Description Usage Arguments Value Details Prediction Examples

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.

1 2 3 4 5 6 |

`x` |
An object of class |

`names` |
Logical scalar or vector specifying whether variable names are
appended to levels in the column labels for the |

`sep.chains` |
Logical value. If |

`likelihood` |
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 |

`NE.include` |
Logical value. If |

`...` |
arguments passed to other methods |

An object of class `mcmc`

or `mcmc.list`

.

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.

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|θ,φ)*, where *y* is a response, *θ*
is the parameter estimated in `object`

, and *φ* 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
*θ* values (each corresponding to one row of `object@grid`

).
Any *φ* 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

*θ*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

*θ*and shape parameter specified by`shape`

1 2 3 4 5 6 7 8 | ```
if(requireNamespace("coda")) {
### A saved reference grid for a mixed logistic model (see lme4::cbpp)
cbpp.rg <- do.call(emmobj,
readRDS(system.file("extdata", "cbpplist", package = "emmeans")))
# Predictive distribution for herds of size 20
# (perhaps a bias adjustment should be applied; see "sophisticated" vignette)
pred.incidence <- coda::as.mcmc(regrid(cbpp.rg), likelihood = "binomial", trials = 20)
}
``` |

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