Description Usage Arguments Value
cox.variational.indep
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
diagonal covariance matrix.
1 2 | cox.variational.indep(y, X, S, wt, beta.start, tau.start = 100,
tol = sqrt(.Machine$double.eps), verbose = TRUE, hess = TRUE)
|
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 compute the hessian after convergence (slow) |
list composed of these 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
diagV
: vector of diagonal entries of the estimated covariance matrix for the posterior of the spatial random effects at convergence of the variational approximation
ltau
: estimated precision component for the spatial random effect
hessian
: estimated hessian matrix for beta
, M
, log(diagV)
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
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