ma.aps.reg: Combinatorial search (all possible subset) for regression...

In DJL: Distance Measure Based Judgment and Learning

Combinatorial search (all possible subset) for regression analysis

Description

Implements combinatorial (exhaustive) search algorithm, aka all-possible-subsets regression. As opposed to the sequential approach (stepwise, forward addition, or backward elimination) that has a potential bias resulting from considering only one variable for selection at a time, all possible combinations of the independent variables are examined, and sets satisfying designated conditions are returned.

Usage

ma.aps.reg(dv, iv, min=1, max, mad=FALSE, aic=FALSE, bic=FALSE,
           model.sig=TRUE, coeff.sig=TRUE, coeff.vif=TRUE, coeff.cor=FALSE)

Arguments

dv	Dependent variable (r by 1)
iv	Independent variable(s) (r by c)
min	Minimum number of independent variable to explore (>=1)
max	Maximum number of independent variable to explore (<=r/10)
mad	Returns mean absolute deviation when TRUE
aic	Returns Akaike's information criterion when TRUE
bic	Returns Bayesian information criterion when TRUE
model.sig	Returns models statistically significant only when TRUE
coeff.sig	Returns models with statistically significant coefficients only when TRUE
coeff.vif	Returns models with allowable level of multicollinearity only when TRUE
coeff.cor	Returns models without suppression effects only when TRUE

Author(s)

Dong-Joon Lim, PhD

References

Hair, Joseph F., et al. Multivariate data analysis. Vol. 7. Upper Saddle River, NJ: Pearson Prentice Hall, 2006.

Examples

# Load airplane dataset
df <- dataset.airplane.2017

# ready
dv <- subset(df, select = 2)
iv <- subset(df, select = 3 : 7)

# go
ma.aps.reg(dv, iv, 1, 3, mad = TRUE, coeff.cor = TRUE)

DJL documentation built on March 31, 2023, 9:05 p.m.