# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ``` 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 31, 2017, 4:06 a.m.