summary.gamselBayes {gamselBayes}

Summarise components of the selected generalized additive model from a gamselBayes() fit

Description

Inference summaries of the estimated linear component coefficients of the generalized additive model selected via gamselBayes are tabulated.

Usage

## S3 method for class 'gamselBayes'
summary(object,credLev = 0.95,sigFigs = 5,nMC = 10000,...)

Arguments

object

A gamselBayes() fit object.

credLev

A number between 0 and 1 such that the credible interval band has (100*credLev)% approximate pointwise coverage. The default value is 0.95.

sigFigs

The number of significant figures used for the entries of the summary table.

nMC

The size of the Monte Carlo sample, a positive integer, for carrying out approximate inference from the mean field variational Bayes-approximate posterior distributions when the method is mean field variational Bayes. The default value is 10000.

...

Place-holder for other summary parameters.

Details

If the selected generalized additive model has at least one predictor having a linear effect then a data frame is returned. The columns of the data correspond to posterior means and credible interval limits of the linear effects coefficients.

Value

A data frame containing linear effect Bayesian inferential summaries.

Author(s)

Virginia X. He [e-mail] and Matt P. Wand [e-mail]

Examples

library(gamselBayes) 

# Generate some simple regression-type data:

set.seed(1) ; n <- 1000 ; x1 <- rbinom(n,1,0.5) ; 
x2 <- runif(n) ; x3 <- runif(n) ; x4 <- runif(n)
y <- x1 + sin(2*pi*x2) - x3 + rnorm(n)
Xlinear <- data.frame(x1) ; Xgeneral <- data.frame(x2,x3,x4)

# Obtain a gamselBayes() fit for the data and print out a summary:

fit <- gamselBayes(y,Xlinear,Xgeneral)
summary(fit)

# Print the summary with different values of some of the arguments:

summary(fit,credLev=0.99,sigFigs=3)

if (require("Ecdat"))
{
   # Obtain a gamselBayes() fit for data on schools in California, U.S.A.:

   Caschool$log.avginc <- log(Caschool$avginc)
   mathScore <- Caschool$mathscr
   Xgeneral <- Caschool[,c("mealpct","elpct","calwpct","compstu","log.avginc")]
   fit <- gamselBayes(y = mathScore,Xgeneral = Xgeneral)
   summary(fit)
}