pMM {MDMA}R Documentation

Posterior model probability

Description

Calculate the posterior model probability for a set of models.

[Stable]

Usage

pMM(...)

Arguments

...

objects of class (g)lm, given as separate arguments.

Details

Posterior model probabilities are calculated for every model i as

\mathrm{pMO}_i = \frac{\mathrm{exp}\Big[-\frac{1}{2}\Delta_i\mathrm{BIC}\Big]}{\sum_{j = 1}^K\mathrm{exp}\Big[-\frac{1}{2}\Delta_j\mathrm{BIC}\Big]},

where the minimal BIC value is subtracted from all BICs. In other words: the model with the lowest BIC has \Delta\mathrm{BIC}=0.

Value

pMM returns to posterior model probabilities for the models provided.

Author(s)

Mathijs Deen

Examples

lm.1 <- lm(mpg ~ hp + wt, data = mtcars)
lm.2 <- lm(mpg ~ hp * wt, data = mtcars)
lm.3 <- lm(mpg ~ hp * wt + gear, data = mtcars)
pMM(lm.1, lm.2, lm.3)

[Package MDMA version 2.0.0 Index]