fitted(LeastSquaresResults,OptimResults,NLINResults)

Return simulation results of SimBiology model fitted using least-squares regression

Syntax

[yfit,parameterEstimates] = fitted(resultsObj)


Description

[yfit,parameterEstimates] = fitted(resultsObj) returns simulation results yfit and parameter estimates parameterEstimates from a fitted SimBiology^® model.

Tip

Use this method to retrieve simulation results from the fitted model if you did not specify the second optional output argument that corresponds to simulation results when you first ran sbiofit.

Input Arguments

resultsObj — Estimation results

OptimResults object | NLINResults object | vector of results objects

Estimation results, specified as an OptimResults object, NLINResults object, or vector of results objects which contains estimation results from running sbiofit.

Output Arguments

yfit — Simulation results

vector of SimData objects

Simulation results, returned as a vector of SimData objects. The states reported in yfit are the states that were included in the responseMap input argument of sbiofit as well as any other states listed in the StatesToLog property of the runtime options (RuntimeOptions) of the SimBiology model.

parameterEstimates — Estimated parameter values

table

Estimated parameter values, returned as a table. This argument is identical to the resultsObj.ParameterEstimates property.

Examples

Estimate a Parameter from the Yeast G Protein Model

This example uses the yeast heterotrimeric G protein model and experimental data reported by [1]. For details about the model, see the Background section in Parameter Scanning, Parameter Estimation, and Sensitivity Analysis in the Yeast Heterotrimeric G Protein Cycle.

Load the G protein model.

sbioloadproject gprotein

Store the experimental data containing the time course for the fraction of active G protein.

time = [0 10 30 60 110 210 300 450 600]';
GaFracExpt = [0 0.35 0.4 0.36 0.39 0.33 0.24 0.17 0.2]';

Create a groupedData object based on the experimental data.

tbl = table(time,GaFracExpt);
grpData = groupedData(tbl);

Map the appropriate model component to the experimental data. In other words, indicate which species in the model corresponds to which response variable in the data. In this example, map the model parameter GaFrac to the experimental data variable GaFracExpt from grpData.

responseMap = 'GaFrac = GaFracExpt';

Use an estimatedInfo object to define the model parameter kGd as a parameter to be estimated.

estimatedParam = estimatedInfo('kGd');

Perform the parameter estimation.

fitResult = sbiofit(m1,grpData,responseMap,estimatedParam);

View the estimated parameter value of kGd.

fitResult.ParameterEstimates

ans=1×3 table
     Name      Estimate    StandardError
    _______    ________    _____________

    {'kGd'}    0.11307      3.4439e-05  

Suppose you want to plot the model simulation results using the estimated parameter value. You can either rerun the sbiofit function and specify to return the optional second output argument, which contains simulation results, or use the fitted method to retrieve the results without rerunning sbiofit.

[yfit,paramEstim] = fitted(fitResult);

Plot the simulation results.

sbioplot(yfit);

References

[1] Yi, T-M., Kitano, H., and Simon, M. (2003). A quantitative characterization of the yeast heterotrimeric G protein cycle. PNAS. 100, 10764–10769.