Description Usage Arguments Examples
A function to implement multinomial probit regression via Bayesian Addition Regression Trees using partial marginal data augmentation.
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x.train |
Training data predictors. |
y.train |
Training data observed classes. |
x.test |
Test data predictors. |
Prior |
List of Priors for MPBART: e.g., Prior = list(nu=p+2, V= diag(p - 1), ntrees=200, kfac=2.0, pbd=1.0, pb=0.5 , beta = 2.0, alpha = 0.95, nc = 100, priorindep = 0, minobsnode = 10) |
Mcmc |
List of MCMC starting values, burn-in ...: e.g., list(sigma0 = diag(p - 1), keep = 1, burn = 100, ndraws = 1000, keep_sigma_draws=FALSE) |
seedvalue |
random seed value: e.g., seedvalue = 99 |
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library(mpbart)
p=3
train_wave = mlbench.waveform(50)
test_wave = mlbench.waveform(100)
traindata = data.frame(train_wave$x, y = train_wave$classes)
testdata = data.frame(test_wave$x, y = test_wave$classes)
x.train = data.frame(train_wave$x)
x.test = data.frame(test_wave$x)
y.train = train_wave$classes
sigma0 = diag(p-1)
burn = 100
ndraws = 200 # a higher number >=1000 is more appropriate.
Mcmc1=list(sigma0=sigma0, burn = burn, ndraws = ndraws)
Prior1 = list(nu=p+2,
V=(p+2)*diag(p-1),
ntrees = 5, #typically 200 trees is good
kfac = 2.0,
pbd = 1.0,
pb = 0.5,
alpha = 0.99,
beta = 2.0,
nc = 200,
priorindep = FALSE)
out = rmpbart(x.train = x.train, y.train = y.train, x.test = x.test,
Prior = Prior1, Mcmc=Mcmc1, seedvalue = 99)
#confusion matrix train
table(y.train, out$predicted_class_train)
table(y.train==out$predicted_class_train)/sum(table(y.train==out$predicted_class_train))
#confusion matrix test
table(test_wave$classes, out$predicted_class_test)
test_err <- sum(test_wave$classes != out$predicted_class_test)/
sum(table(test_wave$classes == out$predicted_class_test))
cat("test error :", test_err )
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