predict.bas

predict

An object of class BAS, created by <code>bas</code>

object

dataframe, new matrix or vector of data for predictions. May 
    include a column for the intercept or just the predictor variables.
  If a dataframe, the variables are extracted using model.matrix using
  the call that created 'object'.

newdata

a scalar integer M.  If supplied, subset the top M models,
  based on posterior probabilities for model predictions and BMA.

Type of predictions required. "Link" which is on the
  scale of the linear predictor is the only option currently.
 

type

Predictions under model averaging from a BMA object of
  class inheriting from 'bas'.


regression

Package for Bayesian Model Averaging in linear models and
generalized linear models using stochastic or
deterministic sampling without replacement from posterior
distributions.  Prior distributions on coefficients are
from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the
mixture of g-priors from
Liang et al (2008)
<http://dx.doi.org/10.1198/016214507000001337>
for linear models or mixtures of
g-priors in GLMs of Li and Clyde (2015)
<http://arxiv.org/abs/1503.06913>. Other model
selection criteria include AIC, BIC and Empirical Bayes estimates of g.
Sampling probabilities may be updated based on the sampled models
using Sampling w/out Replacement or an efficient MCMC algorithm
samples models using the BAS tree structure as an efficient
hash table. Uniform priors over all models or beta-binomial prior distributions on
model size are allowed, and for large p truncated priors on the model
space may be used.  The user may force variables to always be included.
Details behind the sampling algorithm are provided in
Clyde, Ghosh and Littman (2010) <http://dx.doi.org/10.1198/jcgs.2010.09049>.

predict.bas: Prediction Method for an object of class BMA

Description

Usage

Arguments

Value

Details

See Also

Examples