MCMCglmm.utilities: MCMCglmm object utility functions

In dispRity: Measuring Disparity

MCMCglmm object utility functions

Description

Different utility functions to extract aspects of a MCMCglmm object.

Usage

MCMCglmm.traits(MCMCglmm)

MCMCglmm.levels(MCMCglmm, convert)

MCMCglmm.sample(MCMCglmm, n)

MCMCglmm.covars(MCMCglmm, n, sample)

Arguments

MCMCglmm	A MCMCglmm object.
convert	Logical, whether to return the raw term names names as expressed in the model column names (FALSE) or to convert it to something more reader friendly (TRUE; default).
n	Optional, a number of random samples to extract.
sample	Optional, the specific samples to extract (is ignored if n is present).

Details

• MCMCglmm.levels returns the different random and residual terms levels of a MCMCglmm object. This function uses the default option convert = TRUE to convert the names into something more readable. Toggle to convert = FALSE for the raw names.

• MCMCglmm.traits returns the column names of the different traits of a MCMCglmm formula object.

• MCMCglmm.sample returns a vector of sample IDs present in the MCMCglmm object. If n is missing, all the samples IDs are returned. Else, a random series of sample IDs are returned (with replacement if n greater than the number of available samples).

• MCMCglmm.covars returns a list of covariance matrices and intercepts from a MCMCglmm object (respectively from MCMCglmm$VCV and MCMCglmm$Sol). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments sample to return specific samples (e.g. MCMCglmm.covars(data, sample = c(1, 42)) for returning the first and 42nd samples) or n to return a specific number of random samples (e.g. MCMCglmm.covars(data, n = 42) for returning 42 random samples).

Author(s)

Thomas Guillerme

See Also

MCMCglmm.subsets

Examples

## Loading the charadriiformes model
data(charadriiformes)
model <- charadriiformes$posteriors
class(model) # is MCMCglmm

## Get the list of levels from the model
MCMCglmm.levels(model)
## The raw levels names (as they appear in the MCMCglmm object)
MCMCglmm.levels(model, convert = FALSE)

## Get the traits names from the model
MCMCglmm.traits(model)

## Get all the available samples in the model
length(MCMCglmm.sample(model))
## Get 5 random sample IDs from the model
MCMCglmm.sample(model, n = 5)

## Get one specific samples from the model
MCMCglmm.covars(model, sample = 42)
## Get two random samples from the model
MCMCglmm.covars(model, n = 2)

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