Results object containing estimation results from nonlinear mixedeffects modeling
The NLMEResults
object contains estimation
results from fitting a nonlinear mixedeffects model using sbiofitmixed
.
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 mixedeffects estimation using sbiofitmixed 
fitted(NLMEResults)  Return the simulation results of a fitted nonlinear mixedeffects model 
plot(NLMEResults)  Compare simulation results to the training data, creating a timecourse 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 xaxis 
predict(NLMEResults)  Simulate and evaluate fitted SimBiology model 
random(NLMEResults)  Simulate a SimBiology model, adding variations by sampling the error model 
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: There are four builtin error models. Each model defines the error using a standard meanzero and unitvariance (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.

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 =
NP , 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);
sbiofit
 sbiofitmixed
 nlmefit
(Statistics and Machine Learning Toolbox)  nlmefitsa
(Statistics and Machine Learning Toolbox)