rPLSIM | R Documentation |

Using the piecewise linear PLSIM method to simulate non-normal data

```
rPLSIM(
N,
sigma.target,
skewness,
excesskurtosis,
reps = 1,
numsegments = 4,
gammalist = NULL,
monot = FALSE,
verbose = TRUE
)
```

`N` |
Number of observations to simulate. |

`sigma.target` |
Target population covariance matrix |

`skewness` |
Target skewness |

`excesskurtosis` |
Target excess kurtosis |

`reps` |
Number of simulated samples |

`numsegments` |
The number of line segments in each marginal |

`gammalist` |
A list of breakpoints in each margin |

`monot` |
True if piecewise linear functions are forced to be monotonous. The copula will then be normal. |

`verbose` |
If true, progress details of the procedure are printed |

A list with two elements. First element: the list of simulated samples. Second element: The fitted piecewise linear functions and the intermediate correlations matrix.

Njål Foldnes (njal.foldnes@gmail.com)

Foldnes, N. and Grønneberg S. (2021). Non-normal data simulation using piecewise linear transforms.Under review.

```
set.seed(1)
sigma.target <- cov(MASS::mvrnorm(5, rep(0,3), diag(3)))
res <- covsim::rPLSIM(10^5, sigma.target, skewness=rep(1,3), excesskurtosis=rep(4,3))
my.sample <- res[[1]][[1]]
```

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