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Fit nonlinear mixed-effects model (requires Statistics and Machine Learning Toolbox software)

`fitResults = sbiofitmixed(sm,grpData,responseMap,covEstiminfo)`

`fitResults = sbiofitmixed(sm,grpData,responseMap,covEstiminfo,dosing)`

`fitResults = sbiofitmixed(sm,grpData,responseMap,covEstiminfo,dosing,functionName)`

`fitResults = sbiofitmixed(sm,grpData,responseMap,covEstiminfo,dosing,functionName,opt)`

`fitResults = sbiofitmixed(sm,grpData,responseMap,covEstiminfo,dosing,functionName,opt,variants)`

`fitResults = sbiofitmixed(_,'UseParallel',tf_parallel)`

`fitResults = sbiofitmixed(_,'ProgressPlot',tf_progress)`

```
[fitResults,simDataI,simDataP]
= sbiofitmixed(_)
```

performs
nonlinear mixed-effects estimation using the SimBiology`fitResults`

= sbiofitmixed(`sm`

,`grpData`

,`responseMap`

,`covEstiminfo`

)^{®} model `sm`

and
returns a `NLMEResults`

object `fitResults`

.

`grpData`

is a ```
groupedData
object
```

specifying the data to fit.
`responseMap`

defines the mapping between the model
components and response data in `grpData`

.
`covEstiminfo`

is a `CovariateModel object`

or an
array of `estimatedInfo`

objects that defines the parameters to
be estimated.

If the model contains active doses and variants, they are applied during the simulation.

uses
the dosing information specified by a matrix of SimBiology dose objects `fitResults`

= sbiofitmixed(`sm`

,`grpData`

,`responseMap`

,`covEstiminfo`

,`dosing`

)`dosing`

instead
of using the active doses of the model `sm`

if
there are any.

uses
the estimation function specified by `fitResults`

= sbiofitmixed(`sm`

,`grpData`

,`responseMap`

,`covEstiminfo`

,`dosing`

,`functionName`

)`functionName`

that
must be either `'nlmefit'`

or `'nlmefitsa'`

.

uses
the additional options specified by `fitResults`

= sbiofitmixed(`sm`

,`grpData`

,`responseMap`

,`covEstiminfo`

,`dosing`

,`functionName`

,`opt`

)`opt`

for the
estimation function `functionName`

.

applies
variant objects specified as `fitResults`

= sbiofitmixed(`sm`

,`grpData`

,`responseMap`

,`covEstiminfo`

,`dosing`

,`functionName`

,`opt`

,`variants`

)`variants`

instead
of using any active variants of the model.

specifies whether to estimate parameters in parallel if Parallel
Computing Toolbox™ is available.`fitResults`

= sbiofitmixed(_,'UseParallel',`tf_parallel`

)

specifies whether to show the progress of parameter estimation.`fitResults`

= sbiofitmixed(_,'ProgressPlot',`tf_progress`

)

`[`

returns a vector of results objects `fitResults`

,`simDataI`

,`simDataP`

]
= sbiofitmixed(_)`fitResults`

,
vector of simulation results `simDataI`

using individual-specific
parameter estimates, and vector of simulation results `simDataP`

using
population parameter estimates.

`sbiofitmixed`

unifies`sbionlmefit`

and`sbionlmefitsa`

estimation functions. Use`sbiofitmixed`

to perform nonlinear mixed-effects modeling and estimation.`sbiofitmixed`

simulates the model using a`SimFunction object`

, which automatically accelerates simulations by default. Hence it is not necessary to run`sbioaccelerate`

before you call`sbiofitmixed`

.

[1] Grasela Jr, T.H., Donn, S.M. (1985) Neonatal population pharmacokinetics of phenobarbital derived from routine clinical data. Dev Pharmacol Ther. 8(6), 374–83.

`CovariateModel object`

| `NLMEResults object`

| ```
estimatedInfo
object
```

| `groupedData`

| `nlmefit`

| `nlmefitsa`

| `sbiofit`

| `sbiofitstatusplot`

- Modeling the Population Pharmacokinetics of Phenobarbital in Neonates
- What Is a Nonlinear Mixed-Effects Model?
- Nonlinear Mixed-Effects Modeling Workflow
- Specify a Covariate Model
- Specify an Error Model
- Maximum Likelihood Estimation
- Obtain the Fitting Status
- Supported Methods for Parameter Estimation
- Progress Plot