validate {Design} | R Documentation

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

### Description

The `validate` function when used on an object created by one of the `Design` series does resampling validation of a regression model, with or without backward step-down variable deletion. 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 `rcorr.cens` function to be invoked to compute the Somers' D_{xy} rank correlation to be computed at each resample (this takes a bit longer than the likelihood based statistics). 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.

### 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,
             pr=FALSE, ...)

### 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.
`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-square 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-square for all variables deleted
`sls` | significance level for a factor to be kept in a model, or for judging the residual chi-square.
`aics` | cutoff on AIC when `rule="aic"`.
`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 `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).

### 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

### Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

### See Also

`validate.ols`, `validate.cph`, `validate.lrm`, `validate.tree`, `predab.resample`, `fastbw`, `Design`, `Design.trans`, `calibrate`

### Examples

    # 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")

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