fastbw
deletes factors, not columns of the design matrix. Factors requiring multiple d.f. will be retained or dropped as a group.
The function prints the deletion statistics for each variable in
turn, and prints approximate parameter estimates for the model after
deleting variables. The approximation is better when the number of
factors deleted is not large. For
ols
, the approximation is exact for
regression coefficients, and standard errors are only off by a factor
equal to the ratio of the mean squared error estimate for the reduced
model to the original mean squared error estimate for the full model.
If the fit was from
ols
,
fastbw
will compute the usual R^2
statistic for each model.
fastbw(fit, rule="aic", type="residual", sls=.05, aics=0, eps=1e-9, k.aic=2) ## S3 method for class 'fastbw': print(x, digits=4, ...)
Varcov(fit)
defined (e.g., from
ols
,
lrm
,
cph
,
psm
,
lm
,
glm
)
"aic"
for Akaike's information criterion. Use
rule="p"
to use P-values
"residual"
for
the pooled residual chi-square. Use
type="individual"
to use Wald
chi-square of individual factors.
rule="p"
. Default is .05.
rule="aic"
,
variables are deleted until the chi-square -
k.aic
times d.f. falls below
aics
.
Default
aics
is zero to use the ordinary AIC. Set
aics
to say 10000
to see all variables deleted in order of descending importance.
1E-9
.
k.aic
equal
to log(n), where n is the effective sample size (number of events
for survival models).
fastbw
factors.kept
.
parms.kept
.
Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu
Lawless, J. F. and Singhal, K. (1978): Efficient screening of nonnormal regression models. Biometrics 34:318–327.
## Not run: fastbw(fit, optional.arguments) # print results z <- fastbw(fit, optional.args) # typically used in simulations lm.fit(X[,z$parms.kept], Y) # least squares fit of reduced model ## End(Not run)