Estimate nonlinear mixed effects with stochastic EM algorithm (requires Statistics and Machine Learning Toolbox software)
sbionlmefitsa
will be removed in a future
release. Use sbiofitmixed
instead.
results
= sbionlmefitsa(modelObj
, pkModelMapObject
, pkDataObject
, InitEstimates
)results
= sbionlmefitsa(modelObj
, pkModelMapObject
, pkDataObject
, CovModelObj
)results
= sbionlmefitsa(..., Name,Value
)results
= sbionlmefitsa(..., optionStruct
)
[results
, SimDataI
, SimDataP
]
= sbionlmefitsa(...)
performs
estimations using the Stochastic Approximation ExpectationMaximization
(SAEM) algorithm for fitting population data with the SimBiology^{®} model, results
= sbionlmefitsa(modelObj
, pkModelMapObject
, pkDataObject
, InitEstimates
)modelObj
,
and returns the estimated results in the results
structure.
specifies
the relationship between parameters and covariates using results
= sbionlmefitsa(modelObj
, pkModelMapObject
, pkDataObject
, CovModelObj
)CovModelObj
,
a CovariateModel
object. The CovariateModel
object
also provides the parameter transformation.
performs
estimations using the SAEM algorithm, with additional options specified
by one or more results
= sbionlmefitsa(..., Name,Value
)Name,Value
pair arguments.
Following is an alternative to the previous syntax:
specifies results
= sbionlmefitsa(..., optionStruct
)optionStruct
,
a structure containing fields and values, that are the namevalue
pair arguments accepted by nlmefitsa
. The defaults
for optionStruct
are the same as the defaults
for the namevalue pair arguments used by nlmefitsa
,
with the exceptions explained in Input Arguments.
[
returns simulation data of the SimBiology model, results
, SimDataI
, SimDataP
]
= sbionlmefitsa(...)modelObj
,
using the estimated values of the parameters.

SimBiology model object used to fit observed data.  

 

 

Vector of initial estimates for the fixed effects. The first  

 

Structure containing fields and values that are namevalue pair
arguments accepted by the If you have Parallel Computing Toolbox™, you can enable parallel
computing for faster data fitting by setting the namevalue pair argument parpool; % Open a parpool for parallel computing opt = statset(...,'UseParallel',true); % Enable parallel computing results = sbionlmefitsa(...,'Options',opt); % Perform data fitting

Specify optional commaseparated pairs of Name,Value
arguments.
Name
is the argument
name and Value
is the corresponding
value. Name
must appear
inside single quotes (' '
).
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN
.
The sbionlmefitsa
function uses the namevalue
pair arguments supported by the nlmefitsa
function.
These nlmefitsa
namevalue pair arguments
are hardcoded in sbionlmefitsa
, and therefore,
you cannot set them:
FEParamsSelect
FEConstDesign
FEGroupDesign
FEObsDesign
REConstDesign
REGroupDesign
REObsDesign
Vectorization
If you provide a CovariateModel
object as
input to sbionlmefitsa
, then these nlmefitsa
namevalue
pairs are computed from the covariate model, and therefore, you cannot
set them:
FEGroupDesign
ParamTransform
REParamsSelect
You can set all other nlmefitsa
namevalue
pair arguments. For details on these arguments, see the nlmefitsa
reference page.
Be aware that the defaults for these nlmefitsa
namevalue
pair arguments differ when used by sbionlmefitsa
:

Numeric array specifying the design matrix for each group. For details, see Specify a Nonlinear, MixedEffects Model. Default:  

Vector of integers specifying how the parameters are distributed. For details, see Specify Parameter Transformations.
Default: Vector of ones, which specifies all parameters are log transformed.  

Character vector specifying the optimization function used in maximizing the likelihood. Default:  

Structure containing multiple fields, including Default: The default for 
Tip:
SimBiology software includes the 

Structure containing these fields:





Model object
 nlmefitsa
 PKData object
 PKModelDesign
object
 PKModelMap
object
 sbiofitstatusplot
 sbionlinfit
 sbionlmefit
 SimData object