rMMSN | R Documentation |

It generates random realizations following a multivariate finite mixture of Skew-normal (`family == "SN"`

) and normal (`family == "Normal"`

) distributions under censoring. Censoring level can be set as a percentage and it can be adjusted per group if desired.

```
rMMSN(n = NULL, mu = NULL, Sigma = NULL, shape = NULL, percent = NULL,
each = FALSE, pii = NULL, family = "SN")
```

`n` |
number of observations |

`mu` |
a list with |

`Sigma` |
a list with |

`shape` |
a list with |

`percent` |
Percentage of censored data in each group or data as a whole (see next item). |

`each` |
If |

`pii` |
a vector of weights for the mixture of dimension |

`family` |
distribution family to be used for fitting. Options are "SN" for the Skew-normal and "Normal" for the Normal distribution respectively. |

It returns a list that depending of the case, it returns one or more of the following objects:

`y` |
a |

`G` |
a vector of length |

`cutoff` |
a vector containing the censoring cutoffs per group. |

Francisco H. C. de Alencar hildemardealencar@gmail.com, Christian E. Galarza cgalarza88@gmail.com, Victor Hugo Lachos hlachos@uconn.edu and Larissa A. Matos larissam@ime.unicamp.br

Maintainer: Francisco H. C. de Alencar hildemardealencar@gmail.com

Cabral, C. R. B., Lachos, V. H., & Prates, M. O. (2012). Multivariate mixture modeling using skew-normal independent distributions. Computational Statistics & Data Analysis, 56(1), 126-142.

Prates, M. O., Lachos, V. H., & Cabral, C. (2013). mixsmsn: Fitting finite mixture of scale mixture of skew-normal distributions. Journal of Statistical Software, 54(12), 1-20.

C.E. Galarza, L.A. Matos, D.K. Dey & V.H. Lachos. (2019) On Moments of Folded and Truncated Multivariate Extended Skew-Normal Distributions. Technical report. ID 19-14. University of Connecticut.

F.H.C. de Alencar, C.E. Galarza, L.A. Matos & V.H. Lachos. (2019) Finite Mixture Modeling of Censored and Missing Data Using the Multivariate Skew-Normal Distribution. echnical report. ID 19-31. University of Connecticut.

`fit.FMMSNC`

, `rMSN`

and `rMMSN.contour`

```
mu <- Sigma <- shape <- list()
mu[[1]] <- c(-3,-4)
mu[[2]] <- c(2,2)
shape[[1]] <- c(-2,2)
shape[[2]] <- c(-3,4)
Sigma[[1]] <- matrix(c(3,1,1,4.5), 2,2)
Sigma[[2]] <- matrix(c(2,1,1,3.5), 2,2)
pii <- c(0.6,0.4)
percent <- c(0.1,0.1)
family <- "SN"
n <-100
set.seed(20)
rMMSN(n = n,pii = pii, mu = mu, Sigma = Sigma, shape = shape,
percent = percent, each = TRUE, family = family)
```

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