Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function back-fits an initial LMER model on *t*-values, and, if enabled, log-likelihood ratio testing. Note that, this function CAN be used with generalized linear mixed-effects models (`glmer`

s).

1 2 3 4 5 6 |

`model` |
A |

`item` |
Whether or not to evaluate the addition of by-item random intercepts to the model, evaluated by way of log-likelihood ratio test. Either |

`method` |
Backfitting method. One of "t" (lmer), "z" (glmer), "llrt", "AIC", "BIC", "relLik.AIC", or "relLik.BIC" (the latter two are based on relative likelihood, see function |

`threshold` |
Method-specific threshold for parameter selection. It refers to the minimum |

`t.threshold` |
Defaults to |

`alphaitem` |
Alpha value for the evaluation of by-item random intercepts. Defaults to |

`prune.ranefs` |
Logical. Whether to remove any random effect for which its variable is not also present in the fixed effects structure (with the exception of the grouping variables such as |

`set.REML.FALSE` |
Logical. Whether or not to set REML to |

`reset.REML.TRUE` |
Logical. Whether or not to re-set the back-fitted model to |

`keep.single.factors` |
Logical. Whether or not main effects are kept (not subjected to testing and reduction). Defaults to |

`log.file` |
Whether a back-fitting log should be saved. Defaults to |

The back-fitting process works as follows:

If argument

`method`

is not set to "t",`REML`

is set to`FALSE`

;First consider only highest-order interaction model terms:

If

`method`

is "t" or "z", the model term with the lowest*t*/*z*-value is identified. If this*t*/*z*-value is smaller than`threshold`

, the model term is removed and a new model is fitted. This is repeated for each model term for term that has a*t*-value smaller than the threshold value. The algorithm then moves on to step (b). If`method`

is not "t" or "z", the model term with the lowest*t*/*z*-value-value is identified and the following is evaluated:A new model without this model term is fitted;

The more complex and simpler models are compared by way of a log-likelihood ratio test in case

`method`

is "llrt", by way of AIC or BIC comparison if`method`

is "AIC" "BIC", or by calculating the`relLik`

based on AIC or BIC in case`method`

is "relLik.AIC" or "relLik.BIC". If the result determines that the term under consideration does not increase model fit, it is removed; otherwise it is kept.Move on to the next model term with the smallest

*t*/*z*-value smaller than`threshold`

and repeat steps (i)–(iii).

Once all highest-order interaction terms have been evaluated, go down to the second highest order interactions: Repeat steps (ai)–(aiii) with the following addition: If a term would be removed from the model, but it is part of a high-order interaction, keep it. Once all terms of the interaction level have been evaluated, move down to the next lower-order level until main effects have been evaluated, after which the process stops. If

`keep.single factors = TRUE`

, the process stops after the evaluation of all interaction terms.

If argument

`method`

is set to something other than`t`

or`z`

, set`reset.REML.TRUE`

to`TRUE`

(default) unless otherwise specified.

In brief, if `method`

is set to "t" or "z", a term remains in the model if its *t*/*z*-value is equal to or greater than `threshold`

; if `method`

is set to something else, a term remains in the model if

its

*t*/*z*-value is equal to or greater than`threshold`

;it significantly increases model fit as determined by the specified method;

it is part of a significant interaction term.

This backfitting method was used in Tremblay & Tucker (2011). If factorial terms with more than two levels are included in the initial model, back-fitting on F is recommended.

A `mer`

model with back-fitted fixed effects (on `t`

-values) is returned and a log of the back-fitting process is printed on screen and (by default) in a log file.

If you get this error:

1 2 |

It is probably because you updated the model using function `update`

and the data now appears as `data = ..2`

or something similar to this. You can check this by typing `model@call`

. If this is the case, re-fit your model as `lmer(DV ~ IV + IV + (RANEF), data = dat)`

.

Antoine Tremblay, Statistics Canada, trea26@gmail.com and Johannes Ransijn johannesransijn@gmail.com.

Tremblay, A. and Tucker B. V. (2011). The Effects of N-gram Probabilistic Measures on the Processing and Production of Four-word Sequences. *The Mental Lexicon*, *6(2)*, 302–324.

`bfFixefLMER_F.fnc; `

`ffRanefLMER.fnc; `

`fitLMER.fnc; `

`mcposthoc.fnc; `

`pamer.fnc; `

`mcp.fnc; `

`relLik; `

`romr.fnc; `

`perSubjectTrim.fnc. `

1 | ```
# see example in LMERConvenienceFunctions help page.
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.