#' @import TeachingSampling
#' @export
#'
#' @title
#' Statistical errors for the estimation of a difference of means
#' @description
#' This function computes the cofficient of variation and the standard error when estimating a difference of means under a complex sample design.
#' @return
#' The coefficient of variation and the margin of error for a predefined sample size.
#' @details
#' We note that the coefficent of variation is defined as: \deqn{cve = \frac{\sqrt{Var(\bar{y}_1 - \bar{y}_2)}}{\bar{y}_1 - \bar{y}_2}}
#' Also, note that the magin of error is defined as: \deqn{\varepsilon = z_{1-\frac{\alpha}{2}}\sqrt{Var(\bar{y}_1 - \bar{y}_2)}}
#'
#' @author Hugo Andres Gutierrez Rojas <hagutierrezro at gmail.com>
#' @param N The population size.
#' @param n The sample size.
#' @param mu1 The value of the estimated mean of the variable of interes for the first population.
#' @param mu2 The value of the estimated mean of the variable of interes for the second population.
#' @param sigma1 The value of the estimated variance of the variable of interes for the first population.
#' @param sigma2 The value of the estimated mean of a variable of interes for the second population.
#' @param DEFF The design effect of the sample design. By default \code{DEFF = 1}, which corresponds to a simple random sampling design.
#' @param conf The statistical confidence. By default \code{conf = 0.95}.
#' @param plot Optionally plot the errors (cve and margin of error) against the sample size.
#'
#' @references
#' Gutierrez, H. A. (2009), \emph{Estrategias de muestreo: Diseno de encuestas y estimacion de parametros}. Editorial Universidad Santo Tomas
#' @seealso \code{\link{ss4p}}
#' @examples
#' e4dm(N=10000, n=400, mu1 = 100, mu2 = 12, sigma1 = 10, sigma2=8)
#' e4dm(N=10000, n=400, mu1 = 100, mu2 = 12, sigma1 = 10, sigma2=8, plot=TRUE)
#' e4dm(N=10000, n=400, mu1 = 100, mu2 = 12, sigma1 = 10, sigma2=8, DEFF=3.45, conf=0.99, plot=TRUE)
e4dm <- function(N, n, mu1, mu2, sigma1, sigma2, DEFF = 1, conf = 0.95, plot = FALSE) {
S2 <- DEFF * (sigma1^2 + sigma2^2)
Z <- 1 - ((1 - conf)/2)
f <- n/N
VAR <- (1/n) * (1 - f) * S2
CVE <- 100 * sqrt(VAR)/abs(mu1 - mu2)
ME <- qnorm(Z) * sqrt(VAR)
if (plot == TRUE) {
nseq <- seq(1, N, 10)
cveseq <- rep(NA, length(nseq))
meseq <- rep(NA, length(nseq))
for (k in 1:length(nseq)) {
fseq <- nseq[k]/N
varseq <- (1/nseq[k]) * (1 - fseq) * S2
cveseq[k] <- 100 * sqrt(varseq)/abs(mu1 - mu2)
meseq[k] <- qnorm(Z) * sqrt(varseq)
}
par(mfrow = c(1, 2))
plot(nseq, cveseq, type = "l", lty = 1, pch = 1, col = 3, ylab = "Coefficient of variation (%)", xlab = "Sample Size")
points(n, CVE, pch = 8, bg = "blue")
abline(h = CVE, lty = 3)
abline(v = n, lty = 3)
plot(nseq, meseq, type = "l", lty = 1, pch = 1, col = 3, ylab = "Margin of error", xlab = "Sample Size")
points(n, ME, pch = 8, bg = "blue")
abline(h = ME, lty = 3)
abline(v = n, lty = 3)
}
msg <- cat("With the parameters of this function: N =", N, "n = ", n, "mu1 =", mu1, "mu2 =", mu2,
"sigma1 =", sigma1, "sigma2 =", sigma2, "DEFF = ", DEFF, "conf =", conf, ".
\nThe estimated coefficient of variation is ", CVE, ".
\nThe margin of error is", ME, ". \n \n")
result <- list(cve = CVE, Margin_of_error = ME)
result
}
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