Documentation 
Perform nonlinear leastsquares regression
fitResults = sbiofit(sm,grpData,responseMap,estiminfo) estimates parameters of a SimBiology model sm using nonlinear leastsquares regression.
grpData is a groupedData object specifying the data to fit. responseMap is a string or cell array of strings that maps model components to response data in grpData. estimatedInfo is an estimatedInfo object that defines the estimated parameters in the model sm.
If you have specified bound constraints in estiminfo, sbiofit uses the first available function among the following: fmincon, nlinfit, or fminsearch.
If there are no bound constraints, sbiofit uses the first available function among the following: nlinfit, fminunc, or fminsearch.
nlinfit requires Statistics Toolbox™, and fmincon and fminunc require Optimization Toolbox™.
By default, each group in grpData is fit separately, resulting in groupspecific parameter estimates. If the model contains active doses and variants, they are applied during the simulation.
fitResults = sbiofit(sm,grpData,responseMap,estiminfo,dosing) uses the dosing information specified by a matrix of SimBiology dose objects dosing instead of using the active doses of the model sm if there is any.
fitResults = sbiofit(sm,grpData,responseMap,estiminfo,dosing,functionName) uses the estimation function specified by functionName. If the specified function is unavailable, a warning is issued and the first available default function is used.
fitResults = sbiofit(sm,grpData,responseMap,estiminfo,dosing,functionName,options) uses the additional options specified by options for the function functionName.
fitResults = sbiofit(sm,grpData,responseMap,estiminfo,dosing,functionName,options,variants) applies variant objects specified as variants instead of using any active variants of the model.
fitResults = sbiofit(_,Name,Value) uses additional options specified by one or more Name,Value pair arguments.
[fitResults,simdata] = sbiofit(_) also returns a vector of SimData objects simdata using any of the input arguments in the previous syntaxes.
Note:

This example shows how to fit an individual's PK profile data to onecompartment model and estimate pharmacokinetic parameters.
Suppose you have drug plasma concentration data from an individual and want to estimate the volume of the central compartment and the clearance. Assume the drug concentration versus the time profile follows the monoexponential decline , where is the drug concentration at time t, is the initial concentration, and is the elimination rate constant that depends on the clearance and volume of the central compartment .
The synthetic data in this example was generated using the following model and parameters:
Onecompartment model with bolus dosing and firstorder elimination
Volume of the central compartment (Central) = 1.70 liter
Clearance parameter (Cl_Central) = 0.55 liter/hour
Constant error model
The data is stored as a table with variables Time and Conc that represent the time course of the plasma concentration of an individual after an intravenous bolus administration measured at 13 different time points. The variable units for Time and Conc are hour and milligram/liter, respectively.
clear all load(fullfile(matlabroot,'examples','simbio','data15.mat'))
plot(data.Time,data.Conc,'b+') xlabel('Time'); ylabel('Drug Concentration');
Convert the data set to a groupedData object, which is the required data format for the fitting function sbiofit for later use. A groupedData object also lets you set independent variable and group variable names (if they exist). Set the units of the Time and Conc variables. The units are optional and only required for the UnitConversion feature, which automatically converts matching physical quantities to one consistent unit system.
gData = groupedData(data); gData.Properties.VariableUnits = {'hour','milligram/liter'}; gData.Properties
groupedData automatically set the name of the IndependentVariableName property to the Time variable of the data.
ans = Description: '' VariableDescriptions: {} VariableUnits: {'hour' 'milligram/liter'} DimensionNames: {'Row' 'Variable'} UserData: [] RowNames: {} VariableNames: {'Time' 'Conc'} GroupVariableName: '' IndependentVariableName: 'Time'
Construct a OneCompartment Model
Use the builtin PK library to construct a onecompartment model with bolus dosing and firstorder elimination where the elimination rate depends on the clearance and volume of the central compartment. Use the configset object to turn on unit conversion.
pkmd = PKModelDesign; pkc1 = addCompartment(pkmd,'Central'); pkc1.DosingType = 'Bolus'; pkc1.EliminationType = 'linearclearance'; pkc1.HasResponseVariable = true; model = construct(pkmd); configset = getconfigset(model); configset.CompileOptions.UnitConversion = true;
For details on creating compartmental PK models using the SimBiology® builtin library, see Create Pharmacokinetic Models.
Define a single bolus dose of 10 milligram given at time = 0. For details on setting up different dosing schedules, see Doses.
dose = sbiodose('dose'); dose.TargetName = 'Drug_Central'; dose.StartTime = 0; dose.Amount = 10; dose.AmountUnits = 'milligram'; dose.TimeUnits = 'hour';
Map Response Data to the Corresponding Model Component
The data contains drug concentration data stored in the Conc variable. This data corresponds to the Drug_Central species in the model. Therefore, map the data to Drug_Central as follows.
responseMap = {'Drug_Central = Conc'};
Specify Parameters to Estimate
The parameters to fit in this model are the volume of the central compartment (Central) and the clearance rate (Cl_Central). In this case, specify logtransformation for these biological parameters since they are constrained to be positive. The estimatedInfo object lets you specify parameter transforms, initial values, and parameter bounds (optional).
