# R/est_saeRB.R In msaeRB: Ratio Benchmarking for Multivariate Small Area Estimation

#' @title EBLUPs Ratio Benchmarking based on a Univariate Fay-Herriot (Model 1)
#'
#' @description This function gives EBLUPs ratio benchmarking based on univariate Fay-Herriot (model 1)
#'
#' @param formula an object of class list of formula describe the fitted model
#' @param vardir vector containing sampling variances of direct estimators
#' @param weight vector containing proportion of units in small areas
#' @param samevar logical. If \code{TRUE}, the varians is same. Default is \code{FALSE}
#' @param MAXITER maximum number of iterations for Fisher-scoring. Default is 100
#' @param PRECISION coverage tolerance limit for the Fisher Scoring algorithm. Default value is \code{1e-4}
#' @param data dataframe containing the variables named in formula, vardir, and weight
#'
#' @return This function returns a list with following objects:
#' \item{eblup}{a list containing a value of estimators}
#' \itemize{
#'   \item est.eblup : a dataframe containing EBLUP estimators
#'   \item est.eblupRB : a dataframe containing ratio benchmark estimators
#' }
#'
#' \item{fit}{a list contining following objects:}
#' \itemize{
#'   \item method : fitting method, named "REML"
#'   \item convergence : logical value of convergence of Fisher Scoring
#'   \item iterations : number of iterations of Fisher Scoring algorithm
#'   \item estcoef : a data frame containing estimated model coefficients (\code{beta, std. error, t value, p-value})
#'   \item refvar : estimated random effect variance
#' }
#' \item{random.effect}{a data frame containing values of random effect estimators}
#' \item{agregation}{a data frame containing agregation of direct, EBLUP, and ratio benchmark estimation}
#' @export est_saeRB
#'
#' @import abind
#' @importFrom magic adiag
#' @importFrom Matrix forceSymmetric
#' @importFrom stats model.frame na.omit model.matrix median pnorm rnorm
#' @importFrom MASS mvrnorm
#'
#' @examples
#' ## load dataset
#' data(datamsaeRB)
#'
#' # Compute EBLUP and Ratio Benchmark using auxiliary variables X1 and X2 for each dependent variable
#'
#' ## Using parameter 'data'
#' est_sae = est_saeRB(Y1 ~ X1 + X2, v1, w1, data = datamsaeRB)
#'
#' ## Without parameter 'data'
#' est_sae = est_saeRB(datamsaeRB$Y1 ~ datamsaeRB$X1 + datamsaeRB$X2, datamsaeRB$v1, datamsaeRB$w1) #' #' ## Return #' est_sae$eblup$est.eblupRB # to see the Ratio Benchmark estimators #' est_saeRB = function (formula, vardir, weight, samevar = FALSE, MAXITER = 100, PRECISION = 1E-04, data) { if (!is.list(formula)) formula = list(formula) r = length(formula) if (r > 1) stop("You should using est_msaeRB() for multivariate") R_function = function (vardir, n, r) { if (r == 1) { R = diag(vardir) } else { R = matrix(rep(0, times = n*r*n*r), nrow = n*r, ncol = n*r) k = 1 for (i in 1:r) { for (j in 1:r) { if (i <= j) { mat0 = matrix(rep(0, times = r*r), nrow = r, ncol = r) mat0[i, j] = 1 matVr = diag(vardir[, k], length(vardir[, k])) R_hasil = kronecker(mat0, matVr) R = R + R_hasil k = k + 1 } } } R = forceSymmetric(R) } return(as.matrix(R)) } namevar = deparse(substitute(vardir)) nameweight = deparse(substitute(weight)) if (!missing(data)) { formuladata = lapply(formula, function(x) model.frame(x, na.action = na.omit, data)) y = unlist(lapply(formula, function(x) model.frame(x, na.action = na.omit, data)[[1]])) X = Reduce(adiag, lapply(formula, function(x) model.matrix(x, data))) W = as.matrix(data[, nameweight]) n = length(y)/r if (any(is.na(data[, namevar]))) stop("Object vardir contains NA values.") if (any(is.na(data[, nameweight]))) stop("Object weight contains NA values.") R = R_function(data[, namevar], n, r) vardir = data[, namevar] } else { formuladata = lapply(formula, function(x) model.frame(x, na.action = na.omit)) y = unlist(lapply(formula, function(x) model.frame(x, na.action = na.omit)[[1]])) X = Reduce(adiag, lapply(formula, function(x) model.matrix(x))) W = as.matrix(weight) n = length(y)/r if (any(is.na(vardir))) stop("Object vardir contains NA values") if (any(is.na(weight))) stop("Object weight contains NA values.") R = R_function(vardir, n, r) } y_names = sapply(formula, "[[", 2) Ir = diag(r) In = diag(n) dV = list() dV1 = list() for (i in 1:r){ dV[[i]] = matrix(0, nrow = r, ncol = r) dV[[i]][i, i] = 1 dV1[[i]] = kronecker(dV[[i]], In) } convergence = TRUE if (samevar) { Vu = median(diag(R)) k = 0 diff = rep(PRECISION + 1, r) while (any(diff > PRECISION) & (k < MAXITER)) { k = k + 1 Vu1 = Vu Gr = kronecker(Vu1, Ir) Gn = kronecker(Gr, In) V = as.matrix(Gn + R) Vinv = solve(V) XtVinv = t(Vinv %*% X) Q = solve(XtVinv %*% X) P = Vinv - t(XtVinv) %*% Q %*% XtVinv Py = P %*% y s = (-0.5) %*% sum(diag(P)) + 0.5 %*% (t(Py) %*% Py) iF = 0.5 %*% sum(diag(P %*% P)) Vu = Vu1 + solve(iF) %*% s diff = abs((Vu - Vu1)/Vu1) } Vu = as.vector((rep(max(Vu, 0), r))) names(Vu) = y_names if (k >= MAXITER && diff >= PRECISION) { convergence = FALSE } Gn = kronecker(diag(Vu), In) V = as.matrix(Gn + R) Vinv = solve(V) XtVinv = t(Vinv %*% X) Q = solve(XtVinv %*% X) P = Vinv - t(XtVinv) %*% Q %*% XtVinv Py = P %*% y beta = Q %*% XtVinv %*% y res = y - X %*% beta eblup = data.frame(matrix(X %*% beta + Gn %*% Vinv %*% res, n, r)) names(eblup) = y_names se.b = sqrt(diag(Q)) t.value = beta/se.b p.value = 2 * pnorm(abs(as.numeric(t.value)), lower.tail = FALSE) coef = as.matrix(cbind(beta, se.b, t.value, p.value)) colnames(coef) = c("beta", "std. error", "t value", "p-value") rownames(coef) = colnames(X) coef = as.data.frame(coef) } else { Vu = apply(matrix(diag(R), nrow = n, ncol = r), 2, median) k = 0 diff = rep(PRECISION + 1, r) while (any(diff > rep(PRECISION, r)) & (k < MAXITER)) { k = k + 1 Vu1 = Vu if (r == 1) { Gr = Vu1 } else { Gr = diag(as.vector(Vu1)) } Gn = kronecker(Gr, In) V = as.matrix(Gn + R) Vinv = solve(V) XtVinv = t(Vinv %*% X) Q = solve(XtVinv %*% X) P = Vinv - t(XtVinv) %*% Q %*% XtVinv Py = P %*% y s = sapply(dV1, function(x) (-0.5) * sum(diag(P %*% x)) + 0.5 * (t(Py) %*% x %*% Py)) iF = matrix(unlist(lapply(dV1, function(x) lapply(dV1, function(y) 0.5 * sum(diag(P %*% x %*% P %*% y))))), r) Vu = Vu1 + solve(iF) %*% s diff = abs((Vu - Vu1)/Vu1) } Vu = as.vector(sapply(Vu, max, 0)) if (k >= MAXITER && diff >= PRECISION){ convergence = FALSE } if (r == 1) { Gr = Vu1 } else { Gr = diag(as.vector(Vu1)) } Gn = kronecker(Gr, In) V = as.matrix(Gn + R) Vinv = solve(V) XtVinv = t(Vinv %*% X) Q = solve(XtVinv %*% X) P = Vinv - t(XtVinv) %*% Q %*% XtVinv Py = P %*% y beta = Q %*% XtVinv %*% y res = y - X %*% beta eblup = data.frame(matrix(X %*% beta + Gn %*% Vinv %*% res, n, r)) names(eblup) = y_names se.b = sqrt(diag(Q)) t.value = beta/se.b p.value = 2 * pnorm(abs(as.numeric(t.value)), lower.tail = FALSE) coef = as.matrix(cbind(beta, se.b, t.value, p.value)) colnames(coef) = c("beta", "std. error", "t value", "p-value") rownames(coef) = colnames(X) coef = as.data.frame(coef) } random.effect = data.frame(matrix(Gn %*% Vinv %*% res, n, r)) names(random.effect) = y_names y.mat = matrix(y, nrow = n, ncol = r) eblup.mat = as.matrix(eblup) eblup.ratio = eblup.mat %*% (colSums(W * y.mat)/colSums(W * eblup.mat)) eblup.ratio = as.data.frame(eblup.ratio) names(eblup.ratio) = y_names agregation.direct = diag(t(W) %*% y.mat) agregation.eblup = diag(t(W) %*% eblup.mat) agregation.eblup.ratio = diag(t(W) %*% as.matrix(eblup.ratio)) agregation = as.matrix(rbind(agregation.direct, agregation.eblup, agregation.eblup.ratio)) colnames(agregation) = y_names agregation = as.data.frame(agregation) result = list(eblup = list(est.eblup = NA, est.eblupRB = NA), fit = list(method = NA, convergence = NA, iteration = NA, estcoef = NA, refvar = NA), random.effect = NA, agregation = NA) result$eblup$est.eblup = eblup result$eblup$est.eblupRB = eblup.ratio result$fit$method = "REML" result$fit$convergence = convergence result$fit$iteration = k result$fit$estcoef = coef result$fit$refvar = t(Vu) result$random.effect = random.effect
result\$agregation = agregation
return(result)
}


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msaeRB documentation built on June 13, 2021, 1:06 a.m.