plot.mcarlo {analogue} R Documentation

## Plot Monte Carlo simulated dissimilarity distributions

### Description

A plot.lm-like plotting function for objects of class "mcarlo" to visualise the simulated distribution of dissimilarities.

### Usage

## S3 method for class 'mcarlo'
plot(x,
which = c(1:2),
alpha = 0.05,
caption = c("Distribution of dissimilarities",
expression(paste("Simulated probability Pr (Dissim < ",
alpha, ")"))),
col.poly = "lightgrey",
border.poly = "lightgrey",
ask = prod(par("mfcol")) < length(which) &&
dev.interactive(),
...)


### Arguments

 x an object of class "mcarlo", usually the result of a call to mcarlo. which numeric; which of the plots should be produced? alpha numeric; the Monte Carlo significance level to be marked on the cumulative frequency plot. caption character, length 2; captions to appear above the plots. col.poly, border.poly character; the colour to draw the region and border of the polygon enclosing the Monte Carlo significance on the cummulative frequency plot. ask logical; should the function wait for user confirmation to draw each plot? If missing, the function makes a reasonable attempt to guess the current situation and act accordingly. ... additional graphical parameters to be passed to the plotting functions. Currently ignored.

### Details

The "Distribution of dissimilarities" plot produces a histogram and kernel density estimate of the distribution of simulated dissimilarity values.

The "Simulated probability" plot shows a cumulative probability function of the simulated dissimlarity values, and highlights the proportion of the curve that is less than alpha.

### Value

One or more plots on the current device.

Gavin L. Simpson

### References

Sawada, M., Viau, A.E., Vettoretti, G., Peltier, W.R. and Gajewski, K. (2004) Comparison of North-American pollen-based temperature and global lake-status with CCCma AGCM2 output at 6 ka. Quaternary Science Reviews 23, 87–108.

mcarlo

### Examples

## Imbrie and Kipp example
data(ImbrieKipp)
data(SumSST)
data(V12.122)

## merge training and test set on columns
dat <- join(ImbrieKipp, V12.122, verbose = TRUE)

## extract the merged data sets and convert to proportions
ImbrieKipp <- dat[] / 100
V12.122 <- dat[] / 100

## perform the modified method of Sawada (2004) - paired sampling,
## with replacement
ik.mcarlo <- mcarlo(ImbrieKipp, method = "chord", nsamp = 1000,
type = "paired", replace = FALSE)
ik.mcarlo

## plot the simulated distribution
layout(matrix(1:2, ncol = 1))
plot(ik.mcarlo)
layout(1)


[Package analogue version 0.17-6 Index]