paramsToEstimate = {'log(Central)','log(Cl_Central)'}; estimatedParams = estimatedInfo(paramsToEstimate,'InitialValue',[1 1]);
Now that you have defined onecompartment model, data to fit, mapped response data, parameters to estimate, and dosing, use sbiofit to estimate parameters. By default, sbiofit uses the constant error model and nlinfit estimation method, which requires Statistics Toolbox (TM). The results in this example are returned by the nlinfit method. If you do not have Statistics Toolbox, the default function will change, and your results may be slightly different when you run this example.
nlinfitConst = sbiofit(model,gData,responseMap,estimatedParams,dose)
nlinfitConst = NLINResults with properties: GroupName: [1x1 categorical] Beta: [2x3 table] ParameterEstimates: [2x3 table] J: [13x2 double] COVB: [2x2 double] CovarianceMatrix: [2x2 double] R: [13x1 double] MSE: 0.0375 SSE: 0.4122 Weights: [] EstimatedParameterNames: {'Central' 'Cl_Central'} ErrorModel: 'constant' ErrorParameters: [1x1 table] EstimationFunction: 'nlinfit'
Display Estimated Parameters and Plot Results
Notice the parameter estimates were not far off from the true values (1.70 and 0.55) that were used to generate the data. You may also try different error models to see if they could further improve the parameter estimates.
nlinfitConst.ParameterEstimates plot(nlinfitConst);
ans = Name Estimate StandardError ____________ ________ _____________ 'Central' 1.6993 0.034822 'Cl_Central' 0.53358 0.019679
Try three other supported error models (proportional, combination of constant and proportional error models, and exponential).
nlinfitProp = sbiofit(model,gData,responseMap,estimatedParams,dose,... 'ErrorModel','proportional'); nlinfitExp = sbiofit(model,gData,responseMap,estimatedParams,dose,... 'ErrorModel','exponential'); nlinfitComb = sbiofit(model,gData,responseMap,estimatedParams,dose,... 'ErrorModel','combined');
Compare Sum of Squared Errors (SSE)
Compare the SSE of each fit to see which error model best fits the data. However, only the constant and exponential error models provide the raw residuals, that is, observed minus fitted values. The proportional and combined error models provide weighted residuals. Thus to compare SSE of error models, first calculate the raw (unweighted) residuals for the proportional and combined error models.
The results returned by sbiofit contain fitted values at both experimental and solver time points. Use the fitted function of the results object to get all the simulated (fitted) data. Then use the resample function to extract only those fitted values at experimental time points.
propSimData = fitted(nlinfitProp); combSimData = fitted(nlinfitComb); expTime = gData.Time; propSimData = resample(propSimData,expTime); combSimData = resample(combSimData,expTime);
Calculate the sum of squared errors.
nlinfitPropR = gData.Conc  propSimData.Data; nlinfitPropSSE = sum(nlinfitPropR.^2); nlinfitCombR = gData.Conc  combSimData.Data; nlinfitCombSSE = sum(nlinfitCombR.^2);
Construct a table of SSEs for all error models. Use the reported SSE of the constant and exponential error models by accessing the SSE property of the corresponding results object. For the proportional and combined error models, use the calculated SSEs.
ErrorModels = {'Constant','Proportional','Exponential','Combined'}'; SSE = [nlinfitConst.SSE,nlinfitPropSSE,nlinfitExp.SSE,nlinfitCombSSE]'; sseTable = table(ErrorModels,SSE)
sseTable = ErrorModels SSE ______________ _______ 'Constant' 0.4122 'Proportional' 0.7176 'Exponential' 0.63299 'Combined' 0.4122
As shown in the table, the constant and combined error models have the lowest (and same) SSE. However, the constant error model is a better model since it uses only one error parameter (a) whereas the combined error model uses two (a and b).
In addition, notice that the value of b from the combined error model is close to zero, which also suggests the constant error model is better.
nlinfitConst.ErrorParameters
ans = a _______ 0.19358
nlinfitComb.ErrorParameters
ans = a b _______ __________ 0.17807 4.2452e08
Display Estimated Parameter Values
Show the estimated parameter values of each error model.
allResults = [nlinfitConst,nlinfitProp,nlinfitExp,nlinfitComb]; Error_Model = cell(4,1); Estimated_Central = zeros(4,1); Estimated_Cl_Central = zeros(4,1); t = table(Error_Model,Estimated_Central,Estimated_Cl_Central); for i = 1:height(t) t{i,1} = {allResults(i).ErrorModel}; t{i,2} = allResults(i).ParameterEstimates.Estimate(1); t{i,3} = allResults(i).ParameterEstimates.Estimate(2); end t
t = Error_Model Estimated_Central Estimated_Cl_Central ______________ _________________ ____________________ 'constant' 1.6993 0.53358 'proportional' 1.7986 0.50816 'exponential' 1.7872 0.51701 'combined' 1.6993 0.53358
This example showed how to estimate PK parameters, namely the volume of the central compartment and clearance parameter of an individual, by fitting the PK profile data to onecompartment model. You compared SSE and estimated parameter values of different error models to see which model best explained the data. Final fitted results suggested both the constant and combined error models provided the closest estimates to the parameter values used to generate the data. However, the constant error model is a better model since it uses one fewer parameter than the combined model.
