yanomama: Distance Matrices

In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

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)

Example output

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 
 1.70148005  0.01276251  0.02849653 
Monte-Carlo test
Call: procuste.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)

Observation: 0.6819023 

Based on 99 replicates
Simulated p-value: 0.01 
Alternative hypothesis: greater 

    Std.Obs Expectation    Variance 
2.668382474 0.547180243 0.002549068 
Monte-Carlo test
Call: RV.rtest(df1 = pcoscaled(gen), df2 = pcoscaled(ant), nrepet = 99)

Observation: 0.4272698 

Based on 99 replicates
Simulated p-value: 0.03 
Alternative hypothesis: greater 

    Std.Obs Expectation    Variance 
2.814767920 0.252892947 0.003837889 

ade4 documentation built on May 2, 2019, 5:50 p.m.