Description Usage Arguments Value Note Author(s) Examples

This function may be used to estimate the number of components based on a
nonparametric bootstrap approach.A bootstrap sample is obtained from the
original sample with replacement. Corresponding to the bootstrap data set we
obtain an estimate of the number of components `k`

applying a combination
of the VEM- and EM algorithm.
The bootstrap algorithm involves drawing `B`

independent bootstrap samples
and estimating `k`

using the hybrid mixture algorithm. The result is
the bootstrap distribution of the number of components `k`

.
The mode of this distribution is taken as an estimate of the number of components.

1 2 | ```
mixalg.boot(mix, nboot=500, limit=0.01, acc=10^(-5), numiter=5000,
startk=50, returnBootstrapRep=FALSE)
``` |

the parameters `limit, acc, numiter`

and `startk`

are used for the VEM algorithm in each bootstrap sample.

`mix` |
A CAMAN-object which quantifies a finite mixture model. |

`nboot` |
number of bootstrap replications |

`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 for the VEM algorithm in each bootstrap sample. This number will be used to compute the grid in the VEM. Default is 50. |

`returnBootstrapRep` |
A Boolean that indicates whether the bootstrapped data should be returned or not |

The function returns a list, describing the bootstrap replications:

`$dat.bootstrap` |
(used) sampled data |

`$LL` |
Likelihood of the final solutions of each bootstrap replication |

`$LL_k1` |
vector with LL for each bootstrap replication using a homogeneous model (k=1) |

`$numk.boot` |
number of components obtained in replications. |

`mixalg.Boot`

and `mixboot`

are deprecated names for the `mixalg.boot`

function.

Peter Schlattmann and Johannes Hoehne

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
### POISSON data with weights: thai_cohort
data(thai_cohort)
m.thai <- mixalg(obs="counts", weights="frequency",
family="poisson", data=thai_cohort,
acc = 0.00003)
## Not run: boot <- mixalg.boot(m.thai, nboot=1000) #may take a few minutes
### POISSON data with observed and expected data: hepab
data(hepab)
mix <- mixalg(obs="observations",pop.at.risk="expected",family= "poisson",data=hepab)
## Not run: boot <- mixalg.boot(mix, nboot=250) #may take some time
table(boot$numk.boot)
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.