isopam: Isopam (Clustering)

In isopam: Clustering of Sites with Species Data

Isopam (Clustering)

Description

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 and with a diagnostic species approach in mind, thus optimizing the concentration of indicative species in groups. Apart from from the default auto-pilot mode, predefined indicative species and cluster medoids can be added for a supervised classification.

Usage

     isopam(dat, c.fix = FALSE, c.opt = TRUE, c.max = 6,
            l.max = FALSE, stopat = c(1,7), sieve = TRUE,
            Gs = 3.5, ind = NULL, centers = NULL, distance = 'bray',
            k.max = 100, d.max = 7, juice = FALSE,
            wordy = TRUE, ...)

     isopamp(dat, c.fix = FALSE, c.opt = TRUE, c.max = 6,
            l.max = FALSE, stopat = c(1,7), sieve = TRUE,
            Gs = 3.5, ind = NULL, centers = NULL, distance = 'bray',
            k.max = 100, d.max = 7, juice = FALSE,
            wordy = TRUE, ...)

     ## S3 method for class 'isopam'
identify(x, ...)
     ## S3 method for class 'isopam'
plot(x, ...)
     ## S3 method for class 'isopam'
summary(object, ...)
     

Arguments

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 FALSE). If a number is given, non-hierarchical partitioning is performed, c.opt and c.max are ignored and l.max is set to one.
c.opt	if TRUE (the default) cluster numbers are optimized in the range between 2 and c.max (slow and thorough). If FALSE groups are divided into two subgroups (quick and dirty).
c.max	maximum number of clusters per partition. Applies to all partitioning steps if c.opt = TRUE.
l.max	maximum number of hierarchy levels. Defaults to FALSE (no maximum number). Note that divisions may stop well before this number is reached (see stopat). Use l.max = 1 for non-hierarchical partitioning (or use c.fix).
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. stopat is not effective at the first hierarchy level or in non-hierarchical partitioning.
sieve	logical. If TRUE (the deafult), only descriptors (species) exceeding a threshold defined by Gs are used in the search for a good clustering solution. Their number is multiplied with their mean standardized G-value. The product is used as optimality criterion. If FALSE all descriptors are used for optimization.
Gs	threshold (standardized G value) for descriptors (species) to be considered in the search for a good clustering solution. Effective with sieve = TRUE.
ind	optional vector of column names from dat defining descriptors (species) used as indicators. This turns Isopam in an expert system. Replaces the automated selection of indicators with sieve = TRUE (ind overrules sieve).
centers	optional vector with observations used as cluster cores (supervised classification).
distance	name of a dissimilarity index for the distance matrix used as a starting point for Isomap. Any distance measure implemented in packages vegan (predefined or using a designdist equation) or proxy can be used (see details).
k.max	maximum Isomap k.
d.max	maximum number of Isomap dimensions.
juice	logical. If TRUE input files for Juice are generated.
wordy	logical. If TRUE status messages are shown.
...	other arguments used by juice or passed to S3 functions plot and identify (see dendrogram and hclust.
x	isopam result object in methods plot and identify.
object	isopam result object in method summary.

Details

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.

The parallelised version of isopam is isopamp. Depending on your device, parallelisation offers a considerable speed advantage with most large objects but no advantage or even slow-down with small objects.

plot creates (and silently returns) an object of class dendrogram and calls the S3 plot method for that class. identify works just like identify.hclust.

The preset distance measure is Bray-Curtis (Odum 1950). Distance measures are passed to vegdist or to designdist in vegan. If this does not work 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). New measures can be defined and registered as described in ?pr_DB. Isopam does not accept distance matrices as a replacement for the original data matrix because it operates on individual descriptors (species).

Progress bars are build using progressor in progressr. Use handlers to switch them on (handlers(global = TRUE)) and off or to tweak them.

Value

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. Name: name of the respective parent cluster (0 in case of the first partition); Subgroups: number of subgroups; Isomap.dim: Isomap dimensions used; Isomap.k.min: minimum possible Isomap k; Isomap.k: Isomap k used; Isomap.k.max: maximum possible Isomap k; Ind.N: number of indicators reaching or exceeding Gs; Ind.Gs: the average standardized G value of these indicators; and Global.Gs: the average standardized G value of all descriptors.
centers_usr	Cluster centers suggested by user.
ind_usr	Indicators suggested by user.
indicators	Indicators used in each partition.
dendro	an object of class hclust representing the clustering (as used by plot). Not present with only one level of partitioning.
dat	data used

Note

For large datasets, consider using the isopamp function instead of isopam. However, 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. 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.

Author(s)

Sebastian Schmidtlein with contributions from Jason Collison and Lubomir Tichý

References

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.

See Also

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

Examples

     ## 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

     

isopam documentation built on Sept. 8, 2023, 5:06 p.m.