bootAverageDominanceAnalysis: Bootstrap Average Values for Dominance Analysis

Description

Bootstrap average values and corresponding standard errors for each predictor in the dominance analysis. These values are used for assessing general dominance.

Usage

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

Value

An object of class `bootAverageDominanceAnalysis` containing: -

boot: The results of the bootstrap analysis in a boot object.
preds: The predictors analyzed
fit.functions: The fit functions used in the analysis
R: The number of bootstrap resamples
eg: expanded grid of predictors by fit functions
terms: The terms analyzed

Arguments

x: A model object, like `lm`, `glm`, or `lmer`.
R: An integer indicating the number of bootstrap resamples to be performed.
constants: A character vector specifying predictors that should remain constant in the bootstrap analysis. Default is an empty vector.
terms: An optional vector of terms (predictors) to be analyzed. If NULL, terms are obtained from the model. Default is NULL.
fit.functions: A vector of functions providing fit indices for the model. See `fit.functions` parameter in `dominanceAnalysis` function.
null.model: An optional model object specifying the null model for linear mixed models, used as a baseline for testing submodels. Default is NULL.
...: Additional arguments passed to `dominanceAnalysis` method

Details

Use summary() to obtain a nicely formatted data.frame object.

Examples

Run this code

# \donttest{
lm.1 <- lm(Employed ~ ., longley)
da.ave.boot <- bootAverageDominanceAnalysis(lm.1, R = 10) # smaller for example, should be >1000
summary(da.ave.boot)
# }