alr3 (version 1.1.12)

boot.case: Case bootstrap for regression models

Description

This routine does a case bootstrap resampling for regression models. It returns a matrix of the estimated coefficients from each of the bootstrap samples.

Usage

boot.case(object, f=coef, B=999)
next.boot(object,sample)

Arguments

object

Any regression object that works with update and with the subset argument to update.

f

A function that will be applied to the updated regression object to compute the statistics of interest. The default is coef, to return to regression coefficient estimates.

B

Number of bootstrap samples.

sample

A sample of size n with replacement of the integers from 1 to n that defines a bootstrap sample.

Value

• A matrix with B rows and rank(object) columns giving the bootstrap estimates. These can be summarized as needed using standard R/S-plus tools.

Details

This routine does the case-bootstrap described in the reference below. Begin with a regression object. For each of B bootstrap samples, sample the rows of the data matrix with replacement, and recompute and save estimates. For nls objects there may be convergence problems in the bootstrap. The routine will continue until convergence is attained B times, or until there are 25 consecutive failures to converge. next.boot is an internal function that will update a model correctly, depending on the class of the model object.

References

S. Weisberg (2005). Applied Linear Regression, third edition. New York: Wiley, Chapters 4 and 11.

See Also

See Also update

Examples

data(transact)
m1 <- lm(Time~ T1 + T2, data=transact)
betahat <- coef(m1)
betahat.boot <- boot.case(m1,B=99) # 99 bootstrap samples--too small to be useful
summary(betahat.boot)  # default summary
# bootstrap standard errors 
apply(betahat.boot,2,sd)
# bootstrap 95% confidence intervals
cl <- function(x) quantile(x,c(.025,.975))
apply(betahat.boot,2,cl)