This example shows how to estimate pharmacokinetic parameters of multiple individuals using a twocompartment model.
Suppose you have drug plasma concentration data from three individuals that you want to use to estimate corresponding pharmacokinetic parameters, namely the volume of central and peripheral compartment (Central, Peripheral), the clearance rate (Cl_Central), and intercompartmental clearance (Q12). Assume the drug concentration versus the time profile follows the biexponential decline $${C}_{t}=A{e}^{at}+B{e}^{bt}$$, where C_{t} is the drug concentration at time t, a and b are slopes for corresponding exponential declines.
The synthetic data set contains drug plasma concentration data measured in both central and peripheral compartments. The data was generated using a twocompartment model with an infusion dose and firstorder elimination. These parameters were used for each individual.
Central  Peripheral  Q12  Cl_Central  

Individual 1  1.90  0.68  0.24  0.57 
Individual 2  2.10  6.05  0.36  0.95 
Individual 3  1.70  4.21  0.46  0.95 
The data is stored as a table with variables ID, Time, CentralConc, and PeripheralConc. It represents the time course of plasma concentrations measured at eight different time points for both central and peripheral compartments after a bolus dose.
clear all load(fullfile(matlabroot,'examples','simbio','data10_32R.mat'))
Convert the data set to a groupedData object which is the required data format for the fitting function sbiofit for later use. A groupedData object also lets you set independent variable and group variable names (if they exist). Set the units of the ID, Time, CentralConc, and PeripheralConc variables. The units are optional and only required for the UnitConversion feature, which automatically converts matching physical quantities to one consistent unit system.
gData = groupedData(data); gData.Properties.VariableUnits = {'','hour','milligram/liter','milligram/liter'}; gData.Properties
ans = Description: '' VariableDescriptions: {} VariableUnits: {1x4 cell} DimensionNames: {'Row' 'Variable'} UserData: [] RowNames: {} VariableNames: {'ID' 'Time' 'CentralConc' 'PeripheralConc'} GroupVariableName: 'ID' IndependentVariableName: 'Time'
Create a trellis plot that shows the PK profiles of three individuals.
sbiotrellis(gData,'ID','Time',{'CentralConc','PeripheralConc'});
Use the builtin PK library to construct a twocompartment model with infusion dosing and firstorder elimination where the elimination rate depends on the clearance and volume of central compartment. Use the configset object to turn on unit conversion.
pkmd = PKModelDesign; pkc1 = addCompartment(pkmd,'Central'); pkc1.DosingType = 'Infusion'; pkc1.EliminationType = 'linearclearance'; pkc1.HasResponseVariable = true; pkc2 = addCompartment(pkmd,'Peripheral'); model = construct(pkmd); configset = getconfigset(model); configset.CompileOptions.UnitConversion = true;
Assume every individual receives an infusion dose at time = 0, with a total infusion amount of 100 mg at a rate of 50 mg/hour. For details on setting up different dosing strategies, see Doses.
dose = sbiodose('dose','TargetName','Drug_Central'); dose.StartTime = 0; dose.Amount = 100; dose.Rate = 50; dose.AmountUnits = 'milligram'; dose.TimeUnits = 'hour'; dose.RateUnits = 'milligram/hour';
The data contains measured plasma concentrations in the central and peripheral compartments. Map these variables to the appropriate model species, which are Drug_Central and Drug_Peripheral.
responseMap = {'Drug_Central = CentralConc','Drug_Peripheral = PeripheralConc'};
The parameters to estimate in this model are the volumes of central and peripheral compartments (Central and Peripheral), intercompartmental clearance Q12, and clearance rate Cl_Central. In this case, specify logtransform for Central and Peripheral since they are constrained to be positive. The estimatedInfo object lets you specify parameter transforms, initial values, and parameter bounds (optional).
paramsToEstimate = {'log(Central)','log(Peripheral)','Q12','Cl_Central'}; estimatedParam = estimatedInfo(paramsToEstimate,'InitialValue',[1 1 1 1]);
Fit the model to all of the data pooled together, that is, estimate one set of parameters for all individuals. By default (and also in this example), sbiofit uses the nlinfit estimation function, which requires Statistics Toolbox. If you do not have Statistics Toolbox, the default function will change, and your results may be slightly different when you run this example. To see which estimation function sbiofit used for the fitting, check the EstimationFunction property of the corresponding results object.
pooledFit = sbiofit(model,gData,responseMap,estimatedParam,dose,'Pooled',true)
pooledFit = NLINResults with properties: GroupName: [] Beta: [4x3 table] ParameterEstimates: [4x3 table] J: [24x4x2 double] COVB: [4x4 double] CovarianceMatrix: [4x4 double] R: [24x2 double] MSE: 6.6220 SSE: 291.3688 Weights: [] EstimatedParameterNames: {'Central' 'Peripheral' 'Q12' 'Cl_Central'} ErrorModel: 'constant' ErrorParameters: [1x1 table] EstimationFunction: 'nlinfit'
Plot the fitted results versus the original data. Although three separate plots were generated, the data was fitted using the same set of parameters (that is, all three individuals had the same fitted line).
