Description Usage Arguments Value Author(s) Examples

When fitting finite mixture models two cases must be distinguished. The
flexible support size case, where no assumption about the
number of components `k`

is made in advance and the fixed support size
case. For the flexible support size case the VEM-algorithm can be used.

The algorithm proceeds as follows:

Step 1: Define an approximating grid lambda[1], ..., lambda[L]

Step 2: Use the VEM algorithm to maximize `L(P)`

in the simplex
*Ω* and identify grid points with positive support.

1 2 3 |

`mix` |
A CAMAN-object which quantifies a finite mixture model and the input data. |

`obs` |
observed / dependent variable. Vector or colname of |

`weights` |
weights of the data. Vector or colname of |

`family` |
the underlying type density function as a character ("gaussian", "poisson" or "binomial")! |

`data` |
an optional data frame. |

`pop.at.risk` |
population at risk: These data could be used to determine a mixture model for Poisson data. Vector or colname of |

`var.lnOR` |
variances of the data: These variances might be given when working with meta analyses! Vector or colname of |

`limit` |
parameter to control the limit of union several components. Default is 0.01. |

`acc` |
convergence criterion. VEM and EM loops stop when deltaLL<acc. Default is 10^(-7). |

`numiter` |
parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000. |

`startk` |
starting/maximal number of components. This number will be used to compute the grid in the VEM. Default is 50. |

The function returns a `CAMAN.VEM.object`

object.

Peter Schlattmann and Johannes Hoehne

1 2 3 4 |

```
Loading required package: sp
Loading required package: mvtnorm
```

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