cox.variational: Estimate the regression parameters of a Cox process model...

In wrbrooks/cox: Estimate Cox process regression models

Description

cox.variational uses a variational approximation to estimate the parameters of a Cox process regression model with spatial random effects. For this function, the variational approximation to the posterior distribution of the spatial random effects is a multivariate normal with general covariance matrix.

Usage

cox.variational(y, X, S, wt, beta.start, tau.start = 100,
  tol = sqrt(.Machine$double.eps), verbose = TRUE, hess = TRUE)

Arguments

y	vector of response data
X	design matrix for fized effects
S	design matrix for the spatial random effects
wt	vector of observation weights
beta.start	starting values for iteration to estimate the fixed effect coefficients
tau.start	initial value of the precision of the random effects
tol	tolerance for judging convergence of the algorithm
verbose	logical indicating whether to write detailed progress reports to standard output
hess	logical indicating whether to estimate the Hessian matrix

Value

list of results containing the following elements:

beta: estimated vector of fixed effect regression coefficients

M: estimated mean vector for the posterior of the spatial random effects at the converged value of the variational approximation

V: estimated covariance matrix for the posterior of the spatial random effects at the converged value of the variational approximation

ltau: estimated precision component for the spatial random effect

hessian: estimated hessian matrix for beta, M, and ltau at convergence (estimated by call to optim)

neg.log.lik: negative of the variational lower bound on the marginal log-likelihood at convergence

wrbrooks/cox documentation built on May 4, 2019, 11:58 a.m.