rsglmm_mcmc: Restricted Spatial Generalized Linear Mixed model
In DouglasMesquita/RASCO: An R Package to Alleviate Spatial Confounding

rsglmm_mcmc

R Documentation

Restricted Spatial Generalized Linear Mixed model

Fit a Restricted Spatial Generalized Linear Mixed model using ngspatial

rsglmm_mcmc(
  data,
  formula,
  family,
  E,
  n,
  W,
  area,
  proj,
  nsamp,
  burnin,
  lag,
  attractive = round(0.5 * (nrow(W)/2)),
  ...
)

`data`	a data frame or list containing the variables in the model.
`formula`	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`family`	allowed families are: "gaussian", "poisson" and "binomial".
`E`	known component, in the mean for the Poisson likelihoods defined as E = exp(η), where η is the linear predictor. Default = 1.
`n`	a vector containing the number of trials for the binomial likelihood, or the number of required successes for the nbinomial2 likelihood. Default value is set to 1.
`W`	adjacency matrix.
`area`	areal variable name in `data`.
`proj`	"hh"
`nsamp`	number of samples. Default = 1000.
`burnin`	burn-in size.
`lag`	lag parameter.
`attractive`	the number of attractive Moran eigenvectors to use. See ?ngspatial::sparse.sglmm for more information.
`...`	other parameters used in ?ngspatial::sparse.sglmm

`$unrestricted`	A list containing $sample: a sample of size nsamp for all parameters in the model $summary_fixed: summary measures for the coefficients $summary_hyperpar: summary measures for hyperparameters $summary_random: summary measures for random quantities
`$restricted`	A list containing $sample: a sample of size nsamp for all parameters in the model $summary_fixed: summary measures for the coefficients $summary_hyperpar: summary measures for hyperparameters $summary_random: summary measures for random quantities
`$out`	ngspatial output
`$time`	time elapsed for fitting the model