plot.mcarlo {analogue} | R Documentation |

A `plot.lm`

-like plotting function for objects of class
`"mcarlo"`

to visualise the simulated distribution of
dissimilarities.

```
## 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(),
...)
```

`x` |
an object of class |

`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. |

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`

.

One or more plots on the current device.

Gavin L. Simpson

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.

```
## Imbrie and Kipp example
## load the example data
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[[1]] / 100
V12.122 <- dat[[2]] / 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]