rvbm {rpud} | R Documentation |
The rvbm
method trains a Bayesian classification model
with Gaussian process priors. It is based on the variational sparse
approximation technique used by Girolami and Rogers, as well as the
vbmp
package implemented by Lama and Girolami.
In the non-free rpudplus
add-on, the rvbm
method
is implemented in NVIDIA CUDA, and assumes necessary
double precision CUDA hardware support.
While there is no GPU-acceleration in the free rpud
package,
it includes optimized R code nonetheless.
## Default S3 method: rvbm(X, t.class, X.TEST, t.class.TEST, theta = rep(1, ncol(X)), control = list())
X |
Feature matrix for the model to fit. |
t.class |
Response vector for the feature matrix. |
X.TEST |
Out-of-sample test matrix for prediction. |
t.class.TEST |
Out-of-sample response values. |
theta |
Covariance parameters for each feature dimension. |
control |
A list of control parameters including the following:
|
Sharing a single covariance function across all classes.
An S3 object of class "rvbm" with the following components:
Kc |
Number of response categories |
Ptest |
Matrix of predicted posterior probability |
X |
Feature matrix of the Gaussian process model |
cholPHI |
Upper triangular Cholesky factor of the symmetric kernel matrix |
Y |
Matrix of auxiliary variables |
M |
Matrix of latent Gaussian process random variables |
THETA |
covariance kernel hyperparameters |
sKernelType |
Kernel type of the Gaussian process model |
testErr |
Percentage of posterior prediction error. |
PL |
Predictive log likelihood during each model fitting iteration. |
lowerBound |
Lower bound estimates of marginal likelihood during each model fitting iteration. |
Chi Yau (based on the vbmp
R doc by Lama and Girolami)
chi.yau@r-tutor.com
Girolami M, Rogers S, Variational Bayesian Multinomial Probit
Regression with Gaussian Process Priors, Neural Computation 18, 1790-1817 (2006).
Lama N, Girolami M vbmp: Variational Bayesian Multinomial Probit
Regression for multi-class classification in R, Bioinformatics 24(1):135-136 (2008).
http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1
predict.rvbm
plot.rvbm
rpusvm
vbmp
## Not run: library(rpud) x <- rvbm.sample.train$X y <- rvbm.sample.train$t.class model.rvbm <- rvbm( x, y, x, y, theta = rep(1, ncol(x)), control = list( sKernelType="gaussian", bThetaEstimate=TRUE, bMonitor=TRUE, maxIts=12, InfoLevel=1) ) summary(model.rvbm) ## End(Not run)