dispRity (version 1.8)

MCMCglmm.utilities: 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)
MCMCglmm.variance(MCMCglmm, n, sample, levels, scale)

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).

levels

Optional, a vector "character" values (matching MCMCglmm.levels(..., convert = TRUE)) or of "numeric" values designating which levels to be used to calculate the variance (if left empty, all the levels are used).

scale

Logical, whether to scale the variance relative to all the levels (TRUE; default) or not (FALSE)/

Author

Thomas Guillerme

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).

• MCMCglmm.variance 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).

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)