print.binaryGP: Print Fitted results of Binary Gaussian Process In binaryGP: Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response

Description

The function shows the estimation results by `binaryGP_fit`.

Usage

 ```1 2``` ```## 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 <[email protected]>

`binaryGP_fit` for estimation of the binary Gaussian process.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50``` ```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 ```

binaryGP documentation built on Sept. 19, 2017, 9:02 a.m.