diagnose.permat {vegan} R Documentation

## Plotting Methods for Matrix Permutation Algorithms

### Description

Plotting and time series extraction methods for 'permat' objects, that are useful in evaluating the performance of different null model settings, especially that of the sequential algorithms.

### Usage

```## S3 method for class 'permat':
plot(x, type = "bray", ylab, xlab, col, lty,
lowess = TRUE, plot = TRUE, text = TRUE, ...)
## S3 method for class 'permat':
lines(x, type = "bray", ...)
## S3 method for class 'permat':
as.ts(x, type = "bray", ...)
## S3 method for class 'permat':
as.mcmc(x)
```

### Arguments

 `x` Object of class `"permat"` created by the functions `permatfull` and `permatswap`. `ylab, xlab, col, lty` graphical parameters for the `plot` method. `type` Character, type of plot to be displayed: `"bray"` for Bray-Curtis dissimilarities, `"chisq"` for Chi-squared values. `lowess, plot, text` Logical arguments for the `plot` method, whether a lowess curve should be drawn, the plot should be drawn, and statistic values should be printed on the plot. `...` Other arguments passed to methods.

### Details

The `plot` method is useful for graphically testing for trend and independence of permuted matrices. This is especially important when using sequential algorithms (`"swap", "tswap", "abuswap"`).

The `as.ts` method can be used to extract Bray-Curtis dissimilarities or Chi-squared values as time series. This can further used in testing independence (see Examples). The method `as.mcmc` is useful for accessing diagnostic tools available in the 'coda' package.

### Value

The `plot` creates a plot as a side effect.
The `as.ts` method returns an object of class 'ts'.

### Author(s)

P'eter S'olymos, solymos@ualberta.ca

For functions to create 'permat' objects: `permatfull`, `permatswap`

For time-series diagnostics: `Box.test`, `lag.plot`, `tsdiag`, `ar`, `arima`

### Examples

```data(BCI)
## Not sequential algorithm
a <- permatswap(BCI, "quasiswap")
## Sequential algorithm
b <- permatswap(BCI, "abuswap", fixedmar="col",
burnin=0, thin=100, times=50)
opar <- par(mfrow=c(2,2))
plot(a, main="Not sequential")
plot(b, main="Sequential")
plot(a, "chisq")
plot(b, "chisq")
par(opar)
## Extract Bray-Curtis dissimilarities
## as time series
bc <- as.ts(b)
## Lag plot
lag.plot(bc)
## First order autoregressive model
mar <- arima(bc, c(1,0,0))
mar
## Ljung-Box test of residuals
Box.test(mar\$residuals)
## Graphical diagnostics
tsdiag(mar)
```

[Package vegan version 1.16-32 Index]