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
the sample size
vectors of means of the latent variable
covariance matrix of the latent variable
Numeric vector of target depths. The first is recycled if there are not n
values
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 554 1417 2449 1443 447
#> S2 187 1078 585 326 1542
#> S3 1009 2634 627 2245 853
#> S4 2637 372 192 783 1349
#> S5 494 716 2682 539 647
#> S6 7127 889 793 1584 967
#> S7 525 1022 5516 1368 5607
#> S8 1607 1085 167 1113 552
#> S9 1488 594 2125 887 1204
#> S10 1174 1418 431 1496 224
#> 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 69 209 79 53 87 7 12 13 20 7
#> S2 6775 7194 5423 4558 14735 511 1725 1069 985 1017
#> 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