plot(pooledFit);
Use the summary function to display the final estimated parameter values, including the error parameter estimate for the constant error model (the default error model).
summary(pooledFit);
Estimate one set of parameters for each individual and see if there is any improvement in the parameter estimates. In this example, since there are three individuals, three sets of parameters are estimated.
unpooledFit = sbiofit(model,gData,responseMap,estimatedParam,dose,'Pooled',false);
Plot the fitted results versus the original data. Each individual was fitted differently (that is, each fittted line is unique to each individual) and each line appeared to fit well to individual data.
plot(unpooledFit);
Display the summary of estimated parameters.
summary(unpooledFit);
Display the fitted results of the first individual. The MSE was lower than that of the pooled fit. This is also true for the other two individuals.
unpooledFit(1)
ans = NLINResults with properties: GroupName: [1x1 categorical] Beta: [4x3 table] ParameterEstimates: [4x3 table] J: [8x4x2 double] COVB: [4x4 double] CovarianceMatrix: [4x4 double] R: [8x2 double] MSE: 2.1380 SSE: 25.6559 Weights: [] EstimatedParameterNames: {'Central' 'Peripheral' 'Q12' 'Cl_Central'} ErrorModel: 'constant' ErrorParameters: [1x1 table] EstimationFunction: 'nlinfit'
Generate a plot of the residuals over time to compare the pooled and unpooled fit results. The figure indicates unpooled fit residuals are smaller than those of pooled fit as expected. In addition to comparing residuals, other rigorous crieteria can be used to compare the fitted results.
t = [gData.Time;gData.Time]; res_pooled = vertcat(pooledFit.R); res_pooled = res_pooled(:); res_unpooled = vertcat(unpooledFit.R); res_unpooled = res_unpooled(:); plot(t,res_pooled,'o','MarkerFaceColor','w','markerEdgeColor','b') hold on plot(t,res_unpooled,'o','MarkerFaceColor','b','markerEdgeColor','b') refl = refline(0,0); % A reference line representing a zero residual title('Residuals versus Time'); xlabel('Time'); ylabel('Residuals'); legend({'Pooled','Unpooled'});
This example showed how to perform pooled and unpooled fits using sbiofit. As illustrated, the unpooled fit accounts for variations due to the specific subjects in the study, and, in this case, the model fits better to the data. However, the pooled fit returns populationwide parameters. If you want to estimate populationwide parameters while considering individual variations, use sbiofitmixed.
This example shows how to estimate categoryspecific (such as young versus old, male versus female), individualspecific, and populationwide parameters using PK profile data from multiple individuals.
Suppose you have drug plasma concentration data from 30 individuals and want to estimate pharmacokinetic parameters, namely the volumes of central and peripheral compartment, the clearance, and intercompartmental clearance. Assume the drug concentration versus the time profile follows the biexponential decline , where is the drug concentration at time t, and and are slopes for corresponding exponential declines.
This synthetic data contains the time course of plasma concentrations of 30 individuals after a bolus dose (100 mg) measured at different times for both central and peripheral compartments. It also contains categorical variables, namely Sex and Age.
clear load(fullfile(matlabroot,'examples','simbio','sd5_302RAgeSex.mat'))
Convert the data set to a groupedData object, which is the required data format for the fitting function sbiofit. A groupedData object also allows you set independent variable and group variable names (if they exist). Set the units of the ID, Time, CentralConc, PeripheralConc, Age, and Sex variables. The units are optional and only required for the UnitConversion feature, which automatically converts matching physical quantities to one consistent unit system.
gData = groupedData(data); gData.Properties.VariableUnits = {'','hour','milligram/liter','milligram/liter','',''}; gData.Properties
The IndependentVariableName and GroupVariableName properties have been automatically set to the Time and ID variables of the data.
ans = Description: '' VariableDescriptions: {} VariableUnits: {1x6 cell} DimensionNames: {'Row' 'Variable'} UserData: [] RowNames: {} VariableNames: {1x6 cell} GroupVariableName: 'ID' IndependentVariableName: 'Time'
Display the response data for each individual.
sbiotrellis(gData,'ID','Time',{'CentralConc','PeripheralConc'});
Set Up a TwoCompartment Model
Use the builtin PK library to construct a twocompartment model with infusion dosing and firstorder elimination where the elimination rate depends on the clearance and volume of the central compartment. Use the configset object to turn on unit conversion.
pkmd = PKModelDesign; pkc1 = addCompartment(pkmd,'Central'); pkc1.DosingType = 'Bolus'; pkc1.EliminationType = 'linearclearance'; pkc1.HasResponseVariable = true; pkc2 = addCompartment(pkmd,'Peripheral'); model = construct(pkmd); configset = getconfigset(model); configset.CompileOptions.UnitConversion = true;
For details on creating compartmental PK models using the SimBiology® builtin library, see Create Pharmacokinetic Models.
