Perform nonlinear leastsquares regression using SimBiology models (requires Statistics and Machine Learning Toolbox software)
sbionlinfit
will be removed in a future release. Use sbiofit
instead.
results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
)
results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
, Name,Value
)
results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
, optionStruct
)
[results
, SimDataI
]
= sbionlinfit(...)
performs leastsquares regression using the SimBiology^{®} model, results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
)modelObj
, and returns estimated results
in the results
structure.
performs leastsquares regression, with additional options specified by one or more
results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
, Name,Value
)Name,Value
pair arguments.
Following is an alternative to the previous syntax:
specifies results
= sbionlinfit(modelObj
, pkModelMapObject
, pkDataObj
, InitEstimates
, optionStruct
)optionStruct
, a structure containing fields and
values used by the options
input structure to the nlinfit
(Statistics and Machine Learning Toolbox) function.
[
returns simulations of the SimBiology model, results
, SimDataI
]
= sbionlinfit(...)
, using the
estimated values of the parameters.modelObj

SimBiology model object used to fit observed data. 

Note If using a 

Note For each subset of data belonging to a single group (as defined in the
data column specified by the


Vector of initial parameter estimates for each parameter estimated in


Structure containing fields and values used 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 = sbionlinfit(...,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 quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
The Name,Value
arguments are the same as the fields and
values in the options
structure accepted by
nlinfit
. For a complete list, see the
options
input argument in the nlinfit
(Statistics and Machine Learning Toolbox) reference page in the Statistics and Machine Learning Toolbox™ documentation. The defaults for Name,Value
arguments are the same as for the options
structure accepted by
nlinfit
, except for:
DerivStep
— Default is the lesser of
1e4
, or the value of the
SolverOptions.RelativeTolerance
property of the
configuration set associated with modelObj
, with
a minimum of eps^(1/3)
.
FunValCheck
— Default is
off
.
Following are additional Name,Value
arguments that you can
use with sbionlinfit
.

Vector of integers specifying a transformation function for each
estimated parameter. The transformation function, beta = f(estimate) Each element in the vector must be one of these integers specifying
the transformation for the corresponding value of


Character vector specifying the form of the error term. Default is
If you specify an error model, the
Note If you specify an error model, you cannot specify weights. 

Either of the following:
Default is no weights. If you specify weights, you cannot specify an error model. 

Logical specifying whether Default: 

1byN array of objects, where N is
the number of groups in



Model object
 PKData object
 PKModelDesign object
 PKModelDesign object
 PKModelMap object
 sbionlmefit
 sbionlmefitsa
 nlinfit
(Statistics and Machine Learning Toolbox)