Sample from the multivariate normal via mvtnorm — morie_mvnorm

Thin extender over mvtnorm::rmvnorm that draws $n$ observations from the multivariate normal distribution with a given mean vector and covariance matrix.

Usage

morie_mvnorm_sample(n, mean = rep(0, ncol(sigma)), sigma, ...)

Arguments

n: Integer; the number of multivariate observations to draw.
mean: Numeric vector of length ncol(sigma) giving the mean (defaults to a zero vector).
sigma: Numeric positive-(semi)definite covariance matrix.
...: Further arguments forwarded to mvtnorm::rmvnorm (e.g. method, pre0.9_9994, checkSymmetry).

Value

A list with $method = "mvtnorm::rmvnorm" and $raw (a numeric matrix of dimension $n \times \mathrm{ncol}(\Sigma)$).

Examples

# \donttest{
  if (requireNamespace("mvtnorm", quietly = TRUE)) {
    set.seed(1)
    S <- matrix(c(1, 0.4, 0.4, 1), 2, 2)
    morie_mvnorm_sample(100, mean = c(0, 0), sigma = S)
  }
#> $method
#> [1] "mvtnorm::rmvnorm"
#> 
#> $raw
#>               [,1]        [,2]
#>   [1,] -0.57571949  0.05177905
#>   [2,] -0.49207087  1.39090346
#>   [3,]  0.15492763 -0.73584007
#>   [4,]  0.62799423  0.82233717
#>   [5,]  0.50124221 -0.18130894
#>   [6,]  1.55954086  0.69049148
#>   [7,] -1.06062087 -2.29490914
#>   [8,]  1.09202161  0.18584844
#>   [9,]  0.17698609  0.92061940
#>  [10,]  0.92523838  0.74915716
#>  [11,]  1.05939074  0.95339410
#>  [12,] -0.33345160 -1.93215459
#>  [13,]  0.59528369  0.07169160
#>  [14,] -0.45299810 -1.47155932
#>  [15,] -0.38267462  0.31143496
#>  [16,]  1.30901947  0.17697178
#>  [17,]  0.36850130  0.02653495
#>  [18,] -1.43279968 -0.68758735
#>  [19,] -0.39809122 -0.13861950
#>  [20,]  1.23274595  0.97182335
#>  [21,] -0.21281742 -0.28163111
#>  [22,]  0.79599351  0.68731746
#>  [23,] -0.81877538 -0.83329091
#>  [24,]  0.51391027  0.82680934
#>  [25,]  0.07004239  0.83956850
#>  [26,]  0.26466547 -0.51777963
#>  [27,]  0.10318445 -1.03584662
#>  [28,]  1.80741075  2.23140652
#>  [29,] -0.57280220 -1.09713687
#>  [30,]  0.53010918 -0.01580659
#>  [31,]  2.34294168  0.45226138
#>  [32,]  0.68091133  0.16833189
#>  [33,] -0.68902275  0.03295209
#>  [34,] -1.46745840  1.06587045
#>  [35,]  0.59390618  2.15809441
#>  [36,]  0.32043038 -0.59781986
#>  [37,]  0.40699873 -0.78961678
#>  [38,] -1.16764437  0.02916917
#>  [39,] -0.43371538 -0.08948677
#>  [40,] -0.04767162 -0.56189713
#>  [41,] -0.58429171 -0.24851171
#>  [42,]  0.84195741 -1.25073462
#>  [43,]  0.64944264  0.44727615
#>  [44,]  0.97852743 -0.08056601
#>  [45,]  0.41678461  0.33706310
#>  [46,] -0.28429724  1.07154735
#>  [47,]  1.27898585  0.92252501
#>  [48,]  1.66746545  0.87091127
#>  [49,] -1.36678782 -0.82199323
#>  [50,] -1.29550133 -0.71361504
#>  [51,] -0.59867618 -0.08551940
#>  [52,] -0.85942015 -0.03141471
#>  [53,] -0.27970321  1.59627071
#>  [54,]  0.88754685  1.03740555
#>  [55,]  0.71976627  1.72518556
#>  [56,] -0.71664490 -0.58179403
#>  [57,]  1.26912659 -0.34434169
#>  [58,] -0.28326077 -0.42689205
#>  [59,] -0.37026858 -0.33860340
#>  [60,]  0.44753375 -0.07262249
#>  [61,] -0.22088917  1.21133720
#>  [62,] -0.24673827 -0.21960966
#>  [63,]  0.04752725  0.67716361
#>  [64,] -0.07970168 -0.05187025
#>  [65,] -0.73353329 -0.45670003
#>  [66,] -0.06142539 -0.56418117
#>  [67,]  0.21006242 -1.37777575
#>  [68,] -0.01382009 -1.44140770
#>  [69,] -0.40256011 -0.57862886
#>  [70,] -0.64996426 -0.18892587
#>  [71,] -1.63359121  0.76064274
#>  [72,] -1.72455576 -0.79392276
#>  [73,] -1.24578080 -0.96297470
#>  [74,]  2.04669460  0.44345694
#>  [75,] -1.59435924 -1.86880272
#>  [76,]  0.43689904  0.07380923
#>  [77,] -0.50123679 -0.97474291
#>  [78,] -1.67575644 -1.35641501
#>  [79,]  0.85200373 -0.40384636
#>  [80,] -0.97331008  1.54700864
#>  [81,]  0.36737554 -0.14676105
#>  [82,]  1.21726043  1.08398302
#>  [83,] -0.15545292  2.03305030
#>  [84,] -0.54068556 -1.44655129
#>  [85,] -0.09895160  0.17365836
#>  [86,]  2.28091107  0.57511285
#>  [87,]  0.43159593  0.01784377
#>  [88,] -0.33405040 -0.10223312
#>  [89,]  1.19501795  2.19239281
#>  [90,]  1.25250828  1.39233534
#>  [91,] -1.00433092  0.71157034
#>  [92,] -0.08448755 -1.39136756
#>  [93,]  0.47759736 -0.04895591
#>  [94,]  1.27717583 -0.45069319
#>  [95,] -0.61035010 -0.99447084
#>  [96,] -0.09123330  0.35734782
#>  [97,] -0.54665966  0.66335433
#>  [98,] -1.39671319 -1.27270152
#>  [99,]  1.20321094 -0.69997708
#> [100,]  0.32542713 -0.28886735
#> 
# }