covar.utilities {dispRity}

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)