Usage
mat.mc(inModern, modTaxa = c(NULL, NULL), probs = c(0.05, 0.025, 0.01, 0.001), freqint = seq(0, 2, 0.02), sampleSize = length(inModern[, 1]), method = "sawada", withReplace = T, counts = F)
Arguments
inModern
Modern Calibration Dataset: a file containing field names in the first row of the modern calibration dataset where each subsequent row containing a site/row identifier (Site ID), coordinates in either a planar/projected x,y system or as Longitude and Latitude in decimal degrees, and taxon counts followed by the modern environmental variables (Mod.Env 1,Mod.Env n) that will be used for modern training and/or paleoenvironmental reconstruction. The final and optional field would contain, for each row, a nominal code representing the biological zone to which each row/site belongs.
modTaxa
A vector containing the column numbers containing the modern taxon counts or proportions within inModern
probs
A vector of significance levels of 0.1, 0.05 etc. at which to return the dissimilarity values. The valid range is between 0 and 1 for this vector.
freqint
A vector or sequence that spans the range of the dissimiliarity value being analyzed, for example, for squared-chord distance which can range between 0 and 2, a valid sequence would be seq(0,2,0.02).
sampleSize
A single number that determins how many samples are compared for the Monte-Carlo simulation. Defaults to the number of samples in the input inModern dataset.
method
Either "sawada" or "allpairs" are implemented; "sawada" is the default method of comparison whereby pairwise comparisons are taken randomly with replacement from the inModern dataset sampleSize times. Alternatively, for all pairwise comparisons use "allpairs" as the argument value.
withReplace
A logical value specific to the "sawada" comparison method that defaults to TRUE and allows for random pairwise comparisons with replacement from the inModern dataset.
counts
A logical value that describes whether the taxon datset of inModern is in the form of raw counts or proportions. If this artument is FALSE then the comparison method assumes the dataset is proportions.