R/normalTest.R

Defines functions normalTest

## This modifies the approach in (R)DieHarder which does
##   take N draws from a U(0,1)
##   repeat M times
##   and for large enough N, then the sum of all N draws goes to
##       mean   --> N/2
##       stddev --> sqrt(N/12)
##   which is known as the Irwin-Hall distribution
##   then for each of these M values use the inverse of normal to obtain a p-value
##   that p value should be uniformly distributed across these M draws
##   so use Kuiper's K/S test variant to test for uniform U(0,1)
##
## Here we don't need Irwin-Hall: the sum of N vars drawn as N(0,1) will be N(0,sqrt(N))
## So we compute a p value from that and assemple M such p values

normalTest <- function(N=1e5,      	# individual draws
                       M=1e2,  		# repeats
                       seed=123456789,
                       generators=c("Ziggurat", "MT", "LZLLV",
                                     "GSL", "QL", "Gretl"),
                       showplot=interactive()) {

    res <- mclapply(generators, FUN=function(g, seed) {
        res <- ziggsum(M, N, g, seed)
        v <- pnorm(res, sd=sqrt(N))
    }, seed, mc.cores=getOption("mc.cores", 2L))

    names(res) <- generators
    res <- as.data.frame(res)

    attr(res, "testtype")  <- "Normal"
    attr(res, "draws")     <- N
    attr(res, "repeats")   <- M
    attr(res, "seed")      <- seed
    attr(res, "created")   <- format(Sys.time())
    attr(res, "version")   <- packageVersion("RcppZiggurat")

    if (showplot) {
        plotAll(res)
    }

    invisible(res)
}

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RcppZiggurat documentation built on Sept. 28, 2017, 1:02 a.m.