Usage
mat.roc.allpair(inModern, modTaxa = c(), colClasses = NULL, numAnalogs = 2, rocEvalSeq = seq(0, 2, 0.05), counts = F, aucmethod = "trap")
Arguments
inModern
Dataframe: 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 (Sample ID), coordinates in either a planar/projected x,y system or as Longitude and Latitude in decimal degrees, dd, 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
Vector: With two items, the number of the column containing the first taxon for analysis for each sample and the number of the column containing the last taxon for each sample within the inModern dataset.
colClasses
Numeric: The column of inModern that has the zonal affiliations for each sample.
numAnalogs
Numeric: a single number > 1 that specifies the number of modern analogs to use in the reconstruction.
rocEvalSeq
Vector: a numeric vector specifying the sequence over which the ROC analyses will be done. Generally, this sequence will range from the minimum value of the dissimilarity index to the maximum value of the dissimilarity index divided by some interval. For example, the default is set for squared-chord distance as rocEvalSeq=seq(0,2,0.05). This should be changed for other implementations of dissimilarity metrics.
counts
Logical: True (default) then the program assumes that your inFossil AND inModern datasets are taxon counts and so will automatically convert them to proportions.
aucmethod
Character: either "trap" for the trapezoidal integration or "wilcox" for the Mann-Whitney-Wilcoxon statistic.