Assume every individual receives a bolus dose of 100 mg at time = 0. For details on setting up different dosing strategies, see Doses.
dose = sbiodose('dose','TargetName','Drug_Central'); dose.StartTime = 0; dose.Amount = 100; dose.AmountUnits = 'milligram'; dose.TimeUnits = 'hour';
Map the Response Data to Corresponding Model Components
The data contains measured plasma concentration in the central and peripheral compartments. Map these variables to the appropriate model components, which are Drug_Central and Drug_Peripheral.
responseMap = {'Drug_Central = CentralConc','Drug_Peripheral = PeripheralConc'};
Specify Parameters to Estimate
Specify the volumes of central and peripheral compartments Central and Peripheral, intercompartmental clearance Q12, and clearance Cl_Central as parameters to estimate. The estimatedInfo object lets you optionally specify parameter transforms, initial values, and parameter bounds. Since both Central and Peripheral are constrained to be positive, specify a logtransform for each parameter.
paramsToEstimate = {'log(Central)', 'log(Peripheral)', 'Q12', 'Cl_Central'}; estimatedParam = estimatedInfo(paramsToEstimate,'InitialValue',[1 1 1 1]);
Estimate IndividualSpecific Parameters
Estimate one set of parameters for each individual by setting the 'Pooled' namevalue pair argument to false.
unpooledFit = sbiofit(model,gData,responseMap,estimatedParam,dose,'Pooled',false);
Plot the fitted results versus the original data for each individual (group).
plot(unpooledFit);
For an unpooled fit, sbiofit always returns one results object for each individual.
Display the summary of estimated parameters for each individual.
summary(unpooledFit);
Examine Parameter Estimates for Category Dependencies
Explore the unpooled estimates to see if there is any categoryspecific parameters, that is, if some parameters are related to one or more categories. If there are any category dependencies, it might be possible to reduce the number of degrees of freedom by estimating just categoryspecific values for those parameters.
First extract the ID and category values for each ID
catParamValues = unique(gData(:,{'ID','Sex','Age'}));
Add variables to the table containing each parameter's estimate.
allParamValues = vertcat(unpooledFit.ParameterEstimates); catParamValues.Central = allParamValues.Estimate(strcmp(allParamValues.Name, 'Central')); catParamValues.Peripheral = allParamValues.Estimate(strcmp(allParamValues.Name, 'Peripheral')); catParamValues.Q12 = allParamValues.Estimate(strcmp(allParamValues.Name, 'Q12')); catParamValues.Cl_Central = allParamValues.Estimate(strcmp(allParamValues.Name, 'Cl_Central'));
Plot estimates of each parameter for each category. gscatter requires Statistics Toolbox (TM). If you do not have Statistics Toolbox, use other alternative plotting functions such as plot.
h = figure; ylabels = {'Central','Peripheral','Cl\_Central','Q12'}; plotNumber = 1; for i = 1:4 thisParam = estimatedParam(i).Name; % Plot for Sex category subplot(4,2,plotNumber); plotNumber = plotNumber + 1; gscatter(double(catParamValues.Sex), catParamValues.(thisParam), catParamValues.Sex); ax = gca; ax.XTick = []; ylabel(ylabels(i)); % Plot for Age category subplot(4,2,plotNumber); plotNumber = plotNumber + 1; gscatter(double(catParamValues.Age), catParamValues.(thisParam), catParamValues.Age); ax = gca; ax.XTick = []; ylabel(ylabels(i)); end
Based on the plot, it seems that young individuals tend to have higher volumes of central and peripheral compartments (Central, Peripheral) than old invididuals (that is, the volumes seem to be agespecific). In addition, males tend to have lower clearance rates (Cl_Central) than females but the opposite for the Q12 parameter (that is, the clearance and Q12 seem to be sexspecific).
Estimate CategorySpecific Parameters
Use the 'CategoryVariableName' property of the estimatedInfo object to specify which category to use during fitting. Use 'Sex' as the group to fit for the clearrance Cl_Central and Q12 parameters. Use 'Age' as the group to fit for the Central and Peripheral parameters.
estimatedParam(1).CategoryVariableName = 'Age'; estimatedParam(2).CategoryVariableName = 'Age'; estimatedParam(3).CategoryVariableName = 'Sex'; estimatedParam(4).CategoryVariableName = 'Sex'; categoryFit = sbiofit(model,gData,responseMap,estimatedParam,dose)
When fitting by category (or group), sbiofit always returns one results object, not one for each category level. This is because both male and female individuals are considered to be part of the same optimization using the same error model and error parameters, similarly for the young and old individuals.
categoryFit = NLINResults with properties: GroupName: [] Beta: [8x5 table] ParameterEstimates: [120x6 table] J: [240x8x2 double] COVB: [8x8 double] CovarianceMatrix: [8x8 double] R: [240x2 double] MSE: 0.4365 SSE: 206.0170 Weights: [] EstimatedParameterNames: {'Central' 'Peripheral' 'Q12' 'Cl_Central'} ErrorModel: 'constant' ErrorParameters: [1x1 table] EstimationFunction: 'nlinfit'
Plot the categoryspecific estimated results.
plot(categoryFit);
For the Cl_Central and Q12 parameters, all males had the same estimates, and similarly for the females. For the Central and Peripheral parameters, all young individuals had the same estimates, and similarly for the old individuals.
