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#' MGWR
#' to be documented
#' @usage MGWR(Y,XC,XV,ALL_X=NULL,S,H,NN, kernels,adaptive=FALSE,Type="GD",
#' SE=FALSE,isgcv=FALSE,W=NULL,TP=NULL,
#' KernelTP='sheppard',kWtp=8,Model,indexG=NULL,Wd=NULL,dists=NULL,
#' doMC=FALSE,ncore=1,S_out=NULL,get_ts=FALSE)
#' @param Y A vector
#' @param XC A matrix with covariates with stationnary parameters
#' @param XV A matrix with covariates with spatially varying parameters
#' @param S A matrix with variables used in kernel
#' @param H A vector of bandwidths
#' @param NN Number of spatial Neighbours for kernels computations.
#' @param kernels A vector of kernel types
#' @param adaptive A vector of boolean to choose adaptive version for each kernel.
#' @param Type Type of Genelarized kernel product ('GD' only
#' spatial,'GDC' spatial + a categorical variable,
#' 'GDX' spatial + a continuous variable and other
#' combinations like 'GDXXC','GDXCC',...)
#' @param SE If standard error are computed, default FALSE
#' @param isgcv leave one out cross validation, default FALSE
#' @param W A weight matrix for spatial autocorrelation
#' @param TP index of target points, default NULL
#' @param indexG Precomputed Matrix of indexes of NN neighbors, default NULL.
#' @param dists Precomputed Matrix of spatial distances, default NULL
#' @param noisland A boolean to avoid isle with no neighbours for non adaptive kernel, default FALSE
#' @param Model character containing the type of model:
#' Possible values are "OLS", "SAR", "GWR" (default), "MGWR" ,
#' "MGWRSAR_0_0_kv","MGWRSAR_1_0_kv", "MGWRSAR_0_kc_kv",
#' "MGWRSAR_1_kc_kv", "MGWRSAR_1_kc_0". See Details for more
#' @return a list of object for MGWRSAR wrapper
#' @noRd
MGWR<-function(Y,XC,XV,ALL_X=NULL,S,H,NN, kernels,adaptive=FALSE, Type = "GD",SE=FALSE, isgcv=FALSE,W=NULL,TP=NULL,Model,indexG=NULL,Wd=NULL,dists=NULL,doMC=FALSE,ncore=1,S_out=FALSE,noisland=FALSE,get_ts=FALSE){
se = NULL
sev = NULL
pred=FALSE
coords=S[,1:2]
if(ncol(S)>2) Z=matrix(S[,3:ncol(S)]) else Z=NULL
if (!is.null(XC)) XC <- as.matrix(XC)
if (!is.null(XV)) XV <- as.matrix(XV)
ALL_X = cbind(XC, XV)
n <- NROW(Y)
m <- ncol(XV)
K <- ncol(XC)
if (Model %in% c("MGWRSAR_0_kc_kv", "MGWRSAR_0_0_kv")) {
PhWy=PhWY_R(as.matrix(Y), as.matrix(ALL_X), W, rep(1,n))
if (Model == "MGWRSAR_0_kc_kv")
XC = cbind(XC, PhWy)
else XC = as.matrix(PhWy,ncol=1)
}
### prep wd
########
if(is.null(Wd)){
Z=S[TP,]
stage1=prep_w(H=H,kernels=kernels,coord_i=Z,coord_j=S,NN=NN,ncolX=ncol(XV),Type=Type,adaptive=adaptive,dists=dists,indexG=indexG,rowNorm=TRUE,noisland=noisland)
indexG=stage1$indexG
dists=stage1$dists
Wd=stage1$Wd
}
mgwr1=mgwr_stage1(Y=Y,XV=XV,XC=XC,ALL_X=ALL_X,W=W,indexG=indexG,Wd=Wd,NN=NN,isgcv=isgcv,TP=TP,SE=SE,Model=Model,doMC=doMC,ncore=ncore)
if (Model %in% c("MGWRSAR_1_kc_kv","MGWRSAR_1_kc_0")) {
W2=W
} else W2=NULL
model=gwr_beta(Y=mgwr1$ZZ,XV=XV,ALL_X=ALL_X,TP=TP,indexG=indexG,Wd=Wd,NN=NN,W=W2,isgcv=isgcv,SE=SE,doMC=doMC,ncore=ncore, pred=pred,get_ts=get_ts)
if(SE & !isgcv) list(Betac=mgwr1$Betac,Betav=model$Betav,SEV=model$SEV,se=mgwr1$se,edf=n-model$tS-length(mgwr1$Betac),tS=model$tS+length(mgwr1$Betac)) else if(get_ts) list(Betac=mgwr1$Betac,Betav=model$Betav,SEV=NULL,edf=NULL,tS=model$tS) else list(Betac=mgwr1$Betac,Betav=model$Betav,SEV=NULL,edf=NULL,tS=NULL)
}
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