force.heredity.bas {BAS} R Documentation

## Post processing function to force constraints on interaction inclusion bas BMA objects

### Description

This function takes the output of a bas object and allows higher order interactions to be included only if their parent lower order interactions terms are in the model, by assigning zero prior probability, and hence posterior probability, to models that do include their respective parents.

### Usage

force.heredity.bas(object, prior.prob = 0.5)


### Arguments

 object a bas linear model or generalized linear model object prior.prob prior probability that a term is included conditional on parents being included

### Value

a bas object with updated models, coefficients and summaries obtained removing all models with zero prior and posterior probabilities.

### Note

Currently prior probabilities are computed using conditional Bernoulli distributions, i.e. P(gamma_j = 1 | Parents(gamma_j) = 1) = prior.prob. This is not very efficient for models with a large number of levels. Future updates will force this at the time of sampling.

### Author(s)

Merlise A Clyde

Other bas methods: BAS, bas.lm(), coef.bas(), confint.coef.bas(), confint.pred.bas(), diagnostics(), fitted.bas(), image.bas(), plot.confint.bas(), predict.basglm(), predict.bas(), summary.bas(), update.bas(), variable.names.pred.bas()

### Examples


data("chickwts")
bas.chk <- bas.lm(weight ~ feed, data = chickwts)
#  summary(bas.chk)  # 2^5 = 32 models
bas.chk.int <- force.heredity.bas(bas.chk)
#  summary(bas.chk.int)  # two models now

data(Hald)
bas.hald <- bas.lm(Y ~ .^2, data = Hald)
bas.hald.int <- force.heredity.bas(bas.hald)
image(bas.hald.int)

image(bas.hald.int)

# two-way interactions
data(ToothGrowth)
ToothGrowth$dose <- factor(ToothGrowth$dose)
levels(ToothGrowth\$dose) <- c("Low", "Medium", "High")
TG.bas <- bas.lm(len ~ supp * dose, data = ToothGrowth, modelprior = uniform())
TG.bas.int <- force.heredity.bas(TG.bas)
image(TG.bas.int)


[Package BAS version 1.7.1 Index]