wildbootAddResids: Sample residuals according to a new model

Description

Sample residuals according to a new model

Usage

wildbootAddResids(
  means,
  sampling_errors,
  method,
  rescaleResids,
  model,
  invTransFun,
  wild_bootstrap,
  wild_bootType,
  ...
)

Value

sampled residuals

Arguments

means: a vector of means
sampling_errors: Sampling vector to resample errors from. Used only if error is 4 and is passed as argument to generateData. If sampling_errors = NULL (default), mean residuals at off-axis points between observed and predicted response are taken.
method: What assumption should be used for the variance of on- and off-axis points. This argument can take one of the values from c("equal", "model", "unequal"). With the value "equal" as the default. "equal" assumes that both on- and off-axis points have the same variance, "unequal" estimates a different parameter for on- and off-axis points and "model" predicts variance based on the average effect of an off-axis point. If no transformations are used the "model" method is recommended. If transformations are used, only the "equal" method can be chosen.
rescaleResids: a boolean indicating whether to rescale residuals, or else normality of the residuals is assumed.
model: The mean-variance model
invTransFun: the inverse transformation function, back to the variance domain
wild_bootstrap: Whether special bootstrap to correct for heteroskedasticity should be used. If wild_bootstrap = TRUE, errors are generated from sampling_errors multiplied by a random variable following Rademacher distribution. Argument is used only if error = 4.
wild_bootType: Type of distribution to be used for wild bootstrap. If wild_bootstrap = TRUE, errors are generated from "rademacher", "gamma", "normal" or "two-point" distribution.
...: passed on to predictVar