boot
function in the
package also called boot
. Whereas boot
is very general and therefore
has many arguments, the Boot
function has very few arguments, but should
meet the needs of many users.## S3 method for class 'default':
Boot(object, f=coef, labels=names(coef(object)),
R=999, method=c("case", "residual"))
## S3 method for class 'lm':
Boot(object, f=coef, labels=names(coef(object)),
R=999, method=c("case", "residual"))
## S3 method for class 'glm':
Boot(object, f=coef, labels=names(coef(object)),
R=999, method=c("case", "residual"))
lm
or glm
. The function may work with other regression objects that support the update
method and have a subset
argument, but it will fail if the fitting method for the modcoef
, to return to
regression coefficient estimates. For example,f
. If
this argument is of the wrong length, then generic labels will be generated.lm
objects and will return an error for boot
for the returned value from this function. The car
package includes additional generic functions, as listed below.boot
function is
very general, Boot
is very specific. It takes the information from a
regression object and the choice of method
, and creates a function that is
passed as the statistic
argument to boot
. The argument R
is also passed to boot
. All other arguments to boot
are
kept at their default values.
The methods available for lm
objects are boot
.
This function may fail if the model fit to any of the bootstrap samples is of lower rank than the model fit to the original data. This will occur, for example, if the model includes factors and interactions with a very small number of observations per cell. In this case bootstrap samples may have zero counts in some cells and lose rank.Boot
objects from the boot
package are
boot.array
,
boot.ci
,
plot.boot
and
empinf
. Additional
functions in the car
package are
summary.boot
,
confint.boot
, and
hist.boot
.m1 <- lm(Fertility ~ ., swiss)
betahat.boot <- Boot(m1, R=99) # 99 bootstrap samples--too small to be useful
summary(betahat.boot) # default summary
confint(betahat.boot)
hist(betahat.boot)
# Bootstrap for the estimated residual standard deviation:
sigmahat.boot <- Boot(m1, R=99, f=sigmaHat, labels="sigmaHat")
summary(sigmahat.boot)
confint(sigmahat.boot)
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