Description Usage Arguments Details Value Author(s) Examples

The ranef function extracts the conditional modes of the random effects from a clmm object. That is, the modes of the distributions for the random effects given the observed data and estimated model parameters. In a Bayesian language they are posterior modes.

The conditional variances are computed from the second order derivatives of the conditional distribution of the random effects. Note that these variances are computed at a fixed value of the model parameters and thus do not take the uncertainty of the latter into account.

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`object` |
a |

`condVar` |
an optional logical argument indicating of conditional variances should be added as attributes to the conditional modes. |

`...` |
currently not used by the |

The `ranef`

method returns a list of `data.frame`

s; one for
each distinct grouping factor. Each `data.frame`

has as many rows
as there are levels for that grouping factor and as many columns as
there are random effects for each level. For example a model can
contain a random intercept (one column) or a random
intercept and a random slope (two columns) for the same grouping
factor.

If conditional variances are requested, they are returned in the same structure as the conditional modes (random effect estimates/predictions).

The `ranef`

method returns a list of `data.frame`

s with the
random effects predictions/estimates computed as conditional
modes. If `condVar = TRUE`

a `data.frame`

with the
conditional variances is stored as an attribute on each
`data.frame`

with conditional modes.

The `condVar`

method returns a list of `data.frame`

s with
the conditional variances. It is a convenience function that simply
computes the conditional modes and variances, then extracts and
returns only the latter.

Rune Haubo B Christensen

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