predict.surbayes: Get predictive posterior samples

In surbayes: Bayesian Analysis of Seemingly Unrelated Regression Models

Description

This function returns a list of new data sets by sampling from the posterior predictive density of Y | Y0, Xnew.

Usage

## S3 method for class 'surbayes'
predict(object, newdata, nsims = 1, ...)

Arguments

object	Result from calling sur_sample
newdata	data.frame of new data
nsims	number of posterior draws to take. The default and minimum is 1. The maximum is the number of simulations in surbayes
...	further arguments passed to or from other methods

Value

n x J x nsims array of predicted values

Examples

## Taken from bayesm package
if(nchar(Sys.getenv("LONG_TEST")) != 0) {M=1000} else {M=10}
set.seed(66)
## simulate data from SUR
beta1 = c(1,2)
beta2 = c(1,-1,-2)
nobs = 100
nreg = 2
iota = c(rep(1, nobs))
X1 = cbind(iota, runif(nobs))
X2 = cbind(iota, runif(nobs), runif(nobs))
Sigma = matrix(c(0.5, 0.2, 0.2, 0.5), ncol = 2)
U = chol(Sigma)
E = matrix( rnorm( 2 * nobs ), ncol = 2) %*% U
y1 = X1 %*% beta1 + E[,1]
y2 = X2 %*% beta2 + E[,2]
X1 = X1[, -1]
X2 = X2[, -1]
data = data.frame(y1, y2, X1, X2)
names(data) = c( paste0( 'y', 1:2 ), paste0('x', 1:(ncol(data) - 2) ))
## run DMC sampler
formula.list = list(y1 ~ x1, y2 ~ x2 + x3)

## Fit model
out = sur_sample( formula.list, data, M = M )

## Obtain predictions
pred = predict(out, data, nsims = 1)

surbayes documentation built on Aug. 26, 2020, 5:16 p.m.