Description Usage Arguments Details Value References Examples

Fitting a series of mixtures of conjugate distributions to a
`sample`

, using Expectation-Maximization (EM). The number of
mixture components is specified by the vector `Nc`

. First a
`Nc[1]`

component mixture is fitted, then a `Nc[2]`

component mixture, and so on. The mixture providing the best AIC
value is then selected.

1 |

`sample` |
Sample to be fitted by a mixture distribution. |

`Nc` |
Vector of mixture components to try out (default |

`k` |
Penalty parameter for AIC calculation (default 6) |

`thresh` |
The procedure stops if the difference of subsequent AIC values
is smaller than this threshold (default -Inf). Setting the threshold to 0
stops |

`verbose` |
Enable verbose logging. |

`...` |
Further arguments passed to |

The `type`

argument specifies the distribution of
the mixture components, and can be a normal, beta or gamma
distribution.

The penalty parameter `k`

is 2 for the standard AIC
definition. *Collet (2003)* suggested to use values in the
range from 2 to 6, where larger values of `k`

penalize more
complex models. To favor mixtures with fewer components a value of
6 is used as default.

As result the best fitting mixture model is returned,
i.e. the model with lowest AIC. All other models are saved in the
attribute `models`

.

Collet D.
*Modeling Survival Data in Medical Research*.
2003; Chapman and Hall/CRC.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# random sample of size 1000 from a mixture of 2 beta components
bm <- mixbeta(beta1=c(0.4, 20, 90), beta2=c(0.6, 35, 65))
bmSamp <- rmix(bm, 1000)
# fit with EM mixture models with up to 10 components and stop if
# AIC increases
bmFit <- automixfit(bmSamp, Nc=1:10, thresh=0, type="beta")
bmFit
# advanced usage: find out about all discarded models
bmFitAll <- attr(bmFit, "models")
sapply(bmFitAll, AIC, k=6)
``` |

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