glmxdiag (version 1.0.0)

variableOut: Excluding a variable from the model

Description

Graphically compares models in terms of Information Criterion, each one corresponds to a model where a specific variable is deleted from the linear predictor.

Usage

variableOut(model, k = 2, update.it = FALSE, xlab, ylab, pch, 
                   col, lty, ylim, ...)

Arguments

model

a model supported by glmxdiag.

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

update.it

logical; if TRUE, model without the variable corresponding to the lowest score is returned.

xlab

title for the x axis.

ylab

title for the y axis, by default is 'AIC'; should be changed if a different k is chosen.

pch

type of points.

col

color of points and segments.

lty

type of horizontal line.

ylim

y limits of the plot.

...

further arguments passed to plot.

Value

Called for side effects, but if update.it is set to TRUE returns a model without the variable corresponding to the lowest score.

Details

Each plotted point corresponds to the score of the model where the variable indicated on x axis is excluded. A dashed line is drawn in correspondence of the full model score.

Points and segments corresponding to variables whose deletion lead to a increment of the scored are black, those who lead to a decrement are red.

The output plot can be seen as a graphic version of the first step of stepAIC function inside MASS package.

Theory about Information Criterion suggests that if the minimum score doesn't belong to the full model, then the linear predictor may not be appropriate.

Examples

# NOT RUN {
data("moons")
model <- glm(Moons ~ Diameter * Mass + Distance, family = poisson, data = moons)
variableOut(model)

n <- nobs(model)
new.model <- variableOut(model, k = log(n), ylab = 'BIC', update.it = TRUE)
# }