Help for package binaryGP

Type:

Package

Title:

Fit and Predict a Gaussian Process Model with (Time-Series) Binary Response

Version:

0.2

Date:

2017-09-17

Author:

Chih-Li Sung

Maintainer:

Chih-Li Sung <iamdfchile@gmail.com>

Description:

Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <doi:10.48550/arXiv.1705.02511>.

License:

GPL-2 | GPL-3

LazyData:

TRUE

Imports:

Rcpp (≥ 0.12.0), lhs (≥ 0.10), logitnorm (≥ 0.8.29), nloptr (≥ 1.0.4), GPfit (≥ 1.0-0), stats, graphics, utils, methods

LinkingTo:

Rcpp, RcppArmadillo

RoxygenNote:

5.0.1

Depends:

R (≥ 2.14.1)

NeedsCompilation:

yes

Packaged:

2017-09-18 16:24:05 UTC; apple

Repository:

CRAN

Date/Publication:

2017-09-19 08:34:21 UTC

Binary Gaussian Process (with/without time-series)

Description

The function fits Gaussian process models with binary response. The models can also include time-series term if a time-series binary response is observed. The estimation methods are based on PQL/PQPL and REML (for mean function and correlation parameter, respectively).

Usage

binaryGP_fit(X, Y, R = 0, L = 0, corr = list(type = "exponential", power =
  2), nugget = 1e-10, constantMean = FALSE, orthogonalGP = FALSE,
  converge.tol = 1e-10, maxit = 15, maxit.PQPL = 20, maxit.REML = 100,
  xtol_rel = 1e-10, standardize = FALSE, verbose = TRUE)

Arguments

X

a design matrix with dimension n by d.

Y

a response matrix with dimension n by T. The values in the matrix are 0 or 1. If no time-series, T = 1. If time-series is included, i.e., T > 1, the first column is the response vector of time 1, and second column is the response vector of time 2, and so on.

R

a positive integer specifying the order of autoregression only if time-series is included. The default is 1.

L

a positive integer specifying the order of interaction between X and previous Y only if time-series is included. The value couldn't nbe larger than R. The default is 1.

corr

a list of parameters for the specifing the correlation to be used. Either exponential correlation function or Matern correlation function can be used. See corr_matrix for details.

nugget

a positive value to use for the nugget. If NULL, a nugget will be estimated. The default is 1e-10.

constantMean

logical. TRUE indicates that the Gaussian process will have a constant mean, plus time-series structure if R or T is greater than one; otherwise the mean function will be a linear regression in X, plus an intercept term and time-series structure.

orthogonalGP

logical. TRUE indicates that the orthogonal Gaussian process will be used. Only available when corr is list(type = "exponential", power = 2).

converge.tol

convergence tolerance. It converges when relative difference with respect to \eta_t is smaller than the tolerance. The default is 1e-10.

maxit

a positive integer specifying the maximum number of iterations for estimation to be performed before the estimates are convergent. The default is 15.

maxit.PQPL

a positive integer specifying the maximum number of iterations for PQL/PQPL estimation to be performed before the estimates are convergent. The default is 20.

maxit.REML

a positive integer specifying the maximum number of iterations in lbfgs for REML estimation to be performed before the estimates are convergent. The default is 100.

xtol_rel

a postive value specifying the convergence tolerance for lbfgs. The default is 1e-10.

standardize

logical. If TRUE, each column of X will be standardized into [0,1]. The default is FALSE.

verbose

logical. If TRUE, additional diagnostics are printed. The default is TRUE.

Details

Consider the model

logit(p_t(x))=\eta_t(x)=\sum^R_{r=1}\varphi_ry_{t-r}(\mathbf{x})+\alpha_0+\mathbf{x}'\boldsymbol{\alpha}+\sum^L_{l=1}\boldsymbol{\gamma}_l\textbf{x}y_{t-l}(\mathbf{x})+Z_t(\mathbf{x}),

where p_t(x)=Pr(y_t(x)=1) and Z_t(\cdot) is a Gaussian process with zero mean, variance \sigma^2, and correlation function R_{\boldsymbol{\theta}}(\cdot,\cdot) with unknown correlation parameters \boldsymbol{\theta}. The power exponential correlation function is used and defined by

R_{\boldsymbol{\theta}}(\mathbf{x}_i,\mathbf{x}_j)=\exp\{-\sum^{d}_{l=1}\frac{(x_{il}-x_{jl})^p}{\theta_l} \},

where p is given by power. The parameters in the mean functions including \varphi_r,\alpha_0,\boldsymbol{\alpha},\boldsymbol{\gamma}_l are estimated by PQL/PQPL, depending on whether the mean function has the time-series structure. The parameters in the Gaussian process including \boldsymbol{\theta} and \sigma^2 are estimated by REML. If orthogonalGP is TRUE, then orthogonal covariance function (Plumlee and Joseph, 2016) is employed. More details can be seen in Sung et al. (unpublished).

