print.binaryGP {binaryGP} R Documentation

## Print Fitted results of Binary Gaussian Process

### Description

The function shows the estimation results by binaryGP_fit.

### Usage

## S3 method for class 'binaryGP'
print(x, ...)


### Arguments

 x a class binaryGP object estimated by binaryGP_fit. ... for compatibility with generic method print.

### Value

a list of estimates by binaryGP_fit.

### Author(s)

Chih-Li Sung <iamdfchile@gmail.com>

### See Also

binaryGP_fit for estimation of the binary Gaussian process.

### Examples


library(binaryGP)

#####      Testing function: cos(x1 + x2) * exp(x1*x2) with TT sequences      #####
#####   Thanks to Sonja Surjanovic and Derek Bingham, Simon Fraser University #####
test_function <- function(X, TT)
{
x1 <- X[,1]
x2 <- X[,2]

eta_1 <- cos(x1 + x2) * exp(x1*x2)

p_1 <- exp(eta_1)/(1+exp(eta_1))
y_1 <- rep(NA, length(p_1))
for(i in 1:length(p_1)) y_1[i] <- rbinom(1,1,p_1[i])
Y <- y_1
P <- p_1
if(TT > 1){
for(tt in 2:TT){
eta_2 <- 0.3 * y_1 + eta_1
p_2 <- exp(eta_2)/(1+exp(eta_2))
y_2 <- rep(NA, length(p_2))
for(i in 1:length(p_2)) y_2[i] <- rbinom(1,1,p_2[i])
Y <- cbind(Y, y_2)
P <- cbind(P, p_2)
y_1 <- y_2
}
}

return(list(Y = Y, P = P))
}

set.seed(1)
n <- 30
n.test <- 10
d <- 2
X <- matrix(runif(d * n), ncol = d)

##### without time-series #####
Y <- test_function(X, 1)$Y ## Y is a vector binaryGP.model <- binaryGP_fit(X = X, Y = Y) print(binaryGP.model) # print estimation results summary(binaryGP.model) # significance results ##### with time-series, lag 1 ##### Y <- test_function(X, 10)$Y  ## Y is a matrix with 10 columns

binaryGP.model <- binaryGP_fit(X = X, Y = Y, R = 1)
print(binaryGP.model)   # print estimation results
summary(binaryGP.model) # significance results



[Package binaryGP version 0.2 Index]