BAS (version 1.4.5)

predict.basglm: Prediction Method for an object of class basglm

Description

Predictions under model averaging from a BMA (BAS) object for GLMS

Usage

# S3 method for basglm
predict(object, newdata, se.fit=FALSE, 
      type=c("response", "link"), top=NULL,
      estimator="BMA", prediction=FALSE, ...)

Arguments

object

An object of class "basglm", created by bas.glm

newdata

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'. May be missing in which case the data used for fitting will be used for prediction.

se.fit

indicator for whether to compute se of fitted and predictied values

type

Type of predictions required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.

top

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

estimator

estimator used for predictions. Currently supported 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. BMA may be computed using only the 'top' models if supplied

prediction

logical value to indicate whether the observed design matrix used in fitting or the newdata will be used for predictions

...

optional extra arguments

Value

a list of

Ybma

predictions using BMA

Ypred

matrix of predictions under each model

postprobs

renormalized probabilities of the top models

best

index of top models included

Details

Use BMA to form predictions using the top highest probability models.

See Also

bas.glm, predict.bas, fitted.bas

Examples

library(MASS)
data(Pima.tr)
data(Pima.te)
Pima.bas = bas.glm(type ~ ., data=Pima.tr, n.models= 2^7, method="BAS",
           betaprior=CCH(a=1, b=nrow(Pima.tr)/2, s=0), family=binomial(),
           modelprior=uniform())
 pred = predict(Pima.bas, newdata=Pima.te, top=1)  # Highest Probability model
 cv.summary.bas(pred$fit, Pima.te$type, score="miss-class")