All Possible Subsets Regression
The function runs all possible subsets regression and returns data needed to run commonality and dominance analysis.
aps(dataMatrix, dv, ivlist)
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 |
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.
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. |
Kim Nimon <kim.nimon@gmail.com>
Nimon, K., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16, 650-674.
## 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"))
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.