# NLINResults

Estimation results object for `nlinfit` algorithm

## Description

The `NLINResults` object contains estimation results from fitting a SimBiology® model to data using `sbiofit` with `nlinfit` as a choice of estimation algorithm. See the `sbiofit` function for a list of other supported algorithms.

## Creation

Use `sbiofit` with `nlinfit` estimation algorithm to create an `NLINResults` object.

## Properties

expand all

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 `[]`.

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 fitting

• `CategoryVariableName` — the names of categories or groups that you specified during fitting

• `CategoryValue` — the values of category variables specified by `CategoryVariableName`

This table contains one row per distinct parameter value.

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 fitting

• `CategoryVariableName` — the names of categories or groups that you specified during fitting

• `CategoryValue` — the values of category variables specified by `CategoryVariableName`

This table contains sets of parameter values that are identified for each individual or group.

Jacobian matrix of the model, specified as an array. The Jacobian matrix with respect to an estimated parameter is

`$J\left(i,j,k\right)={\frac{\partial {y}_{k}}{\partial {\beta }_{j}}|}_{{t}_{i}}$`

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.

Estimated covariance matrix for `Beta`, specified as a matrix. This matrix is calculated as: `COVB = inv(J'*J)*MSE`.

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`.

Residuals matrix, specified as a matrix. Rij is the residual for the ith time point and the jth response in the group of data.

Maximized loglikelihood for the fitted model, specified as a 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.

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.

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.

Mean squared error, specified as a scalar.

Sum of squared (weighted) errors or residuals, specified as a scalar.

Matrix of weights, specified as a matrix with one column per response and one row per observation.

Estimated parameter names, specified as a cell array of character vectors.

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`, and `b`. The `ErrorModel` variable is categorical. The variables `a` and `b` can be `NaN` 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'`: $y=f+ae$

• `'proportional'`: $y=f+b|f|e$

• `'combined'`: $y=f+\left(a+b|f|\right)e$

• `'exponential'`: $y=f\ast \mathrm{exp}\left(ae\right)$

Name of the estimation function, specified as a character vector.

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