covar.utilities: Utilities for a dispRity object with covariance matrices

In dispRity: Measuring Disparity

Utilities for a dispRity object with covariance matrices

Description

Different utility functions to extract aspects of a MCMCglmm object.

Usage

get.covar(data, subsets, sample, n, dimensions)

axis.covar(data, subsets, sample, n, dimensions, level = 0.95, axis = 1)

Arguments

data	a dispRity object with a covar element.
subsets	optional, a numeric or character for which subsets to get (if missing, the value for all subsets are given).
sample	optional, one or more specific posterior sample IDs (is ignored if n is used) or a function to summarise all axes.
n	optional, a random number of covariance matrices to sample (if left empty, all are used).
dimensions	optional, which dimensions to use. If missing the dimensions from data are used.
level	which confidence interval level to use (default is 0.95).
axis	which major axis to calculate (default is 1, the first one).

Author(s)

Thomas Guillerme

See Also

MCMCglmm.subsets

Examples

## Load the Charadriiformes dataset
data(charadriiformes)
## Making a dispRity object with covar data
covar_data <- MCMCglmm.subsets(data       = charadriiformes$data,
                               posteriors = charadriiformes$posteriors)

## Get the two first covar matrices for each level
get.covar(covar_data, sample = c(1,2))
## Get 2 random covar matrices in 2D for each level
get.covar(covar_data, n = 2, dimensions = c(1,2))
## Get mean covar matrix for each level
get.covar(covar_data, sample = mean)

## Get the 0.95 major axis for the 42th covar matrix
axis.covar(covar_data, sample = 42)
## Get the 0.5 major axis for 2 random samples
axis.covar(covar_data, n = 1, level = 0.5)
## Get the median 0.95 minor axis of the 2D ellipse
axis.covar(covar_data, sample = mean, dimensions = c(1,2), axis = 2)

dispRity documentation built on Aug. 9, 2022, 5:11 p.m.