Aggregated_poisson_log_MCML: Aggregated_poisson_log_MCML function

In olatunjijohnson/SDALGCP: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data

Description

This function performs Monte Carlo maximum likelihood (MCML) estimation for the geostatistical Poisson model with log link function.

Usage

Aggregated_poisson_log_MCML(
  y,
  D,
  m,
  corr,
  par0,
  control.mcmc,
  S.sim,
  Denominator,
  messages
)

Arguments

y	the data
D	the design matrix
m	the offset term
corr	the correlation matrix from exponential correlation function
par0	the initial parameter of the fixed effects beta, the variance sigmasq and the scale parameter phi, specified as c(beta, sigma2, phi)
control.mcmc	output from controlmcmcSDA.
S.sim	the posterior sample of the linear predictor given the initial parameters
Denominator	the value of the denominator of the likelihood
messages	logical; if message=TRUE, it prints the results objective function and the parameters at every phi iteration. Default is FALSE.

Details

The function helps to obtain the MCML estimate for a given value of correlation matrix, i.e for a given value of the scale parameter phi.

Value

estimate: estimates of the model parameters; beta's and with sigma2 on the log scale

covariance: covariance matrix of the MCML estimates.

log.lik: maximum value of the log-likelihood.

S: the linear predictor given the initial parameter

Author(s)

Olatunji O. Johnson [e-mail]

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]

References

Giorgi, E., & Diggle, P. J. (2017). PrevMap: an R package for prevalence mapping. Journal of Statistical Software, 78(8), 1-29. doi:10.18637/jss.v078.i08.

Christensen, O. F. (2004). Monte Carlo maximum likelihood in model-based geostatistics. Journal of Computational and Graphical Statistics 13, 702-718.

See Also

controlmcmcSDA

olatunjijohnson/SDALGCP documentation built on March 20, 2021, 4:24 a.m.