Type: Package
Title: Bayesian Copula Regression
Version: 0.1.3
Date: 2020-11-30
Maintainer: Ethan Alt <ethanalt@live.unc.edu>
Description: Tools for Bayesian copula generalized linear models (GLMs). The sampling scheme is based on Pitt, Chan, and Kohn (2006) <doi:10.1093/biomet/93.3.537>. Regression parameters (including coefficients and dispersion parameters) are estimated via the adaptive random walk Metropolis approach developed by Haario, Saksman, and Tamminen (1999) <doi:10.1007/s001800050022>. The prior for the correlation matrix is based on Hoff (2007) <doi:10.1214/07-AOAS107>.
Depends: R (≥ 3.6.0)
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports: Rcpp (≥ 1.0.3), stats
LinkingTo: Rcpp, RcppArmadillo, RcppDist, mvtnorm
RoxygenNote: 7.1.1
Encoding: UTF-8
URL: https://github.com/ethan-alt/bayescopulareg
BugReports: https://github.com/ethan-alt/bayescopulareg/issues
NeedsCompilation: yes
Packaged: 2020-11-30 16:32:50 UTC; ethanalt
Author: Ethan Alt [aut, cre], Yash Bhosale [aut]
Repository: CRAN
Date/Publication: 2020-11-30 17:10:06 UTC

bayescopulareg: Bayesian Copula Regression

Description

Tools for Bayesian copula generalized linear models (GLMs). The sampling scheme is based on Pitt, Chan, and Kohn (2006) <doi:10.1093/biomet/93.3.537>. Regression parameters (including coefficients and dispersion parameters) are estimated via the adaptive random walk Metropolis approach developed by Haario, Saksman, and Tamminen (1999) <doi:10.1007/s001800050022>. The prior for the correlation matrix is based on Hoff (2007) <doi:10.1214/07-AOAS107>.

Author(s)

Maintainer: Ethan Alt ethanalt@live.unc.edu

Authors:

See Also

Useful links:

Sample from Bayesian copula GLM

Description

Sample from a GLM via Bayesian copula regression model. Uses random-walk Metropolis to update regression coefficients and dispersion parameters. Assumes Inverse Wishart prior on augmented data.

Usage

bayescopulaglm(
  formula.list,
  family.list,
  data,
  histdata = NULL,
  b0 = NULL,
  c0 = NULL,
  alpha0 = NULL,
  gamma0 = NULL,
  Gamma0 = NULL,
  S0beta = NULL,
  sigma0logphi = NULL,
  v0 = NULL,
  V0 = NULL,
  beta0 = NULL,
  phi0 = NULL,
  M = 10000,
  burnin = 2000,
  thin = 1,
  adaptive = TRUE
)

Arguments

formula.list

A J-dimensional list of formulas giving how the endpoints are related to the covariates

family.list

A J-dimensional list of families giving how each endpoint is distributed. See help(family)

data

A data frame containing all response variables and covariates. Variables must be named.

histdata

Optional historical data set for power prior on \beta, \phi

b0

Optional power prior hyperparameter. Ignored if is.null(histdata). Must be a number between (0, 1] if histdata is not NULL

c0

A J-dimensional vector for \beta \mid \phi prior covariance. If NULL, sets c_0 = 10000 for each endpoint

alpha0

A J-dimensional vector giving the shape hyperparameter for each dispersion parameter on the prior on \phi. If NULL sets \alpha_0 = .01 for each dispersion parameter

gamma0

A J-dimensional vector giving the rate hyperparameter for each dispersion parameter on the prior on \phi. If NULL sets \alpha_0 = .01 for each dispersion parameter

Gamma0

Initial value for correlation matrix. If NULL defaults to the correlation matrix from the responses.

S0beta

A J-dimensional list for the covariance matrix for random walk metropolis on beta. Each matrix must have the same dimension as the corresponding regression coefficient. If NULL, uses solve(crossprod(X))

sigma0logphi

A J-dimensional vector giving the standard deviation on \log(\phi) for random walk metropolis. If NULL defaults to 0.1

v0

An integer scalar giving degrees of freedom for Inverse Wishart prior. If NULL defaults to J + 2

V0

An integer giving inverse scale parameter for Inverse Wishart prior. If NULL defaults to diag(.001, J)

beta0

A J-dimensional list giving starting values for random walk Metropolis on the regression coefficients. If NULL, defaults to the GLM MLE

phi0

A J-dimensional vector giving initial values for dispersion parameters. If NULL. Dispersion parameters will always return 1 for binomial and Poisson models

M

Number of desired posterior samples after burn-in and thinning

burnin

burn-in parameter

thin

post burn-in thinning parameter

adaptive

logical indicating whether to use adaptive random walk MCMC to estimate parameters. This takes longer, but generally has a better acceptance rate

Value

A named list. ["betasample"] gives a J-dimensional list of sampled coefficients as matrices. ["phisample"] gives a M \times J matrix of sampled dispersion parameters. ["Gammasample"] gives a J \times J \times M array of sampled correlation matrices. ["betaaccept"] gives a M \times J matrix where each row indicates whether the proposal for the regression coefficient was accepted. ["phiaccept"] gives a M \times J matrix where each row indicates whether the proposal for the dispersion parameter was accepted

Examples

set.seed(1234)
n <- 100
M <- 100

x <- runif(n, 1, 2)
y1 <- 0.25 * x + rnorm(100)
y2 <- rpois(n, exp(0.25 * x))

formula.list <- list(y1 ~ 0 + x, y2 ~ 0 + x)
family.list <- list(gaussian(), poisson())
data = data.frame(y1, y2, x)

## Perform copula regression sampling with default
## (noninformative) priors
sample <- bayescopulaglm(
  formula.list, family.list, data, M = M, burnin = 0, adaptive = F
)
## Regression coefficients
summary(do.call(cbind, sample$betasample))

## Dispersion parameters
summary(sample$phisample)

## Posterior mean correlation matrix
apply(sample$Gammasample, c(1,2), mean)

## Fraction of accepted betas
colMeans(sample$betaaccept)

## Fraction of accepted dispersion parameters
colMeans(sample$phiaccept)

Predictive posterior sample from copula GLM

Description

Sample from the predictive posterior density of a copula generalized linear model regression

Usage

## S3 method for class 'bayescopulaglm'
predict(object, newdata, nsims = 1, ...)

Arguments

object

Result from calling bayescopulaglm

newdata

data.frame of new data

nsims

number of posterior draws to take. The default and minimum is 1. The maximum is the number of simulations in object

...

further arguments passed to or from other methods

Value

array of dimension c(n, J, nsims) of predicted values, where J is the number of endpoints

Examples

set.seed(1234)
n <- 100
M <- 1000

x <- runif(n, 1, 2)
y1 <- 0.25 * x + rnorm(100)
y2 <- rpois(n, exp(0.25 * x))

formula.list <- list(y1 ~ 0 + x, y2 ~ 0 + x)
family.list <- list(gaussian(), poisson())
data = data.frame(y1, y2, x)

## Perform copula regression sampling with default
## (noninformative) priors
sample <- bayescopulaglm(
  formula.list, family.list, data, M = M
)
predict(sample, newdata = data)

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