R: Marginals for Data Simulation, Correlation Assessment,...

marginal.gc {gcKrig}

R Documentation

Marginals for Data Simulation, Correlation Assessment, Likelihood Inference and Spatial Prediction in Gaussian Copula Models for Geostatistical Data

Description

Class of marginals available in gcKrig library for geostatistical data simulation, correlation structure assessment (both continuous and discrete marginals) and model inferences (discrete marginals only). In former cases parameters of the marginals are given by users, otherwise parameters are estimated from the data (except when doing prediction with function predgc, users can choose to either input known estimates or estimate the parameters with input data).

Details

The package gcKrig does not include inference and prediction functionalities for continuous marginals. For inference with continuous marginals, see Masarotto and Varin (2012).

By default, when the marginals are discrete, they are used for estimation with function mlegc and prediction with function predgc. They can be used in function simgc and corrTG as well for the purpose of data simulation and correlation computation in a transformed Gaussian random field (Han and De Oliveira, 2016), if parameter values are specified.

For continuous marginals, they are used for simulation with function simgc and correlation computation with corrTG only, so parameters should always be specified.

Value

At the moment, the following marginals are implemented:

`beta.gc`	beta marginals.
`binomial.gc`	binomial marginals.
`gm.gc`	gamma marginals.
`gaussian.gc`	Gaussian marginals.
`negbin.gc`	negative binomial marginals.
`poisson.gc`	Poisson marginals.
`weibull.gc`	Weibull marginals.
`zip.gc`	zero-inflated Poisson marginals.

Author(s)

Zifei Han hanzifei1@gmail.com

References

Cribari-Neto, F. and Zeileis, A. (2010) Beta regression in R. Journal of Statistical Software, 34(2), 1–24. doi: 10.18637/jss.v034.i02.

Ferrari, S.L.P. and Cribari-Neto, F. (2004) Beta regression for modeling rates and proportions. Journal of Applied Statistics, 31:799-815.

Han, Z. and De Oliveira, V. (2016) On the correlation structure of Gaussian copula models for geostatistical count data. Australian and New Zealand Journal of Statistics, 58:47-69.

Masarotto, G. and Varin, C. (2012) Gaussian copula marginal regression. Electronic Journal of Statistics, 6:1517-1549.

Masarotto, G. and Varin C. (2017) Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1–26. doi: 10.18637/jss.v077.i08.