pMM {MDMA} | R Documentation |
Posterior model probability
Description
Calculate the posterior model probability for a set of models.
Usage
pMM(...)
Arguments
... |
objects of class |
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]