BMS (version 0.3.4)

bma-class: Class "bma"

Description

A list holding results from a BMA iteration chain

Objects from the Class

Objects can be created via calls to bms, but indirectly also via c.bma A bma object is a list whose elements hold information on input and output for a Bayesian Model Averaging iteration chain, such as from a call to bms:

Slots

.S3Class:

Object of class "list", elements are:

info:

An object of class "list" holding aggregate statistics: iter is the number of iterations, burn the number of burn-ins. The following have to be divided by cumsumweights to get posterior expected values: inccount are the posterior inclusion probabilities, b1mo and b2mo the first and second moment of coefficients, add.otherstats other statistics of interest (typically the moments of the shrinkage factor), msize is the post. expected model size, k.vec the posterior model size distribution, pos.sign the unconditional post. probability of positive coefficients, corr.pmp is the correlation between the best models' MCMC frequencies and their marg. likelihoods. timed is the time that was needed for MCMC sampling, cons is the posterior expected value of the constant. K and N are the maximum number of covariates and the sample size, respectively.

arguments:

An object of class "list" holding the evaluated function arguments provided to bms

topmod:

An object of class topmod containing the best drawn models. see topmod for more details

start.pos:

the positions of the starting model. If bmao is a bma object this corresponds to covariates bmao$reg.names[bmao$start.pos]. If bmao is a chain that resulted from several starting models (cf. c.bma, then start.pos is a list detailing all of them.

gprior.info:

a list of class gprior-class, detailing information on the g-prior: gtype corresponds to argument g above, is.constant is FALSE if gtype is either "hyper" or "EBL", return.g.stats corresponds to argument g.stats above, shrinkage.moments contains the first and second moments of the shrinkage factor (only if return.g.stats==TRUE), g details the fixed g (if is.constant==TRUE), hyper.parameter corresponds to the hyper-g parameter $a$ as in Liang et al. (2008).

mprior.info:

a list of class mprior-class, detailing information on the model prior: origargs lists the original arguments to mprior and mprior.size above; mp.msize denotes the prior mode size; mp.Kdist is a (K+1) vector with the prior model size distribution from 0 to K

X.data:

Object of class "data.frame" or class "matrix", matrix: corresponds to argument X.data in bms, possibly cleaned for NAs

reg.names:

Vector of class "character": the covariate names to be used for X.data in bms

bms.call:

Object of class "call": the original call to the bms function

Methods

summary.bma, print.bma, coef.bma, density.bma, image.bma, plot.bma

References

http://bms.zeugner.eu

See Also

bms for creating bma objects, or topmod for the topmod object

Examples

 data(datafls)
 mm=bms(datafls)
 #show posterior model size
 print(mm$info$msize/mm$info$cumsumweights)
 #is the same number as in
 summary(mm)