# bootCase

##### 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.

- Keywords
- internal, regression

##### Usage

```
## S3 method for class 'lm':
bootCase(object, f.=coef, B=999)
## S3 method for class 'glm':
bootCase(object, f.=coef, B=999)
## S3 method for class 'nls':
bootCase(object, f.=coef, B=999)
nextBoot(object, sample)
```

##### Arguments

- object
- A regression object of type
`lm`

,`glm`

or class`nls`

. May work with other regression objects that support the`update`

method and has a`subset`

argument. See details below. - 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 with replacement of the integers from 1 to n=non-missing sample size that defines a bootstrap sample.

##### Details

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 `subset`

argument. 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
incorrect results. 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.

##### Value

- A matrix (with class
`c("bootCase", "matrix")`

) with B rows and rank(object) columns giving the bootstrap estimates. These can be summarized as needed using standard R tools. The returned object has an attribute`"pointEstimate"`

that contains the value of the function`f`

applied to the argument`object`

.

##### References

Fox, J. and Weisberg, S. (2011) *Companion to Applied Regression*, Second Edition.
Thousand Oaks: Sage.
Weisberg, S. (2014) *Applied Linear Regression*, Fourth Edition, Wiley
Wiley, Chapters 4 and 11.

##### See Also

See Also `update`

##### Examples

```
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
cbind("Bootstrap SD"=apply(betahat.boot, 2, sd),
t(apply(betahat.boot, 2, function(x) quantile(x, c(.025, .975)))))
```

*Documentation reproduced from package car, version 2.0-20, License: GPL (>= 2)*