Case bootstrap for regression models
This routine does a case bootstrap resampling for regression models. It returns a matrix of the estimated coefficients from each of the bootstrap samples.
## S3 method for class 'lm': bootCase(object, f=coef, B=999) ## S3 method for class 'lm': bootCase(object, f=coef, B=999) ## S3 method for class 'nls': bootCase(object, f=coef, B=999) nextBoot(object, sample)
- A regression object of type
nls. May work with other regression objects that support the
updatemethod and has a
subsetargument. See details below.
- 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.
- Number of bootstrap samples.
- A sample with replacement of the integers from 1 to n=non-missing sample size that defines a bootstrap sample.
This routine does the case-bootstrap described in the references below. Begin
with a regression object. For each of B bootstrap samples, sample the
non-missing 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.
nextBoot is an internal function that will update a model correctly, depending
on the class of the model object.
This simple routine should return a result with any S3 regression object that
can be updated using the
update function and has a
argument. In addition this method of resampling will not be
appropriate when the original data are grouped, so it is OK in general for
linear regression, logistic regression in which the response is either zero or
one. With bionomial responses, one would generally want to resample one
observation, not all the observations in m trials, so this function will give
wrong answer. The function can be used with Poisson regression with Poisson
sampling, but it is probably wrong for contingency tables with multinomial
sampling. It is OK proportional odds models without Frequencies set, but
inappropriate with Frequencies.
- A matrix with B rows and rank(object) columns giving the bootstrap estimates. These can be summarized as needed using standard R tools.
Fox, J. and Weisberg, S. (2011) Companion to Applied Regression, Second Edition. Thousand Oaks: Sage. S. Weisberg (2005) Applied Linear Regression, Third Edition. Wiley, Chapters 4 and 11.
m1 <- lm(Fertility ~ ., swiss) betahat <- coef(m1) betahat.boot <- bootCase(m1,B=99) # 99 bootstrap samples--too small to be useful summary(betahat.boot) # default summary colnames(betahat.boot) <- names(betahat) cbind("Bootstrap SD"=apply(betahat.boot, 2, sd), t(apply(betahat.boot, 2, function(x) quantile(x, c(.025, .975)))))