Nothing
###Function for summarizing the raw MCMC samples-------------------------------------------------------------------
FormatSamples <- function(DatObj, RawSamples) {
###Set data objects
Nu <- DatObj$Nu
###Format raw samples
Mu <- t(RawSamples[1 : Nu, ])
Tau2 <- t(RawSamples[(1 : Nu) + Nu, ])
Alpha <- t(RawSamples[(1 : Nu) + 2 * Nu, ])
Delta <- t(RawSamples[(3 * Nu + 1) : (3 * Nu + 3), ])
T <- t(RawSamples[(3 * Nu + 4) : (3 * Nu + 9), ])
Phi <- t(RawSamples[(3 * Nu + 10), ,drop = FALSE])
colnames(Mu) <- paste0("mu", 1 : Nu)
colnames(Tau2) <- paste0("tau2", 1 : Nu)
colnames(Alpha) <- paste0("alpha", 1 : Nu)
colnames(Delta) <- paste0("delta", 1 : 3)
colnames(T) <- c("t11", "t21", "t22", "t31", "t32", "t33")
colnames(Phi) <- "phi"
Out <- list(Mu = Mu, Tau2 = Tau2, Alpha = Alpha, Delta = Delta, T = T, Phi = Phi)
return(Out)
}
###Function for creating a data object that contains objects needed for ModelFit-----------------------------------
OutputDatObj <- function(DatObj) {
###Collect needed objects
DatObjOut <- list(M = DatObj$M,
Nu = DatObj$Nu,
Z = DatObj$Z,
AdjacentEdgesBoolean = DatObj$AdjacentEdgesBoolean,
W = DatObj$W,
EyeM = DatObj$EyeM,
OneM = DatObj$OneM,
OneNu = DatObj$OneNu,
YStarWide = DatObj$YStarWide,
Rho = DatObj$Rho,
FamilyInd = DatObj$FamilyInd,
ScaleY = DatObj$ScaleY,
YObserved = DatObj$YObserved,
ScaleDM = DatObj$ScaleDM,
TempCorInd = DatObj$TempCorInd,
WeightsInd = DatObj$WeightsInd,
Time = DatObj$Time)
return(DatObjOut)
}
###Function for creating a data augmentation object that contains objects needed for ModelFit----------------------
OutputDatAug <- function(DatAug) {
###Collect needed objects
DatAugOut <- list(NBelow = DatAug$NBelow,
NBelowCount = DatAug$NBelowCount,
TobitIndeces = DatAug$TobitIndeces,
YStarNonZero = DatAug$YStarNonZero)
return(DatAugOut)
}
###Function for summarizing Metropolis objects post sampler--------------------------------------------------------
SummarizeMetropolis <- function(DatObj, MetrObj, McmcObj) {
###Set data object
Nu <- DatObj$Nu
###Set MCMC object
NSims <- McmcObj$NSims
###Set Metropolis objects
MetropTheta2 <- MetrObj$MetropTheta2
AcceptanceTheta2 <- MetrObj$AcceptanceTheta2
MetropTheta3 <- MetrObj$MetropTheta3
AcceptanceTheta3 <- MetrObj$AcceptanceTheta3
MetropPhi <- MetrObj$MetropPhi
AcceptancePhi <- MetrObj$AcceptancePhi
###Summarize and output
TuningParameters <- c(MetropTheta2, MetropTheta3, MetropPhi)
AcceptanceCount <- c(AcceptanceTheta2, AcceptanceTheta3, AcceptancePhi)
AcceptancePcts <- AcceptanceCount / NSims
MetrSummary <- cbind(AcceptancePcts, TuningParameters)
rownames(MetrSummary) <- c(paste0("Theta2", 1 : Nu), paste0("Theta3", 1 : Nu), "Phi")
colnames(MetrSummary) <- c("acceptance", "tuner")
return(MetrSummary)
}
###Verify the class of our regression object------------------------------------------------------------------------
#' is.STBDwDM
#'
#' \code{is.STBDwDM} is a general test of an object being interpretable as a
#' \code{\link{STBDwDM}} object.
#'
#' @param x object to be tested.
#'
#' @details The \code{\link{STBDwDM}} class is defined as the regression object that
#' results from the \code{\link{STBDwDM}} regression function.
#'
#' @export
is.STBDwDM <- function(x) {
identical(attributes(x)$class, "STBDwDM")
}
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