fastbw {rms} | R Documentation |
Fast Backward Variable Selection
Description
Performs a slightly inefficient but numerically stable version of fast
backward elimination on factors, using a method based on Lawless and Singhal
(1978).
This method uses the fitted complete model and computes approximate Wald
statistics by computing conditional (restricted) maximum likelihood estimates
assuming multivariate normality of estimates.
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.
Usage
fastbw(fit, rule=c("aic", "p"),
type=c("residual", "individual", "total"), sls=.05, aics=0, eps=1e-9,
k.aic=2, force=NULL)
## S3 method for class 'fastbw'
print(x, digits=4, estimates=TRUE, ...)
Arguments
fit |
fit object with |
rule |
Stopping rule. Defaults to |
type |
Type of statistic on which to base the stopping rule. Default is
|
sls |
Significance level for staying in a model if |
aics |
For |
eps |
Singularity criterion, default is |
k.aic |
multiplier to compute AIC, default is 2. To use BIC, set |
force |
a vector of integers specifying parameters forced to be in the model, not counting intercept(s) |
x |
result of |
digits |
number of significant digits to print |
estimates |
set to |
... |
ignored |
Value
a list with an attribute kept
if bw=TRUE
, and the
following components:
result |
matrix of statistics with rows in order of deletion. |
names.kept |
names of factors kept in final model. |
factors.kept |
the subscripts of factors kept in the final model |
factors.deleted |
opposite of |
parms.kept |
column numbers in design matrix corresponding to parameters kept in the final model. |
parms.deleted |
opposite of |
coefficients |
vector of approximate coefficients of reduced model. |
var |
approximate covariance matrix for reduced model. |
Coefficients |
matrix of coefficients of all models. Rows correspond to the successive models examined and columns correspond to the coefficients in the full model. For variables not in a particular sub-model (row), the coefficients are zero. |
Author(s)
Frank Harrell
Department of Biostatistics, Vanderbilt University
fh@fharrell.com
References
Lawless, J. F. and Singhal, K. (1978): Efficient screening of nonnormal regression models. Biometrics 34:318–327.
See Also
rms
, ols
, lrm
,
cph
, psm
, validate
,
solvet
, rmsMisc
Examples
## 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)