# NLMEResults object

Results object containing estimation results from nonlinear mixed-effects modeling

## Description

The `NLMEResults` object contains estimation results from fitting a nonlinear mixed-effects model using `sbiofitmixed`.

## Method Summary

 boxplot(NLMEResults) Create box plot showing the variation of estimated SimBiology model parameters covariateModel(NLMEResults) Return a copy of the covariate model that was used for the nonlinear mixed-effects estimation using `sbiofitmixed` fitted(NLMEResults) Return the simulation results of a fitted nonlinear mixed-effects model plot(NLMEResults) Compare simulation results to the training data, creating a time-course subplot for each group plotActualVersusPredicted(NLMEResults) Compare predictions to actual data, creating a subplot for each response plotResidualDistribution(NLMEResults) Plot the distribution of the residuals plotResiduals(NLMEResults) Plot the residuals for each response, using the time, group, or prediction as the x-axis predict(NLMEResults) Simulate and evaluate fitted SimBiology model random(NLMEResults) Simulate a SimBiology model, adding variations by sampling the error model

## Properties

 `FixedEffects` Table of the estimated fixed effects and their standard errors. `RandomEffects` Table of the estimated random effects for each group. `IndividualParameterEstimates` Table of estimated parameter values, including fixed and random effects. `PopulationParameterEstimates` Table of estimated parameter values, including only fixed effects. `RandomEffectCovarianceMatrix` Table of the covariance matrix of the random effects. `stats` Struct of statistics returned by the `nlmefit` (Statistics and Machine Learning Toolbox) and `nlmefitsa` (Statistics and Machine Learning Toolbox) algorithm. `CovariateNames` Cell array of character vectors specifying covariate names. `EstimatedParameterNames` Cell array of character vectors specifying estimated parameter names. `ErrorModelInfo` Table describing the error models and estimated error model parameters.The table has one row with three variables: `ErrorModel`, `a`, and `b`. The `ErrorModel` variable is categorical. The variables `a` and `b` can be `NaN` when they do not apply to a particular error model.There are four built-in error models. Each model defines the error using a standard mean-zero and unit-variance (Gaussian) variable e, the function value f, and one or two parameters a and b. In SimBiology, the function f represents simulation results from a SimBiology model. `'constant'`: $y=f+ae$`'proportional'`: $y=f+b|f|e$`'combined'`: $y=f+\left(a+b|f|\right)e$`'exponential'`: $y=f\ast \mathrm{exp}\left(ae\right)$ `EstimationFunction` Name of the estimation function which must be either `'nlmefit'` or `'nlmefitsa'`. `LogLikelihood` Maximized loglikelihood for the fitted model. `AIC` Akaike Information Criterion (AIC), calculated as ```AIC = 2*(-LogLikelihood + P)```, where P is the number of parameters. For details, see `nlmefit` (Statistics and Machine Learning Toolbox). `BIC` Bayes Information Criterion (BIC), calculated as ```BIC = -2*LogLikelihood + P*log(N)```, where N is the number of observations or groups, and P is the number of parameters. For details, see `nlmefit` (Statistics and Machine Learning Toolbox). `DFE` Degrees of freedom for error, calculated as ```DFE = N-P```, where N is the number of observations and P is the number of parameters.

Note

If you are using the `nlmefitsa` method, `Loglikelihood`, `AIC`, and `BIC` properties are empty by default. To calculate these values, specify the `'LogLikMethod'` option of `nlmefitsa` (Statistics and Machine Learning Toolbox) when you run `sbiofitmixed` as follows.

```opt.LogLikMethod = 'is'; fitResults = sbiofitmixed(...,'nlmefitsa',opt);```

## Version History

Introduced in R2014a