#' @docType class
#' @title Single level of random factors in HMSC
#'
#' @description Specifies the structure for the single level of random factors, whether the level
#' is assumed to be spatial or not, the spatial coordinates,
#'
#'
#' @param pi determines the unique IDs for the distinct units on this level of random factors
#' @param s matrix of coordinates in the
#' @param sDim number of spatial dimensions
#' @param N number of unique units on this level
#'
#' @examples
#' HmscRandomLevel$new(data.frame(s1=c(1:10),s2=c(10:1)))
#' HmscRandomLevel$new(pi=as.factor(1:10))
#'
#' @export
Hmsc <- R6::R6Class("Hmsc",
public = list(
# data
Y = NULL,
X = NULL,
rL = NULL,
Xs = NULL,
Xv = NULL,
Tr = NULL,
C = NULL,
Pi = NULL,
# dimensions
ny = NULL,
ns = NULL,
nc = NULL,
nr = NULL,
nt = NULL,
nf = NULL,
ncs = NULL,
ncv = NULL,
# names
spNames = NULL,
covNames = NULL,
trNames = NULL,
dist = NULL,
# priors
initialize = function(Y=NULL, X=NULL, Pi=NULL, rL=NULL, Xs=NULL, Xv=NULL, Tr=NULL, C=NULL, dist="normal", priors=NULL){
# combine Hmsc and set data functions from Matlab
},
setData = function(Y=NULL, X=NULL, Pi=NULL, rL=NULL, Xs=NULL, Xv=NULL, dist="normal", spNames=NULL,
trNames=NULL, covNames=NULL, ...){
# Hmsc and set data functions from Matlab
},
setPriors = function(priors=NULL){
# set priors functions from Matlab
},
setMcmcParameters = function(){
# set McmcParameters function from Matlab
},
sample = function(){
},
getPosterior = function(){
# combines setPostThinning, saves a copy of that in the class and returns the posterior as mcmc object
}
)
)
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