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

In BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Prediction Method for an object of class BAS

Description

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

Usage

## S3 method for class 'bas'
predict(
  object,
  newdata,
  se.fit = FALSE,
  type = "link",
  top = NULL,
  estimator = "BMA",
  na.action = na.pass,
  ...
)

Arguments

object	An object of class BAS, created by bas
newdata	dataframe for predictions. If missing, then use the dataframe used for fitting for obtaining fitted and predicted values.
se.fit	indicator for whether to compute se of fitted and predicted values
type	Type of predictions required. "link" which is on the scale of the linear predictor is the only option currently for linear models, which for the normal model is equivalent to type='response'.
top	a scalar integer 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
na.action	function determining what should be done with missing values in newdata. The default is to predict NA.
...	optional extra arguments

Details

Use BMA and/or model selection to form predictions using the top highest probability models.

Value

a list of

fit	fitted values based on the selected estimator
Ybma	predictions using BMA, the same as fit for non-BMA methods for compatibility; will be deprecated
Ypred	matrix of predictions under each model for BMA
se.fit	se of fitted values; in the case of BMA this will be a matrix
se.pred	se for predicted values; in the case of BMA this will be a matrix
se.bma.fit	vector of posterior sd under BMA for posterior mean of the regression function. This will be NULL if estimator is not 'BMA'
se.bma.pred	vector of posterior sd under BMA for posterior predictive values. this will be NULL if estimator is not 'BMA'
best	index of top models included
bestmodels	subset of bestmodels used for fitting or prediction
best.vars	names of variables in the top model; NULL if estimator='BMA'
df	scalar or vector of degrees of freedom for models
estimator	estimator upon which 'fit' is based.

Author(s)

Merlise Clyde

See Also

bas, fitted.bas, confint.pred.bas, variable.names.pred.bas

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

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

Examples

data("Hald")
hald.gprior =  bas.lm(Y ~ ., data=Hald, alpha=13, prior="g-prior")

predict(hald.gprior, newdata=Hald, estimator="BPM", se.fit=TRUE)
# same as fitted
fitted(hald.gprior,estimator="BPM")
# default is BMA and estimation of mean vector
hald.bma = predict(hald.gprior, top=5, se.fit=TRUE)
confint(hald.bma)

hald.bpm = predict(hald.gprior, newdata=Hald[1,],
                    se.fit=TRUE,
                    estimator="BPM")
confint(hald.bpm)
# extract variables
variable.names(hald.bpm)

hald.hpm = predict(hald.gprior, newdata=Hald[1,],
                    se.fit=TRUE,
                    estimator="HPM")
confint(hald.hpm)
variable.names(hald.hpm)

hald.mpm = predict(hald.gprior, newdata=Hald[1,],
                    se.fit=TRUE,
                    estimator="MPM")
confint(hald.mpm)
variable.names(hald.mpm)

BAS documentation built on Nov. 2, 2022, 5:09 p.m.