aps: All Possible Subsets Regression

In yhat: Interpreting Regression Effects

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

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.

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.

Author(s)

Kim Nimon <[e-mail]>

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

  ## 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 
     if (require ("MBESS")){
     data(HS)
  ## APS
     apsOut<-aps(HS,"t6_paragraph_comprehension",list("t5_general_information","t7_sentence",
                                         "t8_word_classification"))
     }

yhat documentation built on Oct. 11, 2023, 1:08 a.m.