infoCriteria {asremlPlus} | R Documentation |

Computes Akiake and Bayesian (Schwarz) Information Criteria for models.
Either the Restricted Maximum likelihood (`REML`

) or the full likelihood
(`full`

) can be used. The full likelihood is used when it is desired to
compare models that differ in their fixed models.

## S3 method for class 'asreml' infoCriteria(object, DF = NULL, bound.exclusions = c("F","B","S","C"), IClikelihood = "REML", fixedDF = NULL, varDF = NULL, ...) ## S3 method for class 'list' infoCriteria(object, bound.exclusions = c("F","B","S","C"), IClikelihood = "REML", fixedDF = NULL, varDF = NULL, ...)

`object` |
An |

`DF` |
A |

`bound.exclusions` |
A |

`IClikelihood` |
A |

`fixedDF` |
A |

`varDF` |
A |

`...` |
Provision for passsing arguments to functions called internally - not used at present. |

The variance degrees of freedom (varDF) are the number of number of variance parameters that
have been estimated, excluding those whose estimates have a code for `bound`

specified in `bound.exclusions`

. If `varDF`

is not `NULL`

, the supplied value
is used. Otherwise `varDF`

is determined from the information in `object`

,
i.e. if `object`

is an `asreml`

object then from it, or if `object`

is a
`list`

then from each `asreml`

object in the `list`

.
Similarly, the fixed degrees of freedom (fixedDF) are the number of number of fixed parameters
that have been estimated, any coefficients that have the value `NA`

being excluded.
If `fixedDF`

is not `NULL`

, the supplied value is used. Otherwise `fixedDF`

is determined from the information in `object`

.

If ASReml-R version 4 is being used then the codes specified in `bound.exclusions`

are
not restricted to a subset of the default codes, but a warning is issued if a code other
than these is specified.
For ASReml-R version 3, only a subset of the default codes are allowed:
`F`

(`Fixed`

), `B`

(`Boundary`

), `C`

(`Constrained`

) and
`S`

(`Singular`

).

The calculation of the information criteria is an adaption of the code supplied in File S1
of Verbyla (2019). The log-likeliood is calculated as
`loglik = log(REML) - log(|C|)/2`

,
where C is the inverse coefficient matrix; the term involving **C** is omitted for `REML`

.
The AIC is calculated as `- 2 * loglik + 2 * (varDF + fixedDF)`

and the BIC as `- 2 * loglik + (fixedDF + varDF) * log(n - r + fixedDF)`

,
where `n`

is the number of observations and `r`

is the rank of the fixed effects
design matrix. For `REML`

, `fixedDF = 0`

.

A `data.frame`

containing the numbers of estimated fixed (fixedDF) and variance (varDF) parameters, the
number of bound parameters (NBound), AIC, BIC and the value of the log-likelihood (loglik). If `object`

is a `list`

and its components are named, then those names will be used to set the
`rownames`

of the `data.frame`

.

Chris Brien

Verbyla, A. P. (2019). A note on model selection using information criteria for general
linear models estimated using REML. *Australian \& New Zealand Journal of Statistics*,
**61**, 39–50. doi: 10.1111/anzs.12254.

`REMLRT.asreml`

, `changeTerms.asrtests`

, `changeModelOnIC.asrtests`

## Not run: data(Wheat.dat) ## Fit several models to the wheat data and caclulate their ICs # Fit initial model m.max <- asreml(yield ~ Rep + WithinColPairs + Variety, random = ~ Row + Column + units, residual = ~ ar1(Row):ar1(Column), data=Wheat.dat) infoCriteria(m.max.asr, IClikelihood = "full") #Drop term for within Column pairs m1 <- asreml(yield ~ Rep + Variety, random = ~ Row + Column + units, residual = ~ ar1(Row):ar1(Column), data=Wheat.dat) #Drop nugget term m2 <- asreml(yield ~ Rep + WithinColPairs + Variety, random = ~ Row + Column, residual = ~ ar1(Row):ar1(Column), data=Wheat.dat) #Drop Row autocorrelation m3 <- asreml(yield ~ Rep + WithinColPairs + Variety, random = ~ Row + Column + units, residual = ~ Row:ar1(Column), data=Wheat.dat) #Drop Col autocorrelation m4 <- asreml(yield ~ Rep + WithinColPairs + Variety, random = ~ Row + Column + units, residual = ~ ar1(Row):Column, data=Wheat.dat) mods.asr <- list(m.max, m1, m2, m3, m4) infoCriteria(mods.asr, IClikelihood = "full") ## End(Not run)

[Package *asremlPlus* version 4.2-32 Index]