mpd.query {PhyloMeasures} | R Documentation |
Calculates the Mean Pairwise Distance (MPD) measure for sets of tips on a phylogeny. The same function can also calculate the standardized value of this measure under three different null models which maintain species richness (this is equal to the Net Relatedness Index, NRI).
mpd.query(tree, matrix, standardize = FALSE, null.model="uniform", abundance.weights, reps=1000, seed)
tree |
A phylo tree object |
matrix |
A matrix with binary (0/1) values, where each row represents a tip set. Each column name in the matrix must match a tip label on the input tree. If not all values in the matrix are binary, we consider two cases; if the matrix contains only non-negative values, all values are coerced to binary ones and a warning message is printed. If the matrix contains at least one negative value, the function throws an error. |
standardize |
Specifies whether the function should standardize the MPD for variation in species richness. For each tip set S, the observed MPD is standardized by subtracting the mean MPD and dividing by the standard deviation of this measure. The mean and standard deviation are calculated among all tip sets that have the same number of elements as set S, the tip set whose value we want to standardize (default = FALSE). |
null.model |
A character vector (string) that defines which null model is used for computing the standardized values of the measure. There are three possible null models that can be used for computing the standardized values: these are "uniform", "frequency.by.richness", and "sequential". All these models maintain species richness. More specifically, the available models are defined as follows:
This argument is optional, and its default value is set to "uniform". |
abundance.weights |
A vector of positive numeric values. These are the abundance weights that will be used if either of the options "frequency.by.richness" or "sequential" are selected. The names stored at the vector must match the names of the tips in the tree. This argument is redundant if the "uniform" model is selected. |
reps |
An integer that defines the number of Monte-Carlo random repetitions that will be performed when using the "sequential" model. This argument is redundant if any of the other two null models is selected. |
seed |
A positive integer that defines the random seed used in the Monte-Carlo randomizations of the "sequential" model. This argument is optional, and becomes redundant if any of the other two null models is selected. |
A vector which stores the computed (standardized) MPD values. The i-th entry in this vector stores the (standardized) MPD value of the i-th row in the input matrix.
Constantinos Tsirogiannis (tsirogiannis.c@gmail.com)
Tsirogiannis, C. and B. Sandel. 2015. PhyloMeasures: A package for computing phylogenetic biodiversity measures and their statistical moments. Ecography, doi: 10.1111/ecog.01814, 2015.
Tsirogiannis, C., B. Sandel and D. Cheliotis. 2012. Efficient computation of popular phylogenetic tree measures. Algorithms in Bioinformatics, LNCS 7534: 30-43.
Webb, C.O. 2000. Exploring the phylogenetic structure of ecological communities: An example for rain forest trees. The American Naturalist 156: 145-155.
#Load phylogenetic tree of bird families from package "ape" data(bird.families, package = "ape") #Create 100 random communities with 50 families each comm = matrix(0,nrow = 100,ncol = length(bird.families$tip.label)) for(i in 1:nrow(comm)) {comm[i,sample(1:ncol(comm),50)] = 1} colnames(comm) = bird.families$tip.label #Calculate mpd values for each community mpd.query(bird.families,comm) #Calculate standardized versions under the uniform model mpd.query(bird.families,comm,TRUE) # Create random abundance weights weights = runif(length(bird.families$tip.label)) names(weights) = bird.families$tip.label #Use query function to calculate standardized versions under the sequential model mpd.query(bird.families,comm,TRUE,null.model="sequential", abundance.weights=weights, reps=1000)