BAS (version 1.7.1)

cv.summary.bas: Summaries for Out of Sample Prediction

Description

Compute average prediction error from out of sample predictions

Usage

cv.summary.bas(pred, ytrue, score = "squared-error")

Value

For squared error, the average prediction error for the Bayesian estimator error = sqrt(sum(ytrue - yhat)^2/npred) while for binary data the misclassification rate is more appropriate.

Arguments

pred

fitted or predicted value from the output from predict.bas

ytrue

vector of left out response values

score

function used to summarize error rate. Either "squared-error", or "miss-class"

Author

Merlise Clyde

See Also

predict.bas

Examples

if (FALSE) {
library(foreign)
cognitive <- read.dta("https://www.stat.columbia.edu/~gelman/arm/examples/child.iq/kidiq.dta")
cognitive$mom_work <- as.numeric(cognitive$mom_work > 1)
cognitive$mom_hs <- as.numeric(cognitive$mom_hs > 0)
colnames(cognitive) <- c("kid_score", "hs", "iq", "work", "age")

set.seed(42)
n <- nrow(cognitive)
test <- sample(1:n, size = round(.20 * n), replace = FALSE)
testdata <- cognitive[test, ]
traindata <- cognitive[-test, ]
cog_train <- bas.lm(kid_score ~ ., prior = "BIC", modelprior = uniform(), data = traindata)
yhat <- predict(cog_train, newdata = testdata, estimator = "BMA", se = F)
cv.summary.bas(yhat$fit, testdata$kid_score)
}