# permustats: Extract, Analyse and Display Permutation Results In vegan: Community Ecology Package

## Description

The `permustats` function extracts permutation results of vegan functions. Its support functions can find quantiles and standardized effect sizes, plot densities and Q-Q plots.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```permustats(x, ...) ## S3 method for class 'permustats' summary(object, interval = 0.95, ...) ## S3 method for class 'permustats' densityplot(x, data, xlab = "Permutations", ...) ## S3 method for class 'permustats' density(x, observed = TRUE, ...) ## S3 method for class 'permustats' qqnorm(y, observed = TRUE, ...) ## S3 method for class 'permustats' qqmath(x, data, observed = TRUE, ylab = "Permutations", ...) ```

## Arguments

 `object, x, y` The object to be handled. `interval` numeric; the coverage interval reported. `xlab, ylab` Arguments of `densityplot` and `qqmath` functions. `observed` Add observed statistic among permutations. `data` Ignored. `...` Other arguments passed to the function. In `density` these are passed to `density.default`.

## Details

The `permustats` function extracts permutation results and observed statistics from several vegan functions that perform permutations or simulations.

The `summary` method of `permustats` estimates the standardized effect sizes (SES) as the difference of observed statistic and mean of permutations divided by the standard deviation of permutations (also known as z-values). It also prints the the mean, median, and limits which contain `interval` percent of permuted values. With the default (`interval = 0.95`), for two-sided test these are (2.5%, 97.5%) and for one-sided tests either 5% or 95% quantile depending on the test direction. The mean, quantiles and z values are evaluated from permuted values without observed statistic.

The `density` and `densityplot` methods display the kernel density estimates of permuted values. When observed value of the statistic is included in the permuted values, the `densityplot` method marks the observed statistic as a vertical line. However the `density` method uses its standard `plot` method and cannot mark the obseved value.

The `qqnorm` and `qqmath` display Q-Q plots of permutations, optionally together with the observed value (default) which is shown as horizontal line in plots. `qqnorm` plots permutation values against standard Normal variate. `qqmath` defaults to the standard Normal as well, but can accept other alternatives (see standard `qqmath`).

Functions `density` and `qqnorm` are based on standard R methods and accept their arguments. They only handle one statistic, and cannot be used when several test statistic were evaluated. The `densityplot` and `qqmath` are lattice graphics, and can be used both for one and several statistics. All these functions pass arguments to their underlying functions; see their documentation.

The `permustats` can extract permutation statistics from the results of `adonis`, `anosim`, `anova.cca`, `mantel`, `mantel.partial`, `mrpp`, `oecosimu`, `ordiareatest`, `permutest.cca`, `protest`, and `permutest.betadisper`.

## Value

The `permustats` function returns an object of class `"permustats"`. This is a list of items `"statistic"` for observed statistics, `permutations` which contains permuted values, and `alternative` which contains text defining the character of the test (`"two.sided"`, `"less"` or `"greater"`). The `qqnorm` and `density` methods return their standard result objects.

## Author(s)

Jari Oksanen with contributions from Gavin L. Simpson (`permustats.permutest.betadisper` method and related modifications to `summary.permustats` and the `print` method) and Eduard Szöcs (`permustats.anova.cca).`

`density`, `densityplot`, `qqnorm`, `qqmath`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```data(dune) data(dune.env) mod <- adonis(dune ~ Management + A1, data = dune.env) ## use permustats perm <- permustats(mod) summary(perm) densityplot(perm) qqmath(perm) ## example of multiple types of statistic mod <- with(dune.env, betadisper(vegdist(dune), Management)) pmod <- permutest(mod, nperm = 99, pairwise = TRUE) perm <- permustats(pmod) summary(perm, interval = 0.90) ```

### Example output  ```Loading required package: permute
This is vegan 2.4-3

statistic    SES   mean lower median  upper Pr(perm)
Management    3.0730 4.9489 1.0309       0.9443 1.7981    0.002 **
A1            2.7676 2.8427 1.0327       0.9160 2.1656    0.021 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Interval (Upper - Lower) = 0.95)

statistic     SES    mean   lower  median   upper Pr(perm)
Overall (F)    1.9506  0.6252  1.1798          0.8134  2.5912    0.160
BF-HF (t)     -0.5634 -0.4703  0.0221 -1.9582  0.0326  2.0450    0.567
BF-NM (t)     -2.2387 -1.9505  0.0390 -1.8576  0.0248  1.9234    0.041 *
BF-SF (t)     -1.1675 -0.9549 -0.0052 -2.0393  0.0093  1.8597    0.289
HF-NM (t)     -2.1017 -1.8494 -0.0042 -1.7651 -0.0489  2.0033    0.061 .
HF-SF (t)     -0.8789 -0.7492 -0.0166 -1.8624 -0.0453  1.8806    0.397
NM-SF (t)      0.9485  0.8862 -0.0097 -1.7603 -0.0003  1.6414    0.355
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Interval (Upper - Lower) = 0.9)
```

vegan documentation built on May 2, 2019, 5:51 p.m.