Integrate over Multivariate Student-t Random Effects
Source:R/RcppExports.R, R/rimplementation.R
integratemvt.RdUsed in the process of Monte Carlo integration over multivariate Student-t random effects. This generates the random draws from the multivariate Student-t distribution and multiplies these by the data. Not intended to be called directly by most users.
Arguments
- X
A numeric matrix of the data to be multiplied by the random effects
- k
An integer, the number of random samples to be used for numerical integration
- sd
A numeric vector of the standard deviations
- chol
A numeric matrix, which should be the Cholesky decomposition of the correlation matrix of the multivariate Student-t distribution.
- df
A numeric scalar giving the degrees of freedom of the (multivariate) Student-t distribution.
Examples
integratemvt(
X = matrix(1, 1, 2),
k = 100L,
sd = c(10, 5),
chol = chol(matrix(c(1, .5, .5, 1), 2)),
df = 5)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 5.20215 -0.2844267 5.694854 -7.281875 -7.924065 25.69829 -6.684368
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] -11.46587 -22.20752 0.6171358 29.99234 -7.524701 27.59598 0.1526352
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22]
#> [1,] -7.245521 -14.46326 -7.99154 5.138873 2.250636 4.658883 10.79124 27.27985
#> [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30]
#> [1,] 20.3724 11.65742 14.79061 3.499234 3.936819 2.520605 -4.299193 -11.0188
#> [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,] 4.16747 -26.88653 18.89355 -1.448951 10.16708 -1.050448 -20.41367 12.91734
#> [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46]
#> [1,] -0.3328938 9.08639 9.618768 2.100537 9.32654 22.06658 22.55632 8.807334
#> [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54]
#> [1,] -4.412645 -1.38984 -4.021542 12.83026 -11.39327 1.427414 11.33248 12.97473
#> [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
#> [1,] 16.26732 -8.42815 9.81665 -11.81352 2.487766 -1.021224 19.93597 16.41659
#> [,63] [,64] [,65] [,66] [,67] [,68] [,69]
#> [1,] 17.16932 -2.953553 -20.08644 -27.13626 1.858663 -18.48578 -21.5683
#> [,70] [,71] [,72] [,73] [,74] [,75] [,76]
#> [1,] 4.788572 -1.087213 -12.00113 8.570569 -43.63469 -35.30215 -2.572512
#> [,77] [,78] [,79] [,80] [,81] [,82] [,83]
#> [1,] -5.559753 5.849954 -22.95129 1.414372 -5.147753 35.54415 -0.949487
#> [,84] [,85] [,86] [,87] [,88] [,89] [,90]
#> [1,] -10.83853 -5.816571 -0.6116516 -3.302185 7.931949 -9.924816 31.94516
#> [,91] [,92] [,93] [,94] [,95] [,96] [,97]
#> [1,] 0.7843987 -1.794488 -3.980526 28.49642 -6.86911 -0.8426098 -23.93309
#> [,98] [,99] [,100]
#> [1,] 2.114413 15.91904 7.158364
integratemvt(matrix(1, 1, 1), 100L, c(5), matrix(1), df = 5)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] -0.6884898 -2.945806 0.3517629 -5.538192 6.85461 -1.950971 -1.000213
#> [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
#> [1,] -7.41137 -4.360758 0.182349 10.34605 2.531249 -3.76624 9.940948 -2.648756
#> [,16] [,17] [,18] [,19] [,20] [,21] [,22]
#> [1,] 4.514654 -0.5612279 -8.124151 -18.70818 4.170286 0.6743294 -4.129927
#> [,23] [,24] [,25] [,26] [,27] [,28] [,29]
#> [1,] 0.7161723 -1.741341 -1.086183 0.8474924 -3.7406 -3.775788 -2.083167
#> [,30] [,31] [,32] [,33] [,34] [,35] [,36]
#> [1,] 0.4251361 1.678449 -19.95289 0.8190724 -0.4320987 -6.398619 -3.04557
#> [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44]
#> [1,] 1.726898 3.465388 4.029738 2.50179 1.336653 -5.926968 -4.081622 -5.041026
#> [,45] [,46] [,47] [,48] [,49] [,50] [,51] [,52]
#> [1,] 1.100334 -6.446528 6.9573 -2.730911 2.361198 5.361252 17.49685 -8.006062
#> [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
#> [1,] 0.7473566 2.898586 1.993 11.36144 -8.699974 1.644445 4.93137 -2.52947
#> [,61] [,62] [,63] [,64] [,65] [,66] [,67]
#> [1,] -0.4244829 -14.16869 2.436079 3.534633 3.069667 1.51326 -1.135709
#> [,68] [,69] [,70] [,71] [,72] [,73] [,74]
#> [1,] -1.203338 -1.772124 -9.871474 3.880475 -1.248791 0.8676228 8.864636
#> [,75] [,76] [,77] [,78] [,79] [,80] [,81]
#> [1,] 0.2149986 -4.0355 3.331739 -5.257585 -6.209077 8.739488 -6.856569
#> [,82] [,83] [,84] [,85] [,86] [,87] [,88]
#> [1,] -3.759035 -9.449988 0.2272446 8.631852 7.772112 3.831445 -2.229927
#> [,89] [,90] [,91] [,92] [,93] [,94] [,95]
#> [1,] -0.3926946 -10.65554 -1.632826 3.50161 -6.566708 -5.83923 -6.842479
#> [,96] [,97] [,98] [,99] [,100]
#> [1,] -1.90805 -5.123172 -8.448042 -6.842286 1.549344