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

Isopam classification is performed either as a hierarchical, divisive method, or as non-hierarchical partitioning. Optimizes clusters and optionally cluster numbers for maximum performance of group indicators. Developed for matrices representing species abundances in plots.

1 2 3 4 5 6 7 8 9 10 |

`dat` |
data matrix: each row corresponds to an object (typically a plot), each column corresponds to a descriptor (typically a species). All variables must be numeric. Missing values (NAs) are not allowed. At least 3 rows (plots) are required. |

`c.fix` |
number of clusters (defaults to |

`c.opt` |
if |

`c.max` |
maximum number of clusters per partition.
Applies to all partitioning steps if |

`l.max` |
maximum number of hierarchy levels. Defaults
to |

`stopat` |
vector with stopping rules for hierarchical
clustering. Two values define if a partition should be
retained in hierarchical clustering: the first determines
how many indicators must be present per cluster, the second
defines the standardized G-value that must be reached by
these indicators. |

`sieve` |
logical. If |

`Gs` |
threshold (standardized G value) for descriptors
(species) to be considered in the search for a good
clustering solution. Effective with |

`ind` |
optional vector of column names from |

`centers` |
optional vector with observations used as cluster cores (supervised classification). |

`distance` |
distance measure for the distance matrix used as a starting point for Isomap. Any distance measure implemented in packages vegan or proxy can be used (see details). |

`k.max` |
maximum Isomap |

`d.max` |
maximum number of Isomap dimensions. |

`...` |
other arguments to S3 functions |

`juice` |
logical. If |

`x` |
an |

Isopam is described in Schmidtlein et al. (2010). It consists of dimensionality reduction (Isomap: Tenenbaum et al. 2000; isomap in vegan) and partitioning of the resulting ordination space (PAM: Kaufman & Rousseeuw 1990; pam in cluster). The classification is performed either as a hierarchical, divisive method, or as non-hierarchical partitioning. Compared to other clustering methods, it has the following features: (a) it optimizes partitions for the performance of group indicators (typically species) or for maximum average 'fidelity' of descriptors to groups; (b) it optionally selects the number of clusters per division; (c) the shapes of groups in feature space are not limited to spherical or other regular geometric shapes (thanks to the underlying Isomap algorithm) and (d) the distance measure used for the initial distance matrix can be freely defined.

Currently, the `plot`

and `identify`

methods
for class `isopam`

simply link to the
hclust object `$dendro`

resulting
from `isopam`

in case of hierarchical partitioning.
The methods work just like `plot.hclust`

and
`identify.hclust`

.

The preset distance measure is Bray-Curtis (Odum 1950).
Distance measures are passed to
vegdist in vegan. If vegan does
not know the given measure it is passed to
dist in proxy. Measures available
in vegan are listed in vegdist.
Measures registered in proxy can be listed with
`summary(pr_DB)`

once proxy is loaded. New
measures can be defined and registered as described in
`?pr_DB`

. Isopam can't deal with distance matrices
as a replacement for the original data matrix because it
operates on individual descriptors (species).

`call` |
generating call |

`distance` |
distance measure used by Isomap |

`flat` |
observations (plots) with group affiliation. Running group numbers for each level of the hierarchy. |

`hier` |
observations (plots) with group affiliation. Group identifiers reflect the cluster hierarchy. Not present with only one level of partitioning. |

`medoids` |
observations (plots) representing the medoids of the resulting groups. |

`analytics` |
table summarizing parameter settings for
the final partitioning steps. |

`dendro` |
an object of class |

`dat` |
data used |

For large datasets, Isopam may need too much memory or too much
computation time. The optimization procedure (selection
of Isomap dimensions and -*k*, optionally selection of cluster
numbers) is based on a brute force approach that takes its time
with large data sets. Low speed is inherent to the method, so don't
complain. If used with data not representing species in plots make
sure that the indicator approach is appropriate.

With very small datasets, the indicator based optimization may fail.
In such cases consider using `filtered = FALSE`

instead of
the default method.

Sebastian Schmidtlein with contributions from Jason Collison and Lubomir Tichý

Odum, E.P. (1950): Bird populations in the Highlands (North
Carolina) plateau in relation to plant succession and avian
invasion. *Ecology* **31**: 587–605.

Kaufman, L., Rousseeuw, P.J. (1990): *Finding groups in
data*. Wiley.

Schmidtlein, S., Tichý, L., Feilhauer, H., Faude, U.
(2010): A brute force approach to vegetation classification.
*Journal of Vegetation Science* **21**: 1162–1171.

Tenenbaum, J.B., de Silva, V., Langford, J.C. (2000): A global
geometric framework for nonlinear dimensionality reduction.
*Science* **290**, 2319–2323.

`isotab`

for a table of descriptor (species)
frequency in clusters.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## load data to the current environment
data(andechs)
## call isopam with the standard options
ip<-isopam(andechs)
## examine cluster hierarchy
plot(ip)
## examine grouping
ip$flat
## examine frequency table (second hierarchy level)
isotab(ip, 2)
## non-hierarchical partitioning
ip<-isopam(andechs,c.fix=3)
ip$flat
``` |

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