bma-class {BMS} | R Documentation |

A list holding results from a BMA iteration chain

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`

:

`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 coefficents,`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

`summary.bma`

, `print.bma`

, `coef.bma`

, `density.bma`

, `image.bma`

, `plot.bma`

Martin Feldkircher and Stefan Zeugner

`bms`

for creating `bma`

objects,

or `topmod`

for the topmod object

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

[Package *BMS* version 0.3.1 Index]