The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

mombf

Model Selection with Bayesian Methods and Information Criteria

Installation

# Install mombf from CRAN
install.packages("mombf")

# from GitHub:
# install.packages("devtools")
devtools::install_github("davidrusi/mombf")

Quick start

The main Bayesian model selection (BMS) function is modelSelection. For information criteria consider bestBIC, bestEBIC, bestAIC, bestIC. Bayesian model averaging (BMA) is also available for some models, mainly linear and generalized linear models. Local variable selection is implemented in localnulltest and localnulltest_fda. Details are in mombf’s vignette, here we illustrate quickly how to get posterior model probabilities, marginal posterior inclusion probabilities, BMA point estimates and posterior intervals for the regression coefficients and predicted outcomes.

library(mombf)
set.seed(1234)
x <- matrix(rnorm(100*3),nrow=100,ncol=3)
theta <- matrix(c(1,1,0),ncol=1)
y <- x %*% theta + rnorm(100)

priorCoef <- momprior(tau=0.348)  # Default MOM prior on parameters
priorDelta <- modelbbprior(1,1)   # Beta-Binomial prior for model space
fit1 <- modelSelection(y ~ x[,1]+x[,2]+x[,3], priorCoef=priorCoef, priorDelta=priorDelta)
# Output
# Enumerating models...
# Computing posterior probabilities................ Done.

from here, we can also get the posterior model probabilities:

postProb(fit1)
# Output
#    modelid family           pp
# 7      2,3 normal 9.854873e-01
# 8    2,3,4 normal 7.597369e-03
# 15   1,2,3 normal 6.771575e-03
# 16 1,2,3,4 normal 1.437990e-04
# 3        3 normal 3.240602e-17
# 5        2 normal 7.292230e-18
# 4      3,4 normal 2.150174e-19
# 11     1,3 normal 9.892869e-20
# 6      2,4 normal 5.615517e-20
# 13     1,2 normal 2.226164e-20
# 12   1,3,4 normal 1.477780e-21
# 14   1,2,4 normal 3.859388e-22
# 1          normal 2.409908e-25
# 2        4 normal 1.300748e-27
# 9        1 normal 2.757778e-28
# 10     1,4 normal 3.971521e-30

also the BMA estimates, 95% intervals, marginal posterior probability

coef(fit1)
# Output
#              estimate        2.5%      97.5%      margpp
# (Intercept) 0.007230966 -0.02624289 0.04085951 0.006915374
# x[, 1]      1.134700387  0.93487948 1.33599873 1.000000000
# x[, 2]      1.135810652  0.94075622 1.33621298 1.000000000
# x[, 3]      0.000263446  0.00000000 0.00000000 0.007741168
# phi         1.100749637  0.83969879 1.44198567 1.000000000

and BMA predictions for y, 95% intervals

ypred <- predict(fit1)
head(ypred)
# Output
#         mean       2.5%       97.5%
# 1 -0.8936883 -1.1165154 -0.67003262
# 2 -0.2162846 -0.3509188 -0.08331286
# 3  1.3152329  1.0673711  1.56348261
# 4 -3.2299241 -3.6826696 -2.77728625
# 5 -0.4431820 -0.6501280 -0.23919345
# 6  0.7727824  0.6348189  0.90977798
cor(y, ypred[,1])
# Output
#           [,1]
# [1,] 0.8468436

Bug report

Please submit bug reports to the issue tracker.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.