Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function studies the association between species patterns and combinations of groups of sites.

1 2 3 |

`x` |
Community data table |

`cluster` |
A vector representing a partition of sites |

`func` |
Species-site group association function. Four values are accepted |

`duleg` |
If TRUE, site group combinations are not considered, only the original site groups, like in Dufrêne & Legendre (1997). Internally, |

`restcomb` |
A vector of integer values used to restrict the combinations of site groups to those with ecological sense according to the analyst. The default |

`min.order` |
An integer indicating the minimum order of site group combinations (by default |

`max.order` |
An integer indicating the maximum order of site group combinations to be considered: |

`control` |
a list of control values describing properties of the permutation design, as returned by a call to |

`permutations` |
a custom matrix of permutations, to be used if |

`print.perm` |
If TRUE, prints permutation numbers after each set of 100 permutations. |

This function creates combinations of the input clusters and compares each combination with the species in the input matrix x. For each species it chooses the combination with a highest association value. Best matching patterns are tested for statistical significance of the associations. Four association indices are possible (some less than for `strassoc`

): "IndVal", "IndVal.g", "r" and "r.g". Indicator value indices will return the pattern that better matches the species observed pattern, whereas correlation indices will return the pattern that creates a highest inside/outside difference. Details are given in De Cáceres et al. (2010). The user can restrict the combinations in three ways: (1) by using `duleg=TRUE`

, which leads to consider single site-groups only; (2) by setting the minimum and maximum order of combinations using `min.order`

and `max.order`

; or (3) by using `restcomb`

to restrict combinations at will. In order to carry out the third way, values in `restcomb`

must be the indices of combinations that appear in the column `index`

of object `sign`

(see below).

Complex permutation designs are allowed through the function `how`

from package "permute". If those are not enough, the user can set `control = NULL`

and specify a custom matrix of permutations to test with parameter `permutations`

.

An object of class `multipatt`

with:

`func` |
The name of the function used. |

`comb` |
A matrix describing all the combinations studied. |

`str` |
A matrix the association strength for all combinations studied. |

`A` |
If |

`B` |
If |

`sign` |
Data table with results of the best matching pattern, the association value and the degree of statistical significance of the association (i.e. p-values from permutation test). Note that p-values are not corrected for multiple testing. |

This function gives the same results as function `indval`

in package "labdsv" when used setting `func="IndVal.g"`

and `duleg=TRUE`

, excepting the fact that the square root IndVal values is returned instead of the original IndVal.

Miquel De Cáceres Ainsa, CTFC

Florian Jansen, Institute of Botany and Landscape Ecology, Ernst-Moritz-Arndt-University

De Cáceres, M. and Legendre, P. 2009. Associations between species and groups of sites: indices and statistical inference. Ecology 90(12): 3566-3574.

De Cáceres, M., Legendre, P., Moretti, M. 2010. Improving indicator species analysis by combining groups of sites. Oikos 119(10): 1674-1684.

Dufrêne, M. and P. Legendre. 1997. Species assemblages and indicator species: The need for a flexible asymetrical approach. Ecological Monographs 67:345-366.

`summary.multipatt`

, `strassoc`

, `signassoc`

, `how`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
library(stats)
data(wetland) ## Loads species data
wetkm = kmeans(wetland, centers=3) ## Creates three clusters using kmeans
## Runs the combination analysis using IndVal.g as statistic
wetpt = multipatt(wetland, wetkm$cluster, control = how(nperm=999))
## Lists those species with significant association to one combination
summary(wetpt)
## Lists those species with significant association to one combination,
## including indval components.
summary(wetpt, indvalcomp=TRUE)
``` |

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