SDALGCP: SDALGCP: A package to make continuous inference from...

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

Description

The SDALGCP package provides four main functions: SDALGCPMCML, SDALGCPMCML_ST, SDALGCPPred and SDALGCPPred_ST.

SDALGCP functions

The SDALGCPMCML function uses Monte Carlo Maximum Likelihood to estimate the parameter of a poisson log-linear model with spatially continuous random effect for static spatial case.

The SDALGCPPred function delivers spatially discrete prediction of the incidence and the covariate adjusted relative risk and spatially continuous prediction of the covariate adjusted relative risk for static spatial case.

The SDALGCPMCML_ST function uses Monte Carlo Maximum Likelihood to estimate the parameter of a poisson log-linear model with spatially continuous random effect for spatio-temporal case.

The SDALGCPPred_ST function delivers spatially discrete prediction of the incidence and the covariate adjusted relative risk and spatially continuous prediction of the covariate adjusted relative risk for spatio-temporal case.

Functions such as summary, confint and print also can be applied to the output.

Author(s)

Olatunji O. Johnson, Emanuele Giorgi, Peter Diggle. All from CHICAS, Lancaster Medical School, Faculty of Health and Medicine, Lancaster University

References

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

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

Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.

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