R: Predict function for Bayesian Model Averaging for generalized...

predict.bic.glm {BMA} | R Documentation

## Predict function for Bayesian Model Averaging for generalized linear models.

### Description

Bayesian Model Averaging (BMA) accounts for the model uncertainty inherent in the variable selection problem by averaging over the best models in the model class according to approximate posterior model probability. This function predicts the response resulting from a BMA generalized linear model from given data.

### Usage

    ## S3 method for class 'bic.glm'
    predict( object, newdata, ...)

### Arguments

`object` | a fitted object inheriting from class `bic.glm`.
`newdata` | a data frame containing observations on variables from which the predictor variables are to be selected or constructed from a formula.
`...` | ignored (for compatibility with generic function).

### Value

The predicted values from the BMA model for each observation in newdata.

### See Also

`bic.glm`

### Examples

    ## Not run:
    # Example 1 (Gaussian)

         library(MASS)
         data(UScrime)

         f <- formula(log(y) ~  log(M)+So+log(Ed)+log(Po1)+log(Po2)+
                log(LF)+log(M.F)+log(Pop)+log(NW)+log(U1)+log(U2)+
                log(GDP)+log(Ineq)+log(Prob)+log(Time))

         bic.glm.crimeT <- bic.glm(f, data = UScrime,
                                   glm.family = gaussian())
         predict(bic.glm.crimeT, newdata = UScrime)

         bic.glm.crimeF <- bic.glm(f, data = UScrime,
                                   glm.family = gaussian(),
                                   factor.type = FALSE)
         predict(bic.glm.crimeF, newdata = UScrime)

    ## End(Not run)

    ## Not run:
    # Example 2 (binomial)

         library(MASS)
         data(birthwt)

         y <- birthwt$lo
         x <- data.frame(birthwt[,-1])
         x$race <- as.factor(x$race)
         x$ht <- (x$ht>=1)+0
         x <- x[,-9]
         x$smoke <- as.factor(x$smoke)
         x$ptl <- as.factor(x$ptl)
         x$ht  <- as.factor(x$ht)

         x$ui <- as.factor(x$ui)

         bic.glm.bwT <- bic.glm(x, y, strict = FALSE, OR = 20,
                                glm.family="binomial",
                                factor.type=TRUE)
         predict( bic.glm.bwT, newdata = x)

         bic.glm.bwF <- bic.glm(x, y, strict = FALSE, OR = 20,
                                glm.family="binomial",
                                factor.type=FALSE)
         predict( bic.glm.bwF, newdata = x)

    ## End(Not run)

    ## Not run:
    # Example 3 (Gaussian)

         library(MASS)
         data(anorexia)

         anorexia.formula <- formula(Postwt ~ Prewt+Treat+offset(Prewt))

         bic.glm.anorexiaF <- bic.glm( anorexia.formula, data=anorexia,
                           glm.family="gaussian", factor.type=FALSE)
         predict( bic.glm.anorexiaF, newdata=anorexia)

         bic.glm.anorexiaT <- bic.glm( anorexia.formula, data=anorexia,
                               glm.family="gaussian", factor.type=TRUE)
         predict( bic.glm.anorexiaT, newdata=anorexia)

    ## End(Not run)

    ## Not run:
    # Example 4 (Gamma)

         library(survival)
         data(veteran)

         surv.t <- veteran$time
         x <- veteran[,-c(3,4)]
         x$celltype <- factor(as.character(x$celltype))
         sel<- veteran$status == 0
         x <- x[!sel,]
         surv.t <- surv.t[!sel]

         bic.glm.vaT <- bic.glm(x, y=surv.t,
                                glm.family=Gamma(link="inverse"),
                                factor.type=TRUE)
         predict( bic.glm.vaT, x)

         bic.glm.vaF <- bic.glm(x, y=surv.t,
                                glm.family=Gamma(link="inverse"),
                                factor.type=FALSE)
         predict( bic.glm.vaF, x)

    ## End(Not run)

    # Example 5 (poisson - Yates teeth data)

         x <- rbind.data.frame(c(0, 0, 0),
                               c(0, 1, 0),
                               c(1, 0, 0),
                               c(1, 1, 1))

         y <- c(4, 16, 1, 21)
         n <- c(1,1,1,1)

         bic.glm.yatesF <- bic.glm( x, y, glm.family=poisson(),
                                   weights=n, factor.type=FALSE)

         predict( bic.glm.yatesF, x)

    ## Not run:
    # Example 6 (binomial - Venables and Ripley)

        ldose <- rep(0:5, 2)
        numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
        sex <- factor(rep(c("M", "F"), c(6, 6)))
        SF <- cbind(numdead, numalive=20-numdead)

        budworm <- cbind.data.frame(ldose = ldose, numdead = numdead,
                                    sex = sex, SF = SF)
        budworm.formula <- formula(SF ~ sex*ldose)

        bic.glm.budwormF <- bic.glm( budworm.formula, data=budworm,
                       glm.family="binomial", factor.type=FALSE)
        predict(bic.glm.budwormF, newdata=budworm)

        bic.glm.budwormT <- bic.glm( budworm.formula, data=budworm,
                          glm.family="binomial", factor.type=TRUE)
        predict(bic.glm.budwormT, newdata=budworm)

    ## End(Not run)

[Package BMA version 3.18.9 Index]