Generate random samples from the inverse Gaussian distribution

This function generates random samples from the inverse Gaussian distribution.

Usage

r_ig(lots, n, mu, lambda)

Arguments

lots: The number of lots to generate.
n: The number of observations per lot.
mu: The mean parameter of the distribution.
lambda: The shape parameter of the distribution.

Value

A matrix of random samples from the inverse Gaussian distribution.

Examples

r_ig(lots = 5, n = 10, mu = 1, lambda = 2)
#>                n_1       n_2       n_3       n_4       n_5       n_6       n_7
#> sample_1 0.5434226 0.4990660 0.2577697 0.8958035 1.8368464 0.4240089 0.7741744
#> sample_2 0.3089847 3.8843986 1.3385396 1.0052560 0.6848023 0.7498329 0.8997784
#> sample_3 0.6247962 0.2217997 1.2744221 1.9818766 2.6427214 0.5814835 1.6960249
#> sample_4 0.3614950 0.8276285 0.6085226 0.5192100 1.7795216 0.8815054 0.9980313
#> sample_5 1.6893568 3.9999973 0.3401027 1.2250130 1.1238594 3.9186088 0.6053483
#>                n_8       n_9      n_10
#> sample_1 0.8535138 0.3009333 1.4889848
#> sample_2 0.6369664 0.5406334 1.6311242
#> sample_3 0.6918169 1.0673099 0.6885640
#> sample_4 1.1684445 0.8340929 0.2484196
#> sample_5 3.1605972 0.4800952 0.9063288