Description Usage Arguments Details Value See Also Examples

Fit a linear mixed-effects model (LMM) to simulated ratings data.

1 |

`.data` |
Data frame, with the format as resulting from a call
to |

`main_effect` |
Whether to test the main effect of repetition (TRUE) or the repetition-by-interval interaction (FALSE; the default). |

This function is used to estimate parameters for power
analysis with simulated data. `fit_lmem`

fits a linear-mixed
effects model to the data with `lmer`

and tests
the specified effect (interaction or main effect) using a
likelihood-ratio test. If the interaction is to be tested, the
following two models are compared.

```
trating ~ R * (I1 + I2 + I3) +
(1 + R:I1 + R:I2 + R:I3 || subj_id) +
(1 + R:I1 + R:I2 + R:I3 || stim_id)
```

```
trating ~ R + I1 + I2 + I3 +
(1 + R:I1 + R:I2 + R:I3 || subj_id) +
(1 + R:I1 + R:I2 + R:I3 || stim_id)
```

.

If the main effect is to be tested, then the following two models are compared.

```
trating ~ R * (I1 + I2 + I3) +
(1 + R || subj_id) +
(1 + R || stim_id)
```

```
trating ~ I1 + I2 + I3 + R:I1 + R:I2 + R:I3) +
(1 + R || subj_id) +
(1 + R || stim_id)
```

.

A vector, with the following elements.

`(Intercept)`

Fixed-effects estimate of the intercept.

`R`

Fixed-effects estimate of the main effect of repetition.

`I1`

Fixed-effects estimate of the main effect of interval (1).

`I2`

Fixed-effects estimate of the main effect of interval (2).

`I3`

Fixed-effects estimate of the main effect of interval (3).

`R:I1`

Fixed-effects estimate of the interaction (1).

`R:I2`

Fixed-effects estimate of the interaction (2).

`R:I3`

Fixed-effects estimate of the interaction (3).

- dev1
Deviance for the model including the effect(s) of interest.

- dev2
Deviance for the model excluding the effect(s) of interest.

- chisq_RI
Chi-square value for the likelihood ratio test.

- p_RI
Associated p-value.

- m1_singular
Whether the covariance matrix for model 1 was singular.

- m2_singular
Whether the covariance matrix for model 2 was singular.

- m1_conv
Whether model 1 converged.

- m2_conv
Whether model 2 converged.

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