To run a linear mixed effects model in MATLAB, you'll need the Statistics and Machine Learning Toolbox. This toolbox provides the fitlme function to fit linear mixed-effects models to your data. Here's a step-by-step guide on how to run a linear mixed effects model in MATLAB:

Import or create your data: Import your dataset into MATLAB, ensuring it is in a table format. You can convert your data to a table using the `array2table` or `cell2table` functions if your data is in another form.

```matlab
% Import data from a CSV file
data = readtable('your_data_file.csv');
```

Prepare your data: Ensure your data is organized with the predictor variables (fixed effects) and the grouping variable(s) (random effects) in separate columns. You might need to preprocess your data depending on your requirements.

Define your model: Specify your linear mixed effects model using Wilkinson notation. The fixed effects are represented by a formula including the predictor variables, and the random effects are specified using the `|` operator.

```matlab
% Define the fixed effects formula
fixedEffectsFormula = 'response ~ predictor1 + predictor2';

% Define the random effects structure
randomEffectsStructure = '(1 | groupingVariable)';
```

Combine the fixed and random effects into a single model formula:

```matlab
% Combine fixed and random effects into a single formula
modelFormula = [fixedEffectsFormula, ' + ', randomEffectsStructure];
```
Fit the linear mixed effects model to your data using the `fitlme` function:

```matlab
% Fit the linear mixed effects model
lme = fitlme(data, modelFormula);
```
Analyze the results: You can now analyze the results of your model using various methods and properties provided by the `LinearMixedModel` object. Some examples include:
- Display the model coefficients: `lme.Coefficients`
- Display the fixed effects: `lme.FixedEffects`
- Display the random effects: `lme.RandomEffects`
- Perform hypothesis tests: `anova(lme)`

For more information on the `fitlme` function and the `LinearMixedModel` object, you can refer to the MATLAB documentation:

- `fitlme`: [https://www.mathworks.com/help/stats/fitlme.html](https://www.mathworks.com/help/stats/fitlme.html)
- `LinearMixedModel`: [https://www.mathworks.com/help/stats/linearmixedmodel.html](https://www.mathworks.com/help/stats/linearmixedmodel.html)
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence