summary.bkpc: Summary statistics for Markov Chain Monte Carlo chain from...

In BKPC: Bayesian Kernel Projection Classifier

Description

summary.bkpc produces two sets of summary statistics for each variable: mean and standard deviation (ignoring autocorrelation of the chain) of the sample distribution and quantiles of the sample distribution using the quantiles argument.

Usage

## S3 method for class 'bkpc'
summary(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), n.burnin = 0, ...)

Arguments

object	an object of class "bkpc".
quantiles	a vector of quantiles to evaluate for each variable.
n.burnin	number of burn-in iterations to discard from the thinned sample.
...	Currently not used.

Author(s)

K. Domijan

See Also

bkpc plot.bkpc

Examples

set.seed(-88106935)

data(iris)
testset <- sample(1:150,50)

train <- as.matrix(iris[-testset,-5])
test <- as.matrix(iris[testset,-5])

wtr <- iris[-testset, 5]
wte <- iris[testset, 5]

result <- bkpc(train, y = wtr,  n.iter = 1000,  thin = 10, n.kpc = 2, 
intercept = FALSE, rotate = TRUE)


summary(result, n.burnin = 0)

BKPC documentation built on May 1, 2019, 9:10 p.m.