Computes random vectors of the multivariate symmetric logistic distribution for arbitrary correlation matrices using the asymptotic Kolmogorov distribution – see references.
Usage
rmvlogis(n, Q = NULL, delta = rep(0, d), BIG = 500)References
Scale Mixtures of Normal Distributions Author(s): D. F. Andrews and C. L. Mallows Source: Journal of the Royal Statistical Society. Series B (Methodological), Vol. 36, No. 1 (1974), pp. 99-102 Published by: Wiley for the Royal Statistical Society Stable URL: http://www.jstor.org/stable/2984774
Examples
rmvlogis(10, Q=diag(5))
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1.1770287 1.5226579 0.6974276 0.8467555 0.09429795
#> [2,] -0.1636597 2.4184144 -3.2223124 -1.5816272 -3.98359028
#> [3,] 2.2395249 -4.2123205 -2.3444725 -0.7518423 -0.25102021
#> [4,] -1.8916177 2.9053885 1.4103430 -1.0662243 0.75352411
#> [5,] 0.4784924 -0.3034091 -0.0250878 0.8599325 -0.10400652
#> [6,] 2.1733984 1.0380266 -0.2616401 0.4669456 -0.69642319
#> [7,] -0.8854321 0.8550146 -3.0059695 -0.1536785 -1.60043221
#> [8,] 2.2554228 -0.9378274 -2.1335241 -3.3860767 1.12269757
#> [9,] 1.0174335 -1.2826962 0.2050895 1.1857028 0.16163594
#> [10,] 2.6151071 0.7222037 1.2803756 0.6625827 -0.59042839
if (FALSE) { # \dontrun{
QMAT <- matrix(c(1,0,0,1),nrow=2)
draw_NNMD <- NonNorMvtDist::rmvlogis(2e3, parm1=rep(0,2), parm2=rep(1,2))
draw_mvpd <- mvpd::rmvlogis(2e3, Q=QMAT)
mean(draw_NNMD[,1] < -1 & draw_NNMD[,2] < 3)
mean(draw_mvpd[,1] < -1 & draw_mvpd[,2] < 3)
plogis(-1)
mean(draw_NNMD[,1] < -1)
mean(draw_mvpd[,1] < -1)
plogis(3)
mean(draw_NNMD[,2] < 3)
mean(draw_mvpd[,2] < 3)
rangex <- range(c(draw_mvpd[,1],draw_NNMD[,1]))
rangey <- range(c(draw_mvpd[,2],draw_NNMD[,2]))
par(mfrow=c(3,2), pty="s", mai=c(.5,.1,.1,.1))
plot(draw_NNMD, xlim=rangex, ylim=rangey); abline(h=0,v=0)
plot(draw_mvpd , xlim=rangex, ylim=rangey); abline(h=0,v=0)
hist(draw_NNMD[,1] , breaks = 100, xlim=rangex, probability=TRUE, ylim=c(0,.40))
curve(dlogis(x), add=TRUE, col="blue",lwd=2)
hist(draw_mvpd[,1], breaks = 100, xlim=rangex, probability=TRUE, ylim=c(0,.40))
curve(dlogis(x), add=TRUE, col="blue",lwd=2)
hist(draw_NNMD[,2] , breaks = 100, xlim=rangex, probability=TRUE, ylim=c(0,.40))
curve(dlogis(x), add=TRUE, col="blue",lwd=2)
hist(draw_mvpd[,2], breaks = 100, xlim=rangex, probability=TRUE, ylim=c(0,.40))
curve(dlogis(x), add=TRUE, col="blue",lwd=2)
} # }