Estimate PopulationWide Parameters
To better compare the results, fit the model to all of the data pooled together, that is, estimate one set of parameters for all individuals by setting the 'Pooled' namevalue pair argument to true. The warning message tells you that this option will ignore any categoryspecific information (if they exist).
pooledFit = sbiofit(model,gData,responseMap,estimatedParam,dose,'Pooled',true);
Warning: You called SBIOFIT using the Pooled option. The CategoryVariableName values of the ESTIMINFO input will be ignored.
Plot the fitted results versus the original data. Although a separate plot was generated for each individual, the data was fitted using the same set of parameters (that is, all individuals had the same fitted line).
plot(pooledFit);
Compare residuals of CentralConc and PeripheralConc responses for each fit.
t = gData.Time; allResid(:,:,1) = pooledFit.R; allResid(:,:,2) = categoryFit.R; allResid(:,:,3) = vertcat(unpooledFit.R); figure; responseList = {'CentralConc', 'PeripheralConc'}; for i = 1:2 subplot(2,1,i); oneResid = squeeze(allResid(:,i,:)); plot(t,oneResid,'o'); refline(0,0); % A reference line representing a zero residual title(sprintf('Residuals (%s)', responseList{i})); xlabel('Time'); ylabel('Residuals'); legend({'Pooled','CategorySpecific','Unpooled'}); end
As shown in the plot, the unpooled fit produced the best fit to the data as it fit the data to each indivdual. This was expected since it used the most number of degrees of freedom. The categoryfit reduced the number of degrees of freedom by fitting the data to two categories (sex and age). As a result, the residuals were larger than the unpooled fit, but still smaller than the populationfit, which estimated just one set of parameters for all individuals. The categoryfit might be a good compromise between the unpooled and pooled fitting provided that any hierarchical model exists within your data.
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 as reported in the reference paper [1].
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 = Name Estimate StandardError _____ ________ _____________ 'kGd' 0.11 0.00037969
SimBiology model, specified as a SimBiology model object. The active configset object of the model contains solver settings for simulation. Any active doses and variants are applied to the model during simulation unless specified otherwise using the dosing and variants input arguments, respectively.
Data to fit, specified as a groupedData object.
The name of the time variable must be defined in the IndependentVariableName property of grpData. For instance, if the time variable name is 'TIME', then specify it as follows.
grpData.Properties.IndependentVariableName = 'TIME';
If the data contains more than one group of measurements, the grouping variable name must be defined in the GroupVariableName property of grpData. For example, if the grouping variable name is 'GROUP', then specify it as follows.
grpData.Properties.GroupVariableName = 'GROUP';
A group usually refers to a set of measurements that represent a single time course, often corresponding to a particular individual or experimental condition.
Note: sbiofit uses the categorical function to identify groups. If any group values are converted to the same value by categorical, then those observations are treated as belonging to the same group. For instance, if some observations have no group information (that is, empty string), then categorical converts empty strings to <undefined>, and these observations are treated as one group. 
Mapping information of model components to grpData, specified as a string or cell array of strings.
Each string is an equationlike expression, similar to assignment rules in SimBiology. It contains the name (or qualified name) of a quantity (species, compartment, or parameter) in the model sm, followed by the character '=' and the name of a variable in grpData. For clarity, white spaces are allowed between names and '='.
For example, if you have the concentration data 'CONC' in grpData for a species 'Drug_Central', you can specify it as follows.
responseMap = 'Drug_Central = CONC';
To unambiguously name a species, use the qualified name, which includes the name of the compartment. To name a reactionscoped parameter, use the reaction name to qualify the parameter. If the name is not a valid MATLAB^{®} variable name, surround it by square brackets such as [reaction 1].[parameter 1].
An error is issued if any (qualified) name matches two components of the same type. However, you can use a (qualified) name that matches two components of different types, and the function first finds the species with the given name, followed by compartments and then parameters.
Estimated parameters, specified as an estimatedInfo object or vector of estimatedInfo objects that defines the estimated parameters in the model sm, and other optional information such as their initial estimates, transformations, bound constraints, and categories. Supported transforms are log, logit, and probit.
If you do not specify Pooled namevalue pair argument, sbiofit uses CategoryVariableName property of estiminfo to decide if parameters should be estimated for each individual, group, category, or all individuals as a whole. Use the Pooled option to override any CategoryVariableName values. For details about CategoryVariableName property, see estimatedInfo object.
Note: sbiofit uses the categorical function to identify groups or categories. If any group values are converted to the same value by categorical, then those observations are treated as belonging to the same group. For instance, if some observations have no group information (that is, empty string as a group value), then categorical converts empty strings to <undefined>, and these observations are treated as one group. 
