Random generation for the PLN model with latent mean equal to mu, latent covariance matrix equal to Sigma and average depths (sum of counts in a sample) equal to depths
a n * p count matrix, with row-sums close to depths, with an attribute "offsets" corresponding to the true generated offsets (in log-scale).
The default value for mu and Sigma assume equal abundances and no correlation between the different species.
## 10 samples of 5 species with equal abundances, no covariance and target depths of 10,000
rPLN()
#> Y1 Y2 Y3 Y4 Y5
#> S1 898 697 2706 1924 1658
#> S2 868 6373 2061 700 4015
#> S3 912 2881 3406 4747 3084
#> S4 542 1714 2802 319 562
#> S5 1005 776 1832 2267 819
#> S6 1008 3069 623 3300 1981
#> S7 2507 3898 2123 875 1494
#> S8 1358 1142 738 1131 328
#> S9 4463 2685 1033 218 3920
#> S10 399 3956 576 443 1561
#> attr(,"offsets")
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [2,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [3,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [4,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [5,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [6,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [7,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [8,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [9,] 7.100902 7.100902 7.100902 7.100902 7.100902
#> [10,] 7.100902 7.100902 7.100902 7.100902 7.100902
## 2 samples of 10 highly correlated species with target depths 1,000 and 100,000
## very different abundances
mu <- rep(c(1, -1), each = 5)
Sigma <- matrix(0.8, 10, 10); diag(Sigma) <- 1
rPLN(n=2, mu = mu, Sigma = Sigma, depths = c(1e3, 1e5))
#> Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10
#> S1 169 129 190 259 203 14 17 29 21 9
#> S2 19184 16106 21985 48841 51481 1911 2253 1955 2895 2644
#> attr(,"offsets")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 3.671389 3.671389 3.671389 3.671389 3.671389 3.671389 3.671389 3.671389
#> [2,] 8.276560 8.276560 8.276560 8.276560 8.276560 8.276560 8.276560 8.276560
#> [,9] [,10]
#> [1,] 3.671389 3.671389
#> [2,] 8.276560 8.276560