BAS (version 0.80)

fitted.bma: Fitted values for a BMA objects

Description

Calculate fitted values for a BMA object

Usage

## S3 method for class 'bma':
fitted(object,  type="HPM", top=NULL, ...)

Arguments

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

Value

• A vector of length n of fitted values.

Details

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.

References

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

See Also

predict.bma

Examples

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"))