Dosing information, specified as an empty array or 2D matrix of dose objects (ScheduleDose object or RepeatDose object). If empty, no doses are applied during simulation, even if the model has active doses. If not empty, the matrix must have a single row or one row per group in the data grpData. If it has a single row, same doses are applied to all groups during simulation. If it has multiple rows, each row is applied to a separate group, in the same order as the groups appear in grpData.
Multiple columns are allowed so that you can apply multiple dose objects to each group. Each column of doses must reference the same components in the model sm. Specifically, all doses (except for empty doses) in a column must have the same values for TargetName, DurationParameterName, and LagParameterName. If some groups require more doses than others, then fill in the matrix with dummy doses that are either default doses or empty doses.
A default dose has default values for all properties, except for the Name property. An empty dose has a dose amount of 0, thus having no effect on the model. Create a default dose as follows.
d1 = sbiodose('d1');
In addition to manually constructing dose objects, if grpData has dosing information, you can use the createDoses method to construct doses.
Estimation function name, specified as a string. Choices are as follows.
'nlinfit' (Statistics Toolbox is required.)
'fminunc' (Optimization Toolbox is required.)
'fmincon' (Optimization Toolbox is required.)
'fminsearch'
'lsqcurvefit' (Optimization Toolbox is required.)
'lsqnonlin' (Optimization Toolbox is required.)
'patternserch' (Global Optimization Toolbox is required.)
'ga' (Global Optimization Toolbox is required.)
'particleswarm' (Global Optimization Toolbox is required.)
If the problem uses bound constraints, sbiofit uses the first available function among the following: fmincon, nlinfit, or fminsearch.
If there are no bound constraints, sbiofit uses the first available function among the following: nlinfit, fminunc, or fminsearch.
Options specific to the estimation function, specified as a struct or optimoptions object.
optimset struct for fminsearch
optimoptions object for lsqcurvefit, lsqnonlin, fminunc, and particleswarm
gaoptimset struct for ga
psoptimset struct for patternsearch
statset struct for nlinfit
See Default Options for Estimation Algorithms for more details and default options associated with each estimation function.
Variants, specified as an empty array or vector of variant objects. If empty, no variants are used even if the model has active variants.
Specify optional commaseparated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.
Example: 'ErrorModel','constant','UseParallel',true specifies a constant error model and to run simulations in parallel during parameter estimation.Error model used for estimation, specified as a string. There are four builtin error models. Each model defines the error using a standard meanzero and unitvariance (Gaussian) variable e, simulation results f, and one or two parameters a and b.
'constant': $$y=f+ae$$
'proportional': $$y=f+b\leftf\righte$$
'combined': $$y=f+(a+b\leftf\right)e$$
'exponential': $$y=f\ast \mathrm{exp}(ae)$$
Weights used for estimation, specified as a matrix of real positive weights, where the number of columns corresponds to the number of responses, or a function handle that accepts a vector of predicted response values and returns a vector of real positive weights.
If you specify an error model, you cannot use weights except for the constant error model. If neither the 'ErrorModel' or 'Weights' is specified, by default the function uses the constant error model with equal weights.
Fit option flag, specifying whether to fit each individual (false) or pool all individual data (true).
When true, the function performs fitting for all individuals or groups simultaneously using the same parameter estimates, and fitResults is a scalar results object.
When false, the function fits each group or individual separately using group or individualspecific parameters, and fitResults is a vector of results objects with one result for each group.
Note: Use this option to override any CategoryVariableName values of estiminfo. 
Flag for parallel simulations during fitting, specified as true or false. If true and Parallel Computing Toolbox™ is available, the function runs simulations in parallel.
Estimation results, returned as a OptimResults object or NLINResults object or a vector of these objects. Both results objects are subclasses of the LeastSquaresResults object.
If the function uses nlinfit, then fitResults is a NLINResults object. Otherwise, fitResults is an OptimResults object.
When 'Pooled' is set to false, the function fits each group separately using groupspecific parameters, and fitResults is a vector of results objects with one results object for each group.
When 'Pooled' is set to true, the function performs fitting for all individuals or groups simultaneously using the same parameter estimates, and fitResults is a scalar results object.
When 'Pooled' is not used, and CategoryVariableName values of estiminfo are all <none>, fitResults is a single result object. This is the same behavior as setting 'Pooled' to true.
When 'Pooled' is not used, and CategoryVariableName values of estiminfo are all <GroupVariableName>, fitResults is a vector of results objects. This is the same behavior as setting 'Pooled' to false.
In all other cases, fitResults is a scalar object containing estimated parameter values for different groups or categories specified by CategoryVariableName.
Simulation results, returned as a vector of SimData objects representing simulation results for each group or individual.
If the 'Pooled' option is false, then each simulation uses groupspecific parameter values. If true, then all simulations use the same (populationwide) parameter values.
The states reported in simdata are the states that were included in the responseMap input argument as well as any other states listed in the StatesToLog property of the runtime options (RuntimeOptions) of the SimBiology model sm.
