bootAverageDominanceAnalysis: Bootstrap average values for Dominance Analysis

In dominanceanalysis: Dominance Analysis

Description

Bootstrap average values and correspond standard errors for each predictor in the dominance analysis. Those values are used for general dominance.

Usage

bootAverageDominanceAnalysis(
  x,
  R,
  constants = c(),
  terms = NULL,
  fit.functions = "default",
  null.model = NULL,
  ...
)

Arguments

x	lm, glm or lmer model
R	number on bootstrap resamples
constants	vector of predictors to remain unchanged between models. i.e. vector of variables not subjected to bootstrap analysis.
terms	vector of terms to be analyzed. By default, obtained from the model
fit.functions	list of functions which provides fit indices for model. See fit.functions param in dominanceAnalysis function.
null.model	only for linear mixed models, null model against to test the submodels. i.e. only random effects, without any fixed effect.
...	Other arguments provided to lm or lmer (not implemented yet).

Details

Use summary() to get a nice formatted data.frame object.

Examples

lm.1<-lm(Employed~.,longley)
da.ave.boot<-bootAverageDominanceAnalysis(lm.1,R=1000)
summary(da.ave.boot)

dominanceanalysis documentation built on Jan. 13, 2021, 3:47 p.m.