Description Usage Arguments Details Value Authors Examples

clusterSelect fits models with varying values of G to determine the appropriate number of archetype species.

1 2 3 | ```
#clusterSelect(sp.form,sp.data,covar.data,G=1:10,\n
#em.prefit=TRUE, em.steps=4 ,em.refit=3,\n
#est.var=FALSE,trace=TRUE)
``` |

` sp.form` |
an object of class "formula" (or one that can be coerced to that class):a symbolic description of the model to be fitted |

` sp.data` |
a data frame containing the species information. The frame is arranged so that each row is a site and each column is a species. Species names should be included as column names otherwise numbers from 1:S are assigned. |

` covar.data` |
a data frame containng the covariate data for each site. Names of columns must match that given in |

` G` |
Vector containing the range of archetype species to fit. |

` em.prefit` |
obtain initial parameter estimates from EM |

` em.steps` |
number of EM steps to do if using em.prefit |

` em.refit` |
refits model so that the global maxima can be found using EM. |

` est.var` |
calculate the variance covariace matrix for each group |

` trace` |
the trace of the EM steps |

fits multiple fitMix models across the range of values for G. Most of the arguments are passed directly to fitMix

` aic` |
vector containing the aic value for each value of G |

` bic` |
bic |

` fm` |
a list containing all output from each vaule of G. |

Piers Dunstan and Scott Foster

1 2 3 4 5 6 7 | ```
G <-4
S <- 20
theta <- matrix(c(-0.9,-0.6,0.5,1,-0.9,1,0.9,-0.9),4,2,byrow=TRUE)
dat <- data.frame(y=rep(1,100),x=runif(100,0,2.5),z=rnorm(100,10,2))
dat1 <- artificial.data(y~1+x,dat,theta,S)
dat <- dat[,2:3]
clusters <- clusterSelect(obs~1+x,dat1$pa,dat,G=2:5,em.refit=2)
``` |

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