The function sbiofit uses maximum likelihood estimation (MLE) for fitting. Mathematically, the problem is typically formulated as the minimization of the negative of the logarithm of the likelihood function. For the constant error model, this is equivalent to the method of leastsquares, which minimizes the sum of squares of the residuals. For other error models, the objective function has additional terms. When the error models are normally distributed, the negative loglikelihood can be written as follows.
$$\begin{array}{l}\mathrm{ln}\mathcal{L}={\displaystyle \sum}_{i}^{N}\frac{{\left({y}_{i}f\left({x}_{i};p\right)\right)}^{2}}{2{\sigma}_{i}^{2}}+{\displaystyle \sum}_{i}^{N}\mathrm{ln}\sqrt{2\pi {\sigma}_{i}^{2}}\\ \end{array}$$
$$\mathcal{L}$$  Log likelihood 
N  Number of experimental observations 
y_{i}  The ith experimental observation 
$f\left({x}_{i};p\right)$  Predicted value of the ith observation, which is a function of independent variables x_{i} and estimated parameters p 
${\sigma}_{i}$  Standard deviation of the ith observation. For instance, for the combined error model $$y=f+(a+b\leftf\right)e$$, the standard deviation is $\sigma =a+b\leftf\right$. 
When you use numeric weights or the weight function, the weights are assumed to be proportional to the variance of the error, i.e., $${\sigma}_{i}^{2}={a}^{2}\cdot {w}_{i}$$ where a is the constant error parameter. If you use weights, you cannot specify an error model except the constant error model.
Various parameter estimation methods have different requirements on the objective function. For some methods, the estimation of model parameters is performed independently of the estimation of the error model parameters. The following table summarizes the objective function F and any separate formulas used for the estimation of error model parameters. r_{i} is the ith residual, a and b are error model parameters, and f_{i} is the predicted value of the ith observation.
Method  Supported Error Model  Objective Function  Error Parameter Estimation Function 

lsqnonlin lsqcurvefit  'constant'  $$\overrightarrow{F}={r}_{i}$$  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
lsqnonlin lsqcurvefit  Numeric weights (w_{i})  $$\overrightarrow{F}=\frac{{r}_{i}}{\sqrt{{w}_{i}}}$$  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
lsqnonlin lsqcurvefit  'exponential'  $$\overrightarrow{F}=\mathrm{ln}{r}_{i}$$  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
nlinfit  All  $$\overrightarrow{F}={f}_{i}$$  nlinfit also estimates error parameters. 
All others  'constant'  $$F=\mathrm{ln}\mathcal{L}$$  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
All others  'proportional'  $$F=\mathrm{ln}\mathcal{L}$$  ${b}^{2}=\frac{1}{N}{{\displaystyle \sum}}^{\text{}}{\left(\raisebox{1ex}{${r}_{i}$}\!\left/ \!\raisebox{1ex}{${f}_{i}$}\right.\right)}^{2}$ 
All others  'combined'  $$F=\mathrm{ln}\mathcal{L}$$  Error parameters are included in minimization. 
All others  'exponential'  $$F={\displaystyle \sum}_{i}^{N}\frac{{\left(\mathrm{ln}{y}_{i}\mathrm{ln}f\left({x}_{i};p\right)\right)}^{2}}{2{a}^{2}}+{\displaystyle \sum}_{i}^{N}\mathrm{ln}\sqrt{2\pi {a}^{2}}$$, where $$a$$ is the standard deviation of the logtransformed ith observation.  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
All others  Weights  $$F=\mathrm{ln}\mathcal{L}$$  $${a}^{2}=\frac{1}{N}{\displaystyle \sum \frac{{r}_{i}^{2}}{{w}_{i}}}$$ 
Function  Default Options  

nlinfit  sbiofit uses the default options structure associated with nlinfit, except for:
 
fminunc  sbiofit uses the default options structure associated with fminunc, except for: Display = 'off'; TolFun = 1e6*[Initial objective function value] Algorithm = 'quasinewton' FinDiffRelStep = max(eps^(1/3), SolverOptions.RelativeTolerance) TypicalX = 1e6*[Initial estimates]  
fminsearch  sbiofit uses the default options structure associated with fminsearch, except for:
 
lsqcurvefit  Requires Optimization Toolbox. sbiofit uses the default options structure associated with lsqcurvefit, except for:
 
lsqnonlin  Requires Optimization Toolbox. sbiofit uses the default options structure associated with lsqnonlin, except for:
 
patternsearch  Requires Global Optimization Toolbox. sbioparamestim uses the default options structure associated with patternsearch, except for:
 
ga  Requires Global Optimization Toolbox. sbioparamestim uses the default options structure associated with ga, except for:
 
particleswarm  Requires Global Optimization Toolbox. sbiofit uses the following default options for the particleswarm algorithm, except for:

[1] Yi, TM., Kitano, H., and Simon, M. (2003). A quantitative characterization of the yeast heterotrimeric G protein cycle. PNAS. 100, 10764–10769.
estimatedInfo object  fmincon  fminsearch  fminunc  ga  groupedData object  LeastSquaresResults object  lsqcurvefit  lsqnonlin  nlinfit  NLINResults object  OptimResults object  particleswarm  patternsearch  sbiofitmixed