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#' CV Score Calculator with Fixed Distance Bandwidth with Parallel Process
#'
#' @description Get CV score with a fixed distance bandwidth
#'
#' @param bw Current potential bandwidth put into the calculation.
#' @param data The data.frame has been washed
#' @param ID_list The data.frame with individuals' ID
#' @param formula The regression formula: : Y ~ X1 + ... + Xk
#' @param p The power of the Minkowski distance, default is 2, i.e. the Euclidean distance (see GWmodel::bw.gwr)
#' @param longlat If TRUE, great circle distances will be calculated (see GWmodel::bw.gwr)
#' @param adaptive If TRUE, adaptive distance bandwidth is used, otherwise, fixed distance bandwidth.
#' @param kernel Kernel, default "bisquare". gaussian,exponential, bisquare, tricube, boxcar (see GWmodel::gw.weight)
#' @param model Panel models transformation : (c("within", "random", "pooling"))
#' @param index The index C("id", "time"), here "id" is always "id", but "time" is set by user
#' @param effect The effects introduced in the model, one of "individual", "time", "twoways", or "nested"
#' @param random.method Method of estimation for the variance components in the random effects model, one of "swar" (default), "amemiya", "walhus", or "nerlove"
#' @param cluster.number Cluster number used in calculation
#'
#' @import dplyr
#' @import GWmodel
#' @import parallel
#' @import foreach
#' @import iterators
#' @import doParallel
#' @importFrom plm plm pdata.frame
#'
#' @return A CV score
#'
#' @references Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003.
#' @noRd
CV_F_para <- function(bw, data, ID_list, formula, p, longlat, adaptive, kernel,
model = model, index = index, effect = effect,
random.method = random.method, cluster.number = cluster.number)
{
ID_list_single <- as.vector(ID_list[[1]])
wgt <- 0
ID_individual <- 0
varibale_name_in_equation <- all.vars(formula)
cl <- parallel::makeCluster(cluster.number)
doParallel::registerDoParallel(cl)
# v0.1.1 the loss function is based on local r2
# CVscore_vector <- foreach(ID_individual = ID_list_single, .combine = c) %dopar%
# v0.1.2
residualsVector <- foreach(ID_individual = ID_list_single, .combine = c) %dopar%
{
data$aim[data$id == ID_individual] <- 1
data$aim[data$id != ID_individual] <- 0
subsample <- data
#v0.1.2
numberOfAim <- nrow(subsample[subsample$aim == 1,])
subsample <- subsample[order(-subsample$aim),]
dp_locat_subsample <- dplyr::select(subsample, 'X', 'Y')
dp_locat_subsample <- as.matrix(dp_locat_subsample)
dMat <- GWmodel::gw.dist(dp.locat = dp_locat_subsample, rp.locat = dp_locat_subsample,
focus = 1, p=p, longlat=longlat)
weight <- GWmodel::gw.weight(as.numeric(dMat), bw=bw, kernel=kernel, adaptive=adaptive)
subsample$wgt <- as.vector(weight)
subsample <- subsample[(subsample$wgt > 0.01),]
Psubsample <- plm::pdata.frame(subsample, index = index, drop.index = FALSE, row.names = FALSE,
stringsAsFactors = FALSE)
plm_subsample <- try(plm::plm(formula=formula, model=model, data=Psubsample,
effect = effect, index=index, weights = wgt,
random.method = random.method), silent = TRUE)
# v0.1.1
# if(!inherits(plm_subsample, "try-error"))
# {
# CVscore <- nrow(subsample) * sum(plm_subsample$residuals^2) /
# (nrow(subsample) - length(varibale_name_in_equation) + 1)^2
# }
# else
# {
# CVscore <- Inf
# }
#0.1.2
if(!inherits(plm_subsample, "try-error"))
{
residualsLocalAim <- plm_subsample$residuals[1:numberOfAim]
}
else
{
residualsLocalAim <- Inf
}
}
parallel::stopCluster(cl)
# v0.1.1 the loss function is based on local r2
# mean_CVscore <- mean(CVscore_vector)
# cat("Fixed Bandwidth:", bw, "CV score:", mean_CVscore, "\n")
# return(mean_CVscore)
#v0.1.2
CVscore <- nrow(data) * sum(residualsVector^2) /
(nrow(data) - length(varibale_name_in_equation) + 1)^2
cat("Fixed Bandwidth:", bw, "CV score:", CVscore, "\n")
return(CVscore)
}
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