smvrnorm() simulates data from a multivariate normal distribution.
Arguments
- n
the number of observations to simulate
- mu
a vector of means
- sigma
a positive-definite symmetric matrix specifying the covariance matrix of the variables.
- tol
tolerance (relative to largest variance) for numerical lack of positive-definiteness in
sigma.- empirical
logical. If true,
muandsigmaspecify the empirical not population mean and covariance matrix.- eispack
logical. values other than FALSE result in an error
- seed
set an optional seed
Details
This is a simple port and rename of mvrnorm() from the MASS package. I elect
to plagiarize/port it because the MASS package conflicts with a lot of things in my workflow,
especially select(). This is useful for "informal Bayes" approaches to generating quantities
of interest from a regression model.
Examples
M1 <- lm(mpg ~ disp + cyl, mtcars)
smvrnorm(100, coef(M1), vcov(M1))
#> (Intercept) disp cyl
#> [1,] 34.44180 -0.016093590 -1.6668364
#> [2,] 32.03398 -0.018701730 -1.2757296
#> [3,] 37.36271 -0.020331480 -2.0825009
#> [4,] 36.71988 -0.012678111 -2.3042494
#> [5,] 34.98588 -0.030837751 -1.2148439
#> [6,] 35.75183 -0.031857450 -1.3338967
#> [7,] 36.17367 -0.006791696 -2.3472860
#> [8,] 32.91489 -0.026496439 -1.0494384
#> [9,] 36.29545 -0.026003203 -1.5875640
#> [10,] 34.96627 -0.016327081 -1.7916997
#> [11,] 30.70833 -0.034137268 -0.4632021
#> [12,] 35.25495 -0.024852183 -1.5871001
#> [13,] 29.67764 -0.045307263 0.1279449
#> [14,] 33.27457 -0.024805958 -1.2678062
#> [15,] 35.90700 -0.028802646 -1.3244104
#> [16,] 32.70040 -0.028711966 -0.8978397
#> [17,] 35.64675 -0.007246586 -2.2356900
#> [18,] 35.50407 -0.023854934 -1.6782045
#> [19,] 33.96691 -0.030668985 -1.2353990
#> [20,] 37.86235 -0.004940957 -2.7436385
#> [21,] 33.28914 -0.012421275 -1.7446521
#> [22,] 34.30010 -0.028712418 -1.3577551
#> [23,] 36.06222 -0.021116329 -1.8633183
#> [24,] 37.46621 -0.023343183 -1.8936744
#> [25,] 36.07328 -0.021921595 -1.8317841
#> [26,] 36.56082 -0.019457823 -1.8880890
#> [27,] 31.39368 -0.040165534 -0.4286164
#> [28,] 29.33698 -0.032083182 -0.4472746
#> [29,] 34.28392 -0.013035185 -1.8310718
#> [30,] 33.58597 -0.023962357 -1.3969869
#> [31,] 28.97209 -0.030077306 -0.3020914
#> [32,] 34.96147 -0.001392552 -2.3925628
#> [33,] 34.29185 -0.027030451 -1.3731181
#> [34,] 30.42862 -0.020690303 -0.8481803
#> [35,] 36.42266 -0.017422488 -1.9164967
#> [36,] 33.56155 -0.043038754 -0.5721694
#> [37,] 31.87355 -0.019477538 -1.2724780
#> [38,] 36.74445 -0.016871084 -1.9450795
#> [39,] 32.78354 -0.016504146 -1.5442419
#> [40,] 35.39997 -0.019395674 -1.8751268
#> [41,] 35.60957 -0.020148263 -1.6666933
#> [42,] 37.99107 0.007172362 -3.3032306
#> [43,] 40.85089 -0.010726402 -2.9515661
#> [44,] 37.95101 -0.028593076 -1.7685981
#> [45,] 33.67591 -0.022775762 -1.3301353
#> [46,] 37.97592 -0.015515732 -2.4204318
#> [47,] 33.96554 -0.019441511 -1.6081523
#> [48,] 34.18454 -0.019333806 -1.4407924
#> [49,] 34.55656 -0.025640883 -1.3469341
#> [50,] 33.92344 -0.014969830 -1.6643016
#> [51,] 34.34051 -0.020552987 -1.5917340
#> [52,] 34.10286 -0.032555955 -1.0019450
#> [53,] 35.99606 -0.016518191 -1.8187662
#> [54,] 31.72513 -0.029475953 -0.7907692
#> [55,] 32.81959 -0.022554931 -1.2138406
#> [56,] 35.99504 -0.028737140 -1.5838799
#> [57,] 33.62026 -0.012538926 -1.6514403
#> [58,] 37.26592 -0.022992995 -1.8182772
#> [59,] 36.14499 -0.017645647 -1.9345679
#> [60,] 35.23342 -0.004586844 -2.2137780
#> [61,] 31.81256 -0.043409009 -0.2694654
#> [62,] 36.43215 -0.022729756 -1.8196314
#> [63,] 39.28006 -0.004245869 -2.9370557
#> [64,] 34.91233 -0.032243438 -1.1931419
#> [65,] 31.46309 -0.015660756 -1.1197044
#> [66,] 34.22477 -0.025875124 -1.3415898
#> [67,] 37.68134 -0.027979933 -1.7807292
#> [68,] 33.35864 -0.017707105 -1.4843595
#> [69,] 33.76621 -0.021178795 -1.5660815
#> [70,] 38.65470 -0.020472447 -2.1995093
#> [71,] 30.48207 -0.028090660 -0.6645297
#> [72,] 32.25134 -0.034734893 -0.6216782
#> [73,] 30.90073 -0.028385834 -0.7434711
#> [74,] 33.09523 -0.033242923 -0.8108385
#> [75,] 35.14176 -0.019699942 -1.5724585
#> [76,] 37.28685 -0.012467155 -2.4016195
#> [77,] 36.72132 -0.008554207 -2.4195983
#> [78,] 36.02494 -0.024415962 -1.8528437
#> [79,] 34.11541 -0.031099366 -1.2083065
#> [80,] 34.19654 -0.020677674 -1.4154843
#> [81,] 30.07842 -0.025284376 -0.6767066
#> [82,] 37.29116 -0.018444192 -2.3045275
#> [83,] 38.45071 -0.010092197 -2.5877892
#> [84,] 33.82277 -0.023651190 -1.3520588
#> [85,] 34.66278 -0.014667477 -1.7998157
#> [86,] 31.40191 -0.032412551 -0.7281680
#> [87,] 42.25891 -0.009559488 -3.1434194
#> [88,] 34.35595 -0.006480486 -2.1190544
#> [89,] 36.11058 -0.010001272 -2.2122046
#> [90,] 33.28499 -0.012173669 -1.6930537
#> [91,] 34.92931 -0.018505094 -1.5643630
#> [92,] 35.37642 -0.025342419 -1.5472113
#> [93,] 35.41801 -0.013244018 -1.9558777
#> [94,] 36.00575 -0.022021632 -1.7105708
#> [95,] 33.56557 -0.031543846 -0.9619895
#> [96,] 34.27518 -0.026714387 -1.2335824
#> [97,] 36.98794 -0.003431031 -2.5075174
#> [98,] 38.92558 -0.018050955 -2.3751259
#> [99,] 37.27147 -0.015998570 -2.1360520
#> [100,] 30.62294 -0.033973310 -0.4645830