Value

alpha_hat

a vector of coefficient estimates for the linear relationship with X.

varphi_hat

a vector of autoregression coefficient estimates.

gamma_hat

a vector of the interaction effect estimates.

theta_hat

a vector of correlation parameters.

sigma_hat

a value of sigma estimate (standard deviation).

nugget_hat

if nugget is NULL, then return a nugget estimate, otherwise return nugget.

orthogonalGP

orthogonalGP.

X

data X.

Y

data Y.

R

order of autoregression.

L

order of interaction between X and previous Y.

corr

a list of parameters for the specifing the correlation to be used.

Model.mat

model matrix.

eta_hat

eta_hat.

standardize

standardize.

mean.x

mean of each column of X. Only available when standardize=TRUE, otherwise mean.x=NULL.

scale.x

max(x)-min(x) of each column of X. Only available when standardize=TRUE, otherwise scale.x=NULL.

Author(s)

Chih-Li Sung <iamdfchile@gmail.com>

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

Predictions of Binary Gaussian Process

Description

The function computes the predicted response and its variance as well as its confidence interval.

Usage

## S3 method for class 'binaryGP'
predict(object, xnew, conf.level = 0.95,
  sim.number = 101, ...)

Arguments

object

a class binaryGP object estimated by binaryGP_fit.

xnew

a testing matrix with dimension n_new by d in which each row corresponds to a predictive location.

conf.level

a value from 0 to 1 specifying the level of confidence interval. The default is 0.95.

sim.number

a positive integer specifying the simulation number for Monte-Carlo method. The default is 101.

...

for compatibility with generic method predict.

Value

mean

a matrix with dimension n_new by T displaying predicted responses at locations xnew.

var

a matrix with dimension n_new by T displaying predictive variances at locations xnew.

upper.bound

a matrix with dimension n_new by T displaying upper bounds with conf.level confidence level.

lower.bound

a matrix with dimension n_new by T displaying lower bounds with conf.level confidence level.

y_pred

a matrix with dimension n_new by T displaying predicted binary responses at locations xnew.

Author(s)

Chih-Li Sung <iamdfchile@gmail.com>

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)
X.test <- matrix(runif(d * n.test), ncol = d)

##### without time-series #####
Y <- test_function(X, 1)$Y  ## Y is a vector
test.out <- test_function(X.test, 1)
Y.test <- test.out$Y
P.true <- test.out$P

# fitting
binaryGP.model <- binaryGP_fit(X = X, Y = Y)

# prediction
binaryGP.prediction <- predict(binaryGP.model, xnew = X.test)
print(binaryGP.prediction$mean)
print(binaryGP.prediction$var)
print(binaryGP.prediction$upper.bound)
print(binaryGP.prediction$lower.bound)

##### with time-series #####
Y <- test_function(X, 10)$Y  ## Y is a matrix with 10 columns
test.out <- test_function(X.test, 10)
Y.test <- test.out$Y
P.true <- test.out$P

# fitting
binaryGP.model <- binaryGP_fit(X = X, Y = Y, R = 1)

# prediction
binaryGP.prediction <- predict(binaryGP.model, xnew = X.test)
print(binaryGP.prediction$mean)
print(binaryGP.prediction$var)
print(binaryGP.prediction$upper.bound)
print(binaryGP.prediction$lower.bound)

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>

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

Summary of Fitting a Binary Gaussian Process

Description

The function summarizes estimation and significance results by binaryGP_fit.

Usage

## S3 method for class 'binaryGP'
summary(object, ...)

Arguments

object

a class binaryGP object estimated by binaryGP_fit.

...

for compatibility with generic method summary.

Value

A table including the estimates by binaryGP_fit, and the correponding standard deviations, Z-values and p-values.

Author(s)

Chih-Li Sung <iamdfchile@gmail.com>

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

Binary Gaussian Process (with/without time-series)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Predictions of Binary Gaussian Process

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Print Fitted results of Binary Gaussian Process

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Summary of Fitting a Binary Gaussian Process

Description

Usage

Arguments

Value

Author(s)

See Also

Examples