor(sample(c('a', 'b', 'c'), n, TRUE))
f <- cph(Surv(d.time) ~ pol(x1,2) * x2, x=TRUE, y=TRUE, surv=TRUE, time.inc=1.5)
#or f <- psm(S ~ \dots)
pa <- 'polspline' %in% row.names(installed.packages())
if(pa) {
cal <- calibrate(f, u=1.5, B=20) # cmethod='hare'
plot(cal)
}
cal <- calibrate(f, u=1.5, cmethod='KM', m=50, B=20) # usually B=200 or 300
plot(cal, add=pa)
set.seed(1)
y <- sample(0:2, n, TRUE)
x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
x4 <- runif(n)
f <- lrm(y ~ x1 + x2 + x3 * x4, x=TRUE, y=TRUE)
cal <- calibrate(f, kint=2, predy=seq(.2, .8, length=60),
group=y)
# group= does k-sample validation: make resamples have same
# numbers of subjects in each level of y as original sample
plot(cal)
#See the example for the validate function for a method of validating
#continuation ratio ordinal logistic models. You can do the same
#thing for calibrate
# }
```

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