boot_lm: Bootstrapping for linear models

In nlraa: Nonlinear Regression for Agricultural Applications

Bootstrapping for linear models

Description

Bootstraping for linear models

Usage

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

Arguments

object	object of class lm
f	function to be applied (and bootstrapped), default coef
R	number of bootstrap samples, default 999
psim	simulation level for simulate_lm
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 message if model does not converge. (rare for linear models).
...	additional arguments to be passed to function boot

Details

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

Note

at the moment, when the argument data is used, it is not possible to check that it matches the original data used to fit the model. It will also override the fetching of data.

Examples

require(car)
data(barley, package = "nlraa")
## Fit a linear model (quadratic)
fit.lm <- lm(yield ~ NF + I(NF^2), data = barley)

## Bootstrap coefficients by default
fit.lm.bt <- boot_lm(fit.lm)
## Compute confidence intervals
confint(fit.lm.bt, type = "perc")
## Visualize
hist(fit.lm.bt, 1, ci = "perc", main = "Intercept")
hist(fit.lm.bt, 2, ci = "perc", main = "NF term")
hist(fit.lm.bt, 3, ci = "perc", main = "I(NF^2) term")

nlraa documentation built on July 9, 2023, 6:08 p.m.