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
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. 

NoteIf using a 

NoteFor 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
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
NoteIf 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
 nlinfit
 sbionlmefit
 sbionlmefitsa