corkdist {ade4} | R Documentation

## Tests of randomization on the correlation between distances applied to 'kdist' objetcs

### Description

The mantelkdist and RVkdist functions apply to blocks of distance matrices the mantel.rtest et RV.rtest functions.

### Usage

    mantelkdist (kd, nrepet = 999)
    RVkdist (kd, nrepet = 999)
    plot.corkdist (x, whichinrow = NULL, whichincol = NULL,
       gap = 4, nclass = 10, coeff = 1,...)

### Arguments

`kd` | a list of class `kdist`
`nrepet` | the number of permutations
`x` | an objet of class `corkdist`, coming from RVkdist or mantelkdist
`whichinrow` | a vector of integers to select the graphs in rows (if NULL all the graphs are computed)
`whichincol` | a vector of integers to select the graphs in columns (if NULL all the graphs are computed)
`gap` | an integer to determinate the space between two graphs
`nclass` | a number of intervals for the histogram
`coeff` | an integer to fit the magnitude of the graph
`...` | further arguments passed to or from other methods

### Details

The `corkdist` class has some generic functions `print`, `plot` and `summary`. The plot shows bivariate scatterplots between semi-matrices of distances or histograms of simulated values with an error position.

### Value

a list of class `corkdist` containing for each pair of distances an object of class `randtest` (permutation tests).

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr
Anne B Dufour dufour@biomserv.univ-lyon1.fr

### Examples

    data(friday87)
    fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo,
        tabnames = friday87$tab.names)
    fri.kc <- lapply(1:10, function(x) dist.binary(fri.w[[x]],10))
    names(fri.kc) <-  substr(friday87$tab.names,1,4)
    fri.kd <- kdist(fri.kc)
     fri.mantel = mantelkdist(kd = fri.kd, nrepet = 999)
     plot(fri.mantel,1:5,1:5)
     plot(fri.mantel,1:5,6:10)
     plot(fri.mantel,6:10,1:5)
     plot(fri.mantel,6:10,6:10)
    s.corcircle (dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co)
    plot(RVkdist(fri.kd),1:5,1:5)

    data(yanomama)
    m1 <- mantelkdist(kdist(yanomama),999)
    m1
    summary(m1)
    plot(m1)

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