Nothing

```
##' This function serves to \sQuote{complete} \code{rms:fastbw}, such that
##' it converts one fitted model to another, the restricted model.
##'
##' @title fastback
##' @param fit A model fit of class \code{rms}
##' @param data The data against which \code{fit} was estimated
##' @param ... Other parameters to be passed to \code{fastbw}
##' @return A fitted model of the same type as \code{fit}, with
##' regressors chosen by stepwise backward regression
##' @author David C. Norris
##' @keywords regression
##' @export fastback
fastback <- function(fit, data, ...){
## N.B. We suppress nuisance warnings "force probably does not work..." from 'fastbw':
deleted.vars <- rownames(suppressWarnings(fastbw(fit, ...))$result)
smaller.formula <- remvar(formula(fit), deleted.vars)
fit$call[[match("formula", names(fit$call))]] <- smaller.formula
red <- eval(fit$call)
## In case a formula with trivial (just Intercept) RHS was estimated,
## red$var will be NULL. To permit this special case to be handled
## within the general algorithm below, we replace this NULL value
## with a 1x1 zero matrix:
if(is.null(red$var)){
red$var <- matrix(0, dimnames=as.list(rep(names(red$coefficients), 2)))
red$info.matrix <- matrix(0)
}
## To return a restricted model more readily comparable with the full model,
## we now zero the coefficients and variances of the original full model,
## and fill in those parts which correspond to the (fewer) parts of the
## restricted model.
keep <- names(red$coefficients)
k <- match(keep, names(fit$coefficients))
## Generally speaking, named numeric vectors and matrices of the original fit
## should be zeroed, and replaced where possible with corresponding elements
## of the reduced model...
for(elem in names(fit)){
if(is.numeric(fit[[elem]])){
if(is.vector(fit[[elem]])){
if(length(fit[[elem]])==length(red[[elem]]))
fit[[elem]] <- red[[elem]]
else if(!is.null(names(fit[[elem]]))){ # Assume named vectors of changed length correspond to coefficients
fit[[elem]] <- 0 * fit[[elem]]
fit[[elem]][keep] <- red[[elem]][keep] # NB: An error will occur, if this assumption is violated!
}
} else if(is.matrix(fit[[elem]])){
fit[[elem]] <- 0 * fit[[elem]]
if(!is.null(dimnames(red[[elem]])))
fit[[elem]][keep,keep] <- red[[elem]][keep,keep]
else
fit[[elem]][k,k] <- red[[elem]] # TODO: Ascertain that this ALWAYS produces correct indexing.
}
}
}
## ..but there are a few exceptions that we deal with separately:
fit$fail <- red$fail
fit$deviance <- red$deviance
## When calculating the Wald statistic, print.lrm encounters a 0/0=NaN error
## where parameter estimates and the associated variances are both strictly zero.
## Therefore, we set the variances of excluded variables to a vanishingly small,
## but finite value:
fit$var[fit$var==0] <- .Machine$double.eps
## Return the reduced fit
eval(fit) # TODO: Find out whether the eval(.) really makes the difference!
}
```

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