Skip to contents

Tests of randomization between distances applied to 'kdist' objetcs

corkdist.Rd

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

Usage

mantelkdist (kd, nrepet = 999, ...)
RVkdist (kd, nrepet = 999, ...)
# S3 method for class 'corkdist'
plot(x, whichinrow = NULL, whichincol = NULL, 
   gap = 4, nclass = 10,...)

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

...

further arguments passed to or from other methods

Value

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

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.

Author

Daniel Chessel
Stéphane Dray stephane.dray@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)
#> Warning: Zero distance(s)
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)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)
#> Warning: Zero distance(s)


data(yanomama)
m1 <- mantelkdist(kdist(yanomama), 999)
m1
#> Mantel's tests for 'kdist' object
#> class: corkdist list 
#> Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
#> 
#> gen-geo 
#> Monte-Carlo test
#> Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
#> 
#> Observation: 0.5098684 
#> 
#> Based on 999 replicates
#> Simulated p-value: 0.001 
#> Alternative hypothesis: greater 
#> 
#>     Std.Obs Expectation    Variance 
#> 3.196556810 0.001240244 0.025318383 
#> 
#> ant-geo 
#> Monte-Carlo test
#> Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
#> 
#> Observation: 0.8428053 
#> 
#> Based on 999 replicates
#> Simulated p-value: 0.001 
#> Alternative hypothesis: greater 
#> 
#>      Std.Obs  Expectation     Variance 
#>  5.432687465 -0.005692316  0.024393370 
#> 
#> ant-gen 
#> Monte-Carlo test
#> Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
#> 
#> Observation: 0.2995506 
#> 
#> Based on 999 replicates
#> Simulated p-value: 0.051 
#> Alternative hypothesis: greater 
#> 
#>     Std.Obs Expectation    Variance 
#> 1.689089145 0.009678608 0.029451488 
#> list of 3 'randtest' objects
summary(m1)
#> Mantel's tests for 'kdist' object
#> Call: mantelkdist(kd = kdist(yanomama), nrepet = 999)
#> Simulated p-values:
#>         1     2 3
#> geo     -     - -
#> gen 0.001     - -
#> ant 0.001 0.051 -
plot(m1)