optpart: tabdev – R documentation

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tabdev

Classification Validity Assessment by Table Deviance

Description

Table deviance is a method to assess the quality of classifications by calculating the clarity of the classification with respect to the original data, as opposed to a dissimilarity or distance matrix representation

Usage

## Default S3 method:
tabdev(x,clustering,nitr=999,...)
    ## S3 method for class 'stride'
tabdev(x,taxa,...)
    ## S3 method for class 'tabdev'
summary(object,p=0.05,...)

Arguments

`x`	a matrix or data.frame of multivariate observations, with objects as rows, and attributes as columns
`clustering`	a vector of integer cluster assignments, or an object of class ‘clustering’ or ‘partana’
`nitr`	number of iterations to perform in calculating the probability of obtaining as effective a classification as observed
`taxa`	a data.frame with samples as rows and species as columns
`object`	and object of class ‘tabdev’
`p`	the maximum probability threshold to list species in the summary table
`...`	ancillary arguments to maintain compatibility with generic summary function

Details

Tabdev calculates the concentration of values within clusters. For each column, tabdev calculates the sum of values within classes and the sum within classes divided by the sum of that column to get fractional sums by class. These values are used to calculate the deviance of each row. Attributes that are widely dispersed among classes exhibit high deviance; attributes that are concentrated within a single class contribute zero deviance. An effective classification should exhibit low deviance.

Tabdev then permutes the values within columns and calculates the probability of observing as low a deviance as observed as $$ (m+1)/(niter + 1)$$ where $m$ is the number of cases with as low or lower deviance as observed.

Value

a list with components:

`spcdev`	a data.frame with species, deviance, and probability as columns
`totdev`	the total deviance of the entire table

Author(s)

David W. Roberts droberts@montana.edu http://ecology.msu.montana.edu/labdsv/R

Examples

data(shoshveg)
dis.bc <- dsvdis(shoshveg,'bray/curtis')
opt.5 <- optpart(5,dis.bc)
tabdev(shoshveg,opt.5)

optpart

Optimal Partitioning of Similarity Relations

v3.0-3

GPL (>= 2)

Authors

David W. Roberts <droberts@montana.edu>

Initial release