fitted.bas {BAS}R Documentation

Fitted values for a BAS BMA objects


Calculate fitted values for a BAS BMA object


## S3 method for class 'bas'
  type = "link",
  estimator = "BMA",
  top = NULL,
  na.action = na.pass,



An object of class 'bas' as created by bas


type equals "response" or "link" in the case of GLMs (default is 'link')


estimator type of fitted value to return. Default is to use BMA with all models. 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. 'BPM' the model that is closest to BMA predictions under squared error loss


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'


function determining what should be done with missing values in newdata. The default is to predict NA.


optional arguments, not used currently


Calculates fitted values at observed design matrix using either the highest probability model, 'HPM', the posterior mean (under BMA) 'BMA', the median probability model 'MPM' or the best predictive model 'BPM". 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. The "BPM" is found be computing the squared distance of the vector of fitted values for a model and the fitted values under BMA and returns the model with the smallest distance. In the presence of multicollinearity this may be quite different from the MPM, with extreme collinearity may drop relevant predictors.


A vector of length n of fitted values.


Merlise Clyde


Barbieri, M. and Berger, J.O. (2004) Optimal predictive model selection. Annals of Statistics. 32, 870-897.

Clyde, M. Ghosh, J. and Littman, M. (2010) Bayesian Adaptive Sampling for Variable Selection and Model Averaging. Journal of Computational Graphics and Statistics. 20:80-101

See Also

predict.bas predict.basglm

Other bas methods: BAS, bas.lm(), coef.bas(), confint.coef.bas(), confint.pred.bas(), diagnostics(), force.heredity.bas(), image.bas(), plot.confint.bas(), predict.basglm(), predict.bas(), summary.bas(), update.bas(), variable.names.pred.bas()

Other predict methods: predict.basglm(), predict.bas(), variable.names.pred.bas()


hald.gprior =  bas.lm(Y~ ., data=Hald, prior="ZS-null", initprobs="Uniform")
plot(Hald$Y, fitted(hald.gprior, estimator="HPM"))
plot(Hald$Y, fitted(hald.gprior, estimator="BMA", top=3))
plot(Hald$Y, fitted(hald.gprior, estimator="MPM"))
plot(Hald$Y, fitted(hald.gprior, estimator="BPM"))

[Package BAS version 1.6.4 Index]