Wolfram Language Paclet Repository
Community-contributed installable additions to the Wolfram Language
MarcoThiel`MMAMixedEffects`
MixedEffectsModel  |
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represents a fitted mixed-effects model.
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MixedEffectsModel objects are returned by all three fitting functions and are not normally constructed directly.
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The compact summary box reports model class, approximation, observation count, grouping count, convergence, and singularity.
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IncludeRandomEffects controls conditional versus population prediction; NewGroupAction controls unseen levels.
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Property lists are stable Associations for one or many grouping structures; no output changes type solely because a second factor is added.
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Properties returns the complete supported property list.
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The following options can be given:
IncludeRandomEffects | True | Include known-level conditional random effects in prediction. | |
NewGroupAction | "Population" | Use zero random effect for an unseen level or use "Fail". |
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The fitted object supports the following property groups:
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Data and design
"Data" | Rows used for fitting after missing-data processing. | |
"Variables" | Ordered predictor and grouping variables. | |
"IncludedRowIndices" | Original row indices retained for fitting. | |
"ExcludedRowIndices" | Original row indices excluded from fitting. | |
"FixedEffectsDesignMatrix" | Fixed-effect design matrix. | |
"RandomEffectsDesignMatrix" | Sparse random-effect design matrix. |
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Fixed effects
"FixedEffects" | Association of fixed-effect estimates. | |
"ParameterTableEntries" | Machine-readable parameter inference entries. | |
"FixedEffectCovariance" | Estimated covariance matrix of fixed effects. | |
"FixedEffectCorrelationMatrix" | Corresponding correlation matrix. |
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Random and residual effects
"RandomEffects" | Conditional modes grouped by random-effect structure. | |
"RandomEffectCovariances" | Association of estimated covariance matrices. | |
"ResidualVariance" | Gaussian residual variance, when applicable. | |
"ResidualParameterEstimates" | Estimated residual correlation and variance-function parameters. |
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Predictions and residuals
"PopulationPredictedResponse" | Training predictions with random effects set to zero. | |
"ConditionalPredictedResponse" | Training predictions using conditional random effects. | |
"MarginalPredictedResponse" | Response-scale predictions integrated over random effects. | |
"Residuals" | Conditional response residuals. | |
"PearsonResiduals" | Variance-standardized residuals. | |
"DevianceResiduals" | Signed deviance residuals when defined. |
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Likelihood and diagnostics
"LogLikelihood" | Constants-inclusive maximized or approximated log likelihood. | |
"AIC" | Akaike information criterion. | |
"BIC" | Bayesian information criterion. | |
"ConvergenceReport" | Gradient, error, optimizer, mode, and tolerance diagnostics. | |
"Converged" | True only when documented numerical gates pass. | |
"SingularFit" | Whether a random-effect covariance lies on a rank boundary. | |
"Warnings" | Statistical and numerical warnings retained with the fit. |
Examples
(4)
Basic Examples
(1)
Extract several properties at once as an Association:
In[1]:=
fit[{"FixedEffects","AIC","Converged","Warnings"}]
Out[1]=
FixedEffects1251.405,days10.4673,AIC1755.63,ConvergedTrue,Warnings{}
Distinguish population, known-group conditional, and marginal predictions:
In[1]:=
Plot[Evaluate[predictionFunctions],{time,0,6},PlotLegends{"population","conditional group","marginal"}]
Out[1]=
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Scope
(1)
Properties & Relations
(1)
Possible Issues
(1)
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