vegan (version 2.6-8)

avgdist: Averaged Subsampled Dissimilarity Matrices

Description

The function computes the dissimilarity matrix of a dataset multiple times using vegdist while randomly subsampling the dataset each time. All of the subsampled iterations are then averaged (mean) to provide a distance matrix that represents the average of multiple subsampling iterations. This emulates the behavior of the distance matrix calculator within the Mothur microbial ecology toolkit.

Usage

avgdist(x, sample, distfun = vegdist, meanfun = mean,
    transf = NULL, iterations = 100, dmethod = "bray",
    diag = TRUE, upper = TRUE, ...)

Arguments

x

Community data matrix.

sample

The subsampling depth to be used in each iteration. Samples that do not meet this threshold will be removed from the analysis, and their identity returned to the user in stdout.

distfun

The dissimilarity matrix function to be used. Default is the vegan vegdist

meanfun

The calculation to use for the average (mean or median).

transf

Option for transforming the count data before calculating the distance matrix. Any base transformation option can be used (e.g. sqrt)

iterations

The number of random iterations to perform before averaging. Default is 100 iterations.

dmethod

Dissimilarity index to be used with the specified dissimilarity matrix function. Default is Bray-Curtis

diag, upper

Return dissimilarities with diagonal and upper triangle. NB. the default differs from vegdist and returns symmetric "dist" structure instead of lower diagonal. However, the object cannot be accessed with matrix indices unless cast to matrix with as.matrix.

...

Any additional arguments to add to the distance function or mean/median function specified.

Author

Geoffrey Hannigan, with some minor tweaks by Gavin L. Simpson.

See Also

This function utilizes the vegdist and rrarefy functions.

Examples

# Import an example count dataset
data(BCI)
# Test the base functionality
mean.avg.dist <- avgdist(BCI, sample = 50, iterations = 10)
# Test the transformation function
mean.avg.dist.t <- avgdist(BCI, sample = 50, iterations = 10, transf = sqrt)
# Test the median functionality
median.avg.dist <- avgdist(BCI, sample = 50, iterations = 10, meanfun = median)
# Print the resulting tables
head(as.matrix(mean.avg.dist))
head(as.matrix(mean.avg.dist.t))
head(as.matrix(median.avg.dist))
# Run example to illustrate low variance of mean, median, and stdev results
# Mean and median std dev are around 0.05
sdd <- avgdist(BCI, sample = 50, iterations = 100, meanfun = sd)
summary(mean.avg.dist)
summary(median.avg.dist)
summary(sdd)
# Test for when subsampling depth excludes some samples
# Return samples that are removed for not meeting depth filter
depth.avg.dist <- avgdist(BCI, sample = 450, iterations = 10)
# Print the result
depth.avg.dist