Bootstrap input dataset and rerun the model to get confidence bounds and aggregated parameters

## Usage

```
bootstrapFit(
fit,
nboot = 200,
nSampIndiv,
stratVar,
stdErrType = c("perc", "se"),
ci = 0.95,
pvalues = NULL,
restart = FALSE,
plotHist = FALSE,
fitName = as.character(substitute(fit))
)
```

## Arguments

- fit
the nlmixr2 fit object

- nboot
an integer giving the number of bootstrapped models to be fit; default value is 200

- nSampIndiv
an integer specifying the number of samples in each bootstrapped sample; default is the number of unique subjects in the original dataset

- stratVar
Variable in the original dataset to stratify on; This is useful to distinguish between sparse and full sampling and other features you may wish to keep distinct in your bootstrap

- stdErrType
This gives the standard error type for the updated standard errors; The current possibilities are:

`"perc"`

which gives the standard errors by percentiles (default) or`"se"`

which gives the standard errors by the traditional formula.- ci
Confidence interval level to calculate. Default is 0.95 for a 95 percent confidence interval

- pvalues
a vector of pvalues indicating the probability of each subject to get selected; default value is

`NULL`

implying that probability of each subject is the same- restart
A boolean to try to restart an interrupted or incomplete boostrap. By default this is

`FALSE`

- plotHist
A boolean indicating if a histogram plot to assess how well the bootstrap is doing. By default this is turned off (

`FALSE`

)- fitName
is the fit name that is used for the name of the boostrap files. By default it is the fit provided though it could be something else.

## Value

Nothing, called for the side effects; The original fit is updated with the bootstrap confidence bands

## Examples

```
if (FALSE) {
one.cmt <- function() {
ini({
tka <- 0.45; label("Ka")
tcl <- 1; label("Cl")
tv <- 3.45; label("V")
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
linCmt() ~ add(add.sd)
})
}
fit <- nlmixr2(one.cmt, nlmixr2data::theo_sd, est = "saem", control = list(print = 0))
withr::with_tempdir({ # Run example in temp dir
bootstrapFit(fit, nboot = 5, restart = TRUE) # overwrites any of the existing data or model files
bootstrapFit(fit, nboot = 7) # resumes fitting using the stored data and model files
# Note this resumes because the total number of bootstrap samples is not 10
bootstrapFit(fit, nboot=10)
# Note the boostrap standard error and variance/covariance matrix is retained.
# If you wish to switch back you can change the covariance matrix by
nlmixr2est::setCov(fit, "linFim")
# And change it back again
nlmixr2est::setCov(fit, "boot10")
# This change will affect any simulations with uncertainty in their parameters
# You may also do a chi-square diagnostic plot check for the bootstrap with
bootplot(fit)
})
}
```