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': $$y=f+b\left|f\right|e$$ 'combined': $$y=f+(a+b\left|f\right|)e$$ 'exponential': $$y=f\ast \exp (ae)$$
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