NLINResults
Estimation results object for nlinfit
algorithm
Description
Creation
Use sbiofit
with nlinfit
estimation algorithm to create an NLINResults
object.
Properties
GroupName
— Name of the group associated with the results
categorical | empty array
Name of the group associated with the results, specified as a categorical. If the
'Pooled'
name-value pair argument was set to
true
when you ran sbiofit
, then
GroupName
is returned as an empty array or
[]
.
Beta
— Table of estimated parameters
table
Table of estimated parameters, specified as a table. The jth
row of the table represents the jth estimated parameter
βj. It contains transformed values of
parameter estimates if any parameter transform is specified. Standard errors of these
parameter estimates (StandardError
) are calculated as:
sqrt(diag(COVB))
.
It can also contain the following variables:
Bounds
— the values of transformed parameter bounds that you specified during fittingCategoryVariableName
— the names of categories or groups that you specified during fittingCategoryValue
— the values of category variables specified byCategoryVariableName
This table contains one row per distinct parameter value.
ParameterEstimates
— Table of estimated parameters
table
Table of estimated parameters, specified as a table. The jth
row of the table represents the jth estimated parameter
βj. This table contains untransformed
values of parameter estimates. Standard errors of these parameter estimates
(StandardError
) are calculated as:
sqrt(diag(CovarianceMatrix))
.
It can also contain the following variables:
Bounds
— the values of transformed parameter bounds that you specified during fittingCategoryVariableName
— the names of categories or groups that you specified during fittingCategoryValue
— the values of category variables specified byCategoryVariableName
This table contains sets of parameter values that are identified for each individual or group.
J
— Jacobian matrix of the model
array
Jacobian matrix of the model, specified as an array. The Jacobian matrix with respect to an estimated parameter is
where ti is the ith time point, βj is the jth estimated parameter in the transformed space, and yk is the kth response in the group of data.
COVB
— Estimated covariance matrix for Beta
matrix
Estimated covariance matrix for Beta
, specified as a matrix. This
matrix is calculated as: COVB = inv(J'*J)*MSE
.
CovarianceMatrix
— Estimated covariance matrix for ParameterEstimates
matrix
Estimated covariance matrix for ParameterEstimates
, specified as
a matrix. This matrix is calculated as: CovarianceMatrix = T'*COVB*T
,
where T = diag(JInvT(Beta))
. JInvT(Beta)
returns a
Jacobian matrix of Beta
which is inverse transformed accordingly if
you specified any transform to estimated parameters.
For instance, suppose you specified the log-transform for an estimated parameter
x
when you ran sbiofit
. The inverse transform is: InvT = exp(x)
, and
its Jacobian is: JInvT = exp(x)
since the derivative of
exp
is also exp
.
R
— Residuals matrix
matrix
Residuals matrix, specified as a matrix. Rij is the residual for the ith time point and the jth response in the group of data.
LogLikelihood
— Maximized loglikelihood for the fitted model
scalar
Maximized loglikelihood for the fitted model, specified as a scalar.
AIC
— Akaike Information Criterion (AIC)
scalar
Akaike Information Criterion (AIC), specified as a scalar. The AIC is calculated as
AIC = 2*(-LogLikelihood + P)
, where P is the
number of parameters.
BIC
— Bayes Information Criterion (BIC)
scalar
Bayes Information Criterion (BIC), specified as a scalar. The BIC is calculated as
BIC = -2*LogLikelihood + P*log(N)
, where N is
the number of observations, and P is the number of parameters.
DFE
— Degrees of freedom for error
scalar
Degrees of freedom for error (DFE), specified as a scalar. The DFE is calculated as
DFE = N-P
, where N is the number of observations
and P is the number of parameters.
MSE
— Mean squared error
scalar
Mean squared error, specified as a scalar.
SSE
— Sum of squared (weighted) errors or residuals
scalar
Sum of squared (weighted) errors or residuals, specified as a scalar.
Weights
— Matrix of weights
matrix
Matrix of weights, specified as a matrix with one column per response and one row per observation.
Data
— Data used for fitting
groupedData
object
Data used for fitting, specified as a groupedData
object.
In most cases, this Data
property contains
a copy of groupedData
specified as the input data in the
sbiofit
call or the Data property of
a fitproblem
object.
One exception is that the Data
property of unpooled fit results objects
contain only the subset of data for the individual group used for fitting.
EstimatedParameterNames
— Estimated parameter names
cell array of character vectors
Estimated parameter names, specified as a cell array of character vectors.
ErrorModelInfo
— Error models and estimated error model parameters
table
Error models and estimated error model parameters, specified as a table.
The table has one row per error model.
The
ErrorModelInfo.Properties.RowsNames
property identifies which responses the row applies to.The table contains three variables:
ErrorModel
,a
, andb
. TheErrorModel
variable is categorical. The variablesa
andb
can beNaN
when they do not apply to a particular error model.
There are four built-in error models. Each model defines the error using a standard mean-zero and unit-variance (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.
'constant'
:'proportional'
:'combined'
:'exponential'
:
EstimationFunction
— Name of the estimation function
character vector
Name of the estimation function, specified as a character vector.
DependentFiles
— File names to include for deployment
cell array of character vectors
File names to include for deployment, specified as a cell array of character vectors.
Object Functions
boxplot | Create box plot showing the variation of estimated SimBiology model parameters |
fitted | Return simulation results of SimBiology model fitted using least-squares regression |
plot | Compare simulation results to the training data, creating a time-course subplot for each group |
plotActualVersusPredicted | Compare predictions to actual data, creating a subplot for each response |
plotResidualDistribution | Plot the distribution of the residuals |
plotResiduals | Plot residuals for each response, using time, group, or prediction as x-axis |
predict | Simulate and evaluate fitted SimBiology model |
random | Simulate SimBiology model, adding variations by sampling error model |
summary | Return structure array that contains estimated values and fit quality statistics |
Version History
Introduced in R2014a
See Also
LeastSquaresResults object
| OptimResults object
| sbiofit
| sbiofitmixed
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)