nlraa (version 1.9.7)

boot_nls: Bootstrapping for nonlinear models

Description

Bootstraping for nonlinear models

Usage

boot_nls(
  object,
  f = NULL,
  R = 999,
  psim = 2,
  resid.type = c("resample", "normal", "wild"),
  data = NULL,
  verbose = TRUE,
  ...
)

Arguments

object

object of class nls

f

function to be applied (and bootstrapped), default coef

R

number of bootstrap samples, default 999

psim

simulation level for simulate_nls

resid.type

either “resample”, “normal” or “wild”.

data

optional data argument (useful/needed when data are not in an available environment).

verbose

logical (default TRUE) whether to print a message if model does not converge.

...

additional arguments to be passed to function boot

Details

The residuals can either be generated by resampling with replacement (default or non-parametric), from a normal distribution (parameteric) or by changing their signs (wild). This last one is called “wild bootstrap”. There is more information in boot_lm.

See Also

Boot

Examples

# \donttest{
require(car)
data(barley, package = "nlraa")
## Fit a linear-plateau
fit.nls <- nls(yield ~ SSlinp(NF, a, b, xs), data = barley)

## Bootstrap coefficients by default
## Keeping R small for simplicity, increase R for a more realistic use
fit.nls.bt <- boot_nls(fit.nls, R = 1e2)
## Compute confidence intervals
confint(fit.nls.bt, type = "perc")
## Visualize
hist(fit.nls.bt, 1, ci = "perc", main = "Intercept")
hist(fit.nls.bt, 2, ci = "perc", main = "linear term")
hist(fit.nls.bt, 3, ci = "perc", main = "xs break-point term")
# }