# validate

##### Resampling Validation of a Fitted Model's Indexes of Fit

The `validate`

function when used on an object created by one of the
`rms`

series does resampling validation of a
regression model, with or without backward step-down variable deletion.

- Keywords
- models, methods, regression, survival

##### Usage

```
# fit <- fitting.function(formula=response ~ terms, x=TRUE, y=TRUE)
validate(fit, method="boot", B=40,
bw=FALSE, rule="aic", type="residual", sls=0.05, aics=0,
force=NULL, estimates=TRUE, pr=FALSE, …)
# S3 method for validate
print(x, digits=4, B=Inf, …)
# S3 method for validate
latex(object, digits=4, B=Inf, file='', append=FALSE,
title=first.word(deparse(substitute(x))),
caption=NULL, table.env=FALSE,
size='normalsize', extracolsize=size, …)
# S3 method for validate
html(object, digits=4, B=Inf, caption=NULL, …)
```

##### Arguments

- fit
a fit derived by e.g.

`lrm`

,`cph`

,`psm`

,`ols`

. The options`x=TRUE`

and`y=TRUE`

must have been specified.- method
may be

`"crossvalidation"`

,`"boot"`

(the default),`".632"`

, or`"randomization"`

. See`predab.resample`

for details. Can abbreviate, e.g.`"cross", "b", ".6"`

.- B
number of repetitions. For

`method="crossvalidation"`

, is the number of groups of omitted observations. For`print.validate`

,`latex.validate`

, and`html.validate`

,`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.- bw
`TRUE`

to do fast step-down using the`fastbw`

function, for both the overall model and for each repetition.`fastbw`

keeps parameters together that represent the same factor.- rule
Applies if

`bw=TRUE`

.`"aic"`

to use Akaike's information criterion as a stopping rule (i.e., a factor is deleted if the \(\chi^2\) falls below twice its degrees of freedom), or`"p"`

to use \(P\)-values.- type
`"residual"`

or`"individual"`

- stopping rule is for individual factors or for the residual \(\chi^2\) for all variables deleted- sls
significance level for a factor to be kept in a model, or for judging the residual \(\chi^2\).

- aics
cutoff on AIC when

`rule="aic"`

.- force
see

`fastbw`

- estimates
see

`print.fastbw`

- pr
`TRUE`

to print results of each repetition- …
parameters for each specific validate function, and parameters to pass to

`predab.resample`

(note especially the`group`

,`cluster`

, amd`subset`

parameters). For`latex`

, optional arguments to`latex.default`

. Ignored for`html.validate`

.For

`psm`

, you can pass the`maxiter`

parameter here (passed to`survreg.control`

, default is 15 iterations) as well as a`tol`

parameter for judging matrix singularity in`solvet`

(default is 1e-12) and a`rel.tolerance`

parameter that is passed to`survreg.control`

(default is 1e-5).For

`print.validate`

… is ignored.- x,object
an object produced by one of the

`validate`

functions- digits
number of decimal places to print

- file
file to write LaTeX output. Default is standard output.

- append
set to

`TRUE`

to append LaTeX output to an existing file- title, caption, table.env, extracolsize
see

`latex.default`

. If`table.env`

is`FALSE`

and`caption`

is given, the character string contained in`caption`

will be placed before the table, centered.- size
size of LaTeX output. Default is

`'normalsize'`

. Must be a defined LaTeX size when prepended by double slash.

##### Details

It provides bias-corrected indexes that are specific to each type
of model. For `validate.cph`

and `validate.psm`

, see `validate.lrm`

,
which is similar.
For `validate.cph`

and `validate.psm`

, there is
an extra argument `dxy`

, which if `TRUE`

causes the `dxy.cens`

function to be invoked to compute the Somers' \(D_{xy}\) rank correlation
to be computed at each resample. The values corresponding to the row
\(D_{xy}\) are equal to \(2 * (C - 0.5)\) where C is the
C-index or concordance probability.

For `validate.cph`

with `dxy=TRUE`

,
you must specify an argument `u`

if the model is stratified, since
survival curves can then cross and \(X\beta\) is not 1-1 with
predicted survival.
There is also `validate`

method for
`tree`

, which only does cross-validation and which has a different
list of arguments.

##### Value

a matrix with rows corresponding to the statistical indexes and columns for columns for the original index, resample estimates, indexes applied to the whole or omitted sample using the model derived from the resample, average optimism, corrected index, and number of successful re-samples.

##### Side Effects

prints a summary, and optionally statistics for each re-fit

##### See Also

`validate.ols`

, `validate.cph`

,
`validate.lrm`

, `validate.rpart`

,
`predab.resample`

, `fastbw`

, `rms`

,
`rms.trans`

, `calibrate`

,
`dxy.cens`

, `survConcordance`

##### Examples

```
# NOT RUN {
# See examples for validate.cph, validate.lrm, validate.ols
# Example of validating a parametric survival model:
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n, TRUE))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
S <- Surv(dt,e)
f <- psm(S ~ age*sex, x=TRUE, y=TRUE) # Weibull model
# Validate full model fit
validate(f, B=10) # usually B=150
# Validate stepwise model with typical (not so good) stopping rule
# bw=TRUE does not preserve hierarchy of terms at present
validate(f, B=10, bw=TRUE, rule="p", sls=.1, type="individual")
# }
```

*Documentation reproduced from package rms, version 5.1-4, License: GPL (>= 2)*