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.

AUTHOR(S):

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

SEE ALSO:

, , , , , , , ,

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")