When a model is fitted using Markov chain Monte Carlo (MCMC) methods,
its reference grid contains a post.beta slot. These functions
transform those posterior samples to posterior samples of EMMs or
related contrasts. They can then be summarized or plotted using,
e.g., functions in the coda package.
# S3 method for emmGrid
as.mcmc(x, names = TRUE, sep.chains = TRUE, likelihood,
NE.include = FALSE, ...)# S3 method for emm_list
as.mcmc(x, which = 1, ...)
# S3 method for emmGrid
as.mcmc.list(x, names = TRUE, ...)
# S3 method for emm_list
as.mcmc.list(x, which = 1, ...)
An object of class mcmc or mcmc.list.
An object of class emmGrid
Logical scalar or vector specifying whether variable names are
appended to levels in the column labels for the as.mcmc or
as.mcmc.list result -- e.g., column names of treat A and
treat B versus just A and B. When there is more than
one variable involved, the elements of names are used cyclically.
Logical value. If TRUE, and there is more than one
MCMC chain available, an mcmc.list object is returned
by as.mcmc, with separate EMMs posteriors in each chain.
Character value or function. If given, simulations are made from
the corresponding posterior predictive distribution. If not given, we obtain
the posterior distribution of the parameters in object. See Prediction
section below.
Logical value. If TRUE, non-estimable columns are
kept but returned as columns of NA values (this may create errors or
warnings in subsequent analyses using, say, coda). If FALSE,
non-estimable columns are dropped, and a warning is issued. (If all are
non-estimable, an error is thrown.)
arguments passed to other methods
item in the emm_list to use
When likelihood is specified, it is used to simulate values from the
posterior predictive distribution corresponding to the given likelihood and
the posterior distribution of parameter values. Denote the likelihood
function as \(f(y|\theta,\phi)\), where \(y\) is a response, \(\theta\)
is the parameter estimated in object, and \(\phi\) comprises zero or
more additional parameters to be specified. If likelihood is a
function, that function should take as its first argument a vector of
\(\theta\) values (each corresponding to one row of object@grid).
Any \(\phi\) values should be specified as additional named function
arguments, and passed to likelihood via .... This function should
simulate values of \(y\).
A few standard likelihoods are available by specifying likelihood as
a character value. They are:
"normal"The normal distribution with mean \(\theta\) and
standard deviation specified by additional argument sigma
"binomial"The binomial distribution with success probability
\(theta\), and number of trials specified by trials
"poisson"The Poisson distribution with mean \(theta\) (no additional parameters)
"gamma"The gamma distribution with scale parameter \(\theta\)
and shape parameter specified by shape
When the object's post.beta slot is non-trivial, as.mcmc will
return an mcmc or mcmc.list object
that can be summarized or plotted using methods in the coda package.
In these functions, post.beta is transformed by post-multiplying it by
t(linfct), creating a sample from the posterior distribution of LS
means. In as.mcmc, if sep.chains is TRUE and there is in
fact more than one chain, an mcmc.list is returned with each chain's
results. The as.mcmc.list method is guaranteed to return an
mcmc.list, even if it comprises just one chain.
if(requireNamespace("coda"))
emm_example("as.mcmc-coda")
# Use emm_example("as.mcmc-coda", list = TRUE) # to see just the code
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