R/00_jb.norm.test.R

In Antoine-Masse/KefiR: KefiR Package

#' Jarque Bera test for normality
#'
#' @param x a numeric vector of data values.
#' @param nrepl the number of replications in Monte Carlo simulation.
#'
#' @return This function is extracted from the deprecated library normtest
#' @return Performs Jarque Bera test for the composite hypothesis of normality, see Jarque and Bera (1987).
#'
#' @examples
#' #x <- rnorm(100)
#' # jb.normtest(x)
jb.normtest <- function(x, nrepl = 2000) {
    DNAME <- deparse(substitute(x))
    l <- 0
    n <- length(x)
    x1 <- sum(x)/n
    b1 <- sqrt(n) * (sum((x - x1)^3))/((sum((x - x1)^2))^(3/2))
    b2 <- n * (sum((x - x1)^4))/((sum((x - x1)^2))^2)
    t <- (n/6) * ((b1)^2 + ((b2 - 3)^2)/4)
    for (i in 1:nrepl) {
        z <- rnorm(n)
        z1 <- sum(z)/n
        a1 <- sqrt(n) * (sum((z - z1)^3))/((sum((z - z1)^2))^(3/2))
        a2 <- n * (sum((z - z1)^4))/((sum((z - z1)^2))^2)
        T <- (n/6) * ((a1)^2 + ((a2 - 3)^2)/4)
        if (T > t) 
            l = l + 1
    }
    p.value <- l/nrepl
    RVAL <- list(statistic = c(JB = t), p.value = p.value, method = "Jarque-Bera test for normality", 
        data.name = DNAME)
    class(RVAL) <- "htest"
    return(RVAL)
	}