forward: F-test based model effect selection for linear models.

In mixlm: Mixed Model ANOVA and Statistics for Education

F-test based model effect selection for linear models.

Description

Adaptation of existing methods based on AIC/BIC.

Usage

forward(model, alpha = 0.2, full = FALSE, force.in)
backward(model, alpha = 0.2, full = FALSE, hierarchy = TRUE, force.in)
stepWise(model, alpha.enter = 0.15, alpha.remove = 0.15, full = FALSE)
stepWiseBack(model, alpha.remove = 0.15, alpha.enter = 0.15, full = FALSE)
wideForward(formula, data, alpha = 0.2, force.in = NULL)

Arguments

model	object class lm to select effects from.
formula	formula specifying all possible effects.
data	data.frame corresponding to formula.
alpha	numeric p-value cut-off for inclusion/exclusion.
full	logical indicating extended output of forward/backward selection.
force.in	character vector indicating effects to keep in all models.
alpha.enter	numeric p-value cut-off for inclusion.
alpha.remove	numeric p-value cut-off for exclusion.
hierarchy	logical indicating if hierarchy should be forced in backward selection.

Details

F-based versions of built in stepwise methods.

Value

The final linear model after selection is returned.

Author(s)

Kristian Hovde Liland

Examples

set.seed(0)
data <- data.frame(y = rnorm(8),
				   x = factor(c('a','a','a','a','b','b','b','b')),
				   z = factor(c('a','a','b','b','a','a','b','b')))
mod <- lm(y ~ x + z, data=data)
forward(mod)
backward(mod)
stepWise(mod)
stepWiseBack(mod)

# Forward selection for wide matrices (large number of predictors)
set.seed(0)
mydata <- data.frame(y = rnorm(6), X = matrix(rnorm(60),6,10))
fs <- wideForward(y ~ ., mydata)
print(fs)

mixlm documentation built on Aug. 8, 2023, 5:08 p.m.