Learn R Programming
BAS (version 1.0.5)

fitted.bma: Fitted values for a BMA objects

Description

Calculate fitted values for a BMA object

Usage

## S3 method for class 'bma}(object,  type="HPM", top=NULL, ...)':
fittedundefined
object{An object of class 'bma' as created by bas}
  type{type of fitted value to return.  Options include
   
'HPM' the highest probability model 
'BMA' Bayesian model averaging, using optionally only the 'top'
    models 
'MPM' the median probability model of Barbieri and Berger.}
top{optional argument specifying that the 'top' models will be
    used in constructing the BMA prediction, if NULL all models will be
    used.  If top=1, then this is equivalent to 'HPM'}
  ...{optional arguments, not used currently}
A vector of length n of fitted values.

Calcuates fitted values at observed design matrix using either
  the highest probability model, 'HPM', the posterior mean (under BMA)
  'BMA', or the median probability model 'MPM'.  The median probability
  model is defined by including variable where the marginal inclusion
  probability is greater than or equal to 1/2. For type="BMA", the
  weighted average may be based on using a subset of the highest
  probability models if an optional argument is given for top.  By
  default BMA uses all sampled models, which may take a while to compute
  if the number of variables or number of models is large.

Barbieri, M.  and Berger, J.O. (2004) Optimal predictive
  model selection. Annals of Statistics. 32, 870-897.
  http://projecteuclid.org/Dienst/UI/1.0/Summarize/euclid.aos/1085408489
predict.bma
data(Hald)
hald.gprior =  bas.lm(Y~ ., data=Hald, prior="ZS-null", initprobs="Uniform")
plot(Hald$Y, fitted(hald.gprior, type="HPM"))
plot(Hald$Y, fitted(hald.gprior, type="BMA"))
plot(Hald$Y, fitted(hald.gprior, type="MPM"))
[object Object]
regression

Arguments