inla.group.cv {INLA} | R Documentation

## Compute group.cv-values

### Description

From a fitted model, compute and add the `group.cv`-values

### Usage

    inla.group.cv(
      result,
      group.cv = NULL,
      num.level.sets = -1,
      strategy = c("posterior", "prior"),
      size.max = 32,
      groups = NULL,
      selection = NULL,
      group.selection = NULL,
      friends = NULL,
      weights = NULL,
      verbose = FALSE,
      epsilon = 0.005,
      prior.diagonal = 1e-04,
      keep = NULL,
      remove = NULL,
      remove.fixed = TRUE
    )

### Arguments

`result` | An object of class `inla`, ie a result of a call to `inla()`.
`group.cv` | If given, the groups are taken from this argument. `group.cv` must be the output of previous call to `inla.group.cv()`.
`num.level.sets` | Number of level.sets to use. The default value `-1` corresponds to leave-one-out cross-validation. If argument `weights` is used, then this is threshold for the sum of the weights defining a group.
`strategy` | One of `"posterior"` or `"prior"`. See the vignette for details.
`size.max` | The maximum size (measure in the number of nodes) of a group. If the computed group-size is larger, it will be truncated to `size.max`. Note that: If `weights` are in use, then this still corresponds to the number of nodes in the group, and not the sum of the weights. This is ment as an emergency option to avoid the size of the group to go nuts.
`groups` | An (optional) predefined list of groups. See the vignette for details.
`selection` | An optional list of data-indices to use. If not given, then all data are used.
`group.selection` | An optional list of data-indices to use when building the groups. If given, each group beyond the observation itself, must be a subset of `group.selection`. If not given, then all data are used.
`friends` | An optional list of lists of indices to use a friends
`weights` | An optional positive weight attached to each datapoint. The sume of the weights define the size of the group. If `NULL`, then unit weight is used.
`verbose` | Run with `verbose` output of some of the internals in the calculations. This option will also enable `inla(..., verbose=TRUE)` if its not enabled already.
`epsilon` | Two correlations with a difference less than `epsilon`, will be classified as identical.
`prior.diagonal` | When `strategy="prior"`, `prior.diagonal` is added to the diagonal of the prior precision matrix to avoid singularities
`keep` | For `strategy="prior"`, then this gives a vector of the name of model-components TO USE when computing the groups. See the vignette for details. Not both of `keep` and `remove` can be defined.
`remove` | For `strategy="prior"`, then this gives a vector of the name of model-components NOT TO USE when computing the groups. See the vignette for details. Not both of `keep` and `remove` can be defined.
`remove.fixed` | For `strategy="prior"`, this is the default option which is in effect if both `keep` and `remove` are `NULL`. If `TRUE`, it will remove (or condition on) all fixed effects when computing the groups. See the vignette for details.

### Value

The object returned is list related to leave-group-out cross-validation. See the vignette for details.

### Author(s)

Havard Rue hrue@r-inla.org

### See Also

`control.compute()`

[Package INLA version 25.06.07 Index]