BKPC (version 1.0.1)

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

See Also

bkpc plot.bkpc

Examples

# NOT RUN {
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)
# }