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)