methods
Methods for 'lmSubsets' and 'lmSelect' Objects
Extractor methods for "lmSubsets"
and "lmSelect"
objects.
- Keywords
- regression
Usage
# S3 method for lmSubsets
variable.names(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
variable.names(object, best = 1, …, na.rm = TRUE, drop = TRUE)# S3 method for lmSubsets
formula(x, size, best = 1, …)
# S3 method for lmSelect
formula(x, best, …)
# S3 method for lmSubsets
model.frame(formula, …)
# S3 method for lmSelect
model.frame(formula, …)
# S3 method for lmSubsets
model.matrix(object, size, best = 1, …)
# S3 method for lmSelect
model.matrix(object, best, …)
# S3 method for lmSubsets
model_response(data, …)
# S3 method for lmSelect
model_response(data, …)
# S3 method for lmSubsets
refit(object, size, best = 1, …)
# S3 method for lmSelect
refit(object, best = 1, …)
# S3 method for lmSubsets
deviance(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
deviance(object, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
sigma(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
sigma(object, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
logLik(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
logLik(object, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
AIC(object, size, best = 1, …, k = 2, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
AIC(object, best = 1, …, k = 2, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
BIC(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
BIC(object, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
coef(object, size, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSelect
coef(object, best = 1, …, na.rm = TRUE, drop = TRUE)
# S3 method for lmSubsets
vcov(object, size, best = 1, …)
# S3 method for lmSelect
vcov(object, best = 1, …)
# S3 method for lmSubsets
fitted(object, size, best = 1, …)
# S3 method for lmSelect
fitted(object, best = 1, …)
# S3 method for lmSubsets
residuals(object, size, best = 1, …)
# S3 method for lmSelect
residuals(object, best = 1, …)
Arguments
- object, formula, data, x
An object of class
"lmSubsets"
or"lmSelect"
.- size
The submodel size.
- best
The submodel ranking.
- …
Forwarded arguments.
- k
AIC penalty.
- drop
Control shape of return value.
- na.rm
Remove missing submodels.
Details
The extractor methods work for "lmSubsets"
and
"lmSelect"
objects, i.e., objects that have been generated
using the formula interface.
If drop == FALSE
, the extractor methods variable.names
,
deviance
, sigma
, logLik
, AIC
, BIC
and coef
return a data.frame
object. If drop ==
TRUE
, the return value is a logical
matrix
(variable.names
), a numeric
matrix (coef
), or a
numeric
vector. If the drop
parameter is not set
explicitly when calling variable.names
or coef
,
one-dimensional values are represented in a compact form.
If a desired extractor function is not available, refit
can be
called explicitly to obtain the corresponding "lm"
object.
See Also
Examples
# NOT RUN {
## load data
data("AirPollution", package = "lmSubsets")
## fit subsets (5 best subsets per size)
lm_all <- lmSubsets(mortality ~ ., data = AirPollution, nbest = 5)
## extract information (for best submodel of size 3)
coef(lm_all, size = 3)
vcov(lm_all, size = 3)
residuals(lm_all, size = 3)
fitted(lm_all, size = 3)
model.matrix(lm_all, size = 3)
## select best (BIC) submodels
lm_best <- lmSelect(lm_all)
## extract information
deviance(lm_best)
logLik(lm_best)
AIC(lm_best)
BIC(lm_best, best = 1:5)
## refit model
lm5 <- refit(lm_all, size = 5)
summary(lm5)
## (Note that the p-values are not valid due to model selection.)
# }