yanomama {ade4}R Documentation

Distance Matrices

Description

This data set gives 3 matrices about geographical, genetic and anthropometric distances.

Usage

data(yanomama)

Format

yanomama is a list of 3 components:

geo
is a matrix of 19-19 geographical distances
gen
is a matrix of 19-19 SFA (genetic) distances
ant
is a matrix of 19-19 anthropometric distances

Source

Spielman, R.S. (1973) Differences among Yanomama Indian villages: do the patterns of allele frequencies, anthropometrics and map locations correspond? American Journal of Physical Anthropology, 39, 461–480.

References

Table 7.2 Distance matrices for 19 villages of Yanomama Indians. All distances are as given by Spielman (1973), multiplied by 100 for convenience in: Manly, B.F.J. (1991) Randomization and Monte Carlo methods in biology Chapman and Hall, London, 1–281.

Examples

    data(yanomama)
    gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva
    ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva
    par(mfrow = c(2,2))
    plot(gen, ant)
    t1 <- mantel.randtest(gen, ant, 99);
    plot(t1, main = "gen-ant-mantel") ; print(t1)
    t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99)
    plot(t1, main = "gen-ant-procuste") ; print(t1)
    t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99)
    plot(t1, main = "gen-ant-RV") ; print(t1)

Worked out examples


> library(ade4)
> ### Name: yanomama
> ### Title: Distance Matrices
> ### Aliases: yanomama
> ### Keywords: datasets
> 
> ### ** Examples
> 
>     data(yanomama)
>     gen <- quasieuclid(as.dist(yanomama$gen)) # depends of mva
>     ant <- quasieuclid(as.dist(yanomama$ant)) # depends of mva
>     par(mfrow = c(2,2))
>     plot(gen, ant)
>     t1 <- mantel.randtest(gen, ant, 99);
>     plot(t1, main = "gen-ant-mantel") ; print(t1)
Monte-Carlo test
Call: mantel.randtest(m1 = gen, m2 = ant, nrepet = 99)

Observation: 0.2999879 

Based on 99 replicates
Simulated p-value: 0.06 
Alternative hypothesis: greater 

    Std.Obs Expectation    Variance 
1.779363944 0.007293457 0.027058231 
>     t1 <- procuste.rtest(pcoscaled(gen), pcoscaled(ant), 99)
>     plot(t1, main = "gen-ant-procuste") ; print(t1)
Monte-Carlo test
Observation: 0.6819023 
Call: procuste.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.01 
>     t1 <- RV.rtest(pcoscaled(gen), pcoscaled(ant), 99)
>     plot(t1, main = "gen-ant-RV") ; print(t1)
Monte-Carlo test
Observation: 0.4272698 
Call: RV.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)
Based on 99 replicates
Simulated p-value: 0.02 
> 
> 
> 
> 

[Package ade4 Index]