yhat (version 2.0-2)

aps: All Possible Subsets Regression

Description

The function runs all possible subsets regression and returns data needed to run commonality and dominance analysis.

Usage

aps(dataMatrix, dv, ivlist)

Arguments

dataMatrix

Dataset containing the dependent and independent variables

dv

The dependent variable named in the dataset

ivlist

List of independent variables named in the dataset

Value

ivID

Matrix containing independent variable IDS.

PredBitMap

All possible subsets predictor bit map.

apsBitMap

Index into all possible subsets predictor bit map.

APSMatrix

Table containing the number of predictors and Multiple R^2 for each possible set of predictors.

Details

Function returns all possible subset information that is used by commonality and dominance. If data are missing, non-missing data are eliminated based on listwise deletion for full model.

References

Nimon, K., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16, 650-674.

See Also

calc.yhat commonality dominance rlw

Examples

# NOT RUN {
  ## APS regression predicting miles per gallon based 
  ## on vehicle weight, type of 
  ## carborator, & number of engine cylinders
     apsOut<-aps(mtcars,"mpg",list("wt","carb","cyl"))

  ## APS regression predicting paragraph comprehension based 
  ## on thre verbal tests: general info, sentence comprehension,
  ## & word classification
 
  ## Use HS dataset in MBESS 
     require ("MBESS")
     data(HS)
  ## APS
     apsOut<-aps(HS,"t6_paragraph_comprehension",list("t5_general_information","t7_sentence",
                                         "t8_word_classification"))
# }