plot

Compare simulation results to the training data, creating a time-course subplot for each group

Syntax

plot(resultsObj)

plot(resultsObj,Name,Value)

Description

plot(resultsObj) displays a figure showing the comparison between simulation results to the training data, with a time-course subplot for each group.

example

plot(resultsObj,Name,Value) uses additional options specified by one or more name-value arguments.

example

Examples

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Estimate Two-Compartment PK Parameters

Open Live Script

Load the sample data set.

load data10_32R.mat
gData = groupedData(data);
gData.Properties.VariableUnits = ["","hour","milligram/liter","milligram/liter"];

Create a two-compartment PK model.

pkmd                 = PKModelDesign;
pkc1                 = addCompartment(pkmd,"Central");
pkc1.DosingType      = "Infusion";
pkc1.EliminationType = "linear-clearance";
pkc1.HasResponseVariable = true;
pkc2                 = addCompartment(pkmd,"Peripheral");
model                = construct(pkmd);
configset            = getconfigset(model);
configset.CompileOptions.UnitConversion = true;
responseMap = ["Drug_Central = CentralConc","Drug_Peripheral = PeripheralConc"];

Provide model parameters to estimate.

paramsToEstimate   = ["log(Central)","log(Peripheral)","Q12","Cl_Central"];
estimatedParam     = estimatedInfo(paramsToEstimate,'InitialValue',[1 1 1 1]);

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.

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";

Estimate model parameters. By default, the function estimates a set of parameter for each individual (unpooled fit).

fitResults =  sbiofit(model,gData,responseMap,estimatedParam,dose);

Plot the results.

plot(fitResults);

Plot all groups in one plot.

plot(fitResults,"PlotStyle","one axes");

Change some axes properties.

s = struct;
s.Properties.XGrid = "on";
s.Properties.YGrid = "on";
plot(fitResults,"PlotStyle","one axes","AxesStyle",s);

Compare the model predictions to the actual data.

plotActualVersusPredicted(fitResults)

Use boxplot to show the variation of estimated model parameters.

boxplot(fitResults)

Figure contains an axes object. The axes object contains 28 objects of type line. One or more of the lines displays its values using only markers

Plot the distribution of residuals. This normal probability plot shows the deviation from normality and the skewness on the right tail of the distribution of residuals. The default (constant) error model might not be the correct assumption for the data being fitted.

plotResidualDistribution(fitResults)

Plot residuals for each response using the model predictions on x-axis.

plotResiduals(fitResults,"Predictions")

Figure contains 2 axes objects. Axes object 1 with ylabel PeripheralConc contains 2 objects of type line. One or more of the lines displays its values using only markers Axes object 2 with ylabel CentralConc contains 2 objects of type line. One or more of the lines displays its values using only markers

Get the summary of the fit results. stats.Name contains the name for each table from stats.Table, which contains a list of tables with estimated parameter values and fit quality statistics.

stats = summary(fitResults);
stats.Name

ans = 
'Unpooled Parameter Estimates'

ans = 
'Statistics'

ans = 
'Unpooled Beta'

ans = 
'Residuals'

ans = 
'Covariance Matrix'

ans = 
'Error Model'

stats.Table

ans=3×9 table
    Group    Central Estimate    Central StandardError    Peripheral Estimate    Peripheral StandardError    Q12 Estimate    Q12 StandardError    Cl_Central Estimate    Cl_Central StandardError
    _____    ________________    _____________________    ___________________    ________________________    ____________    _________________    ___________________    ________________________

    {'1'}          1.422                0.12334                 1.5619                   0.36355               0.47163            0.15196                0.5291                  0.036978        
    {'2'}         1.8322               0.019672                 5.3364                   0.65327                0.2764           0.030799               0.86035                  0.026257        
    {'3'}         1.6657               0.038529                 5.5632                   0.37063               0.78361           0.058657                1.0233                  0.027311

ans=3×7 table
    Group      AIC        BIC      LogLikelihood    DFE      MSE         SSE  
    _____    _______    _______    _____________    ___    ________    _______

    {'1'}     60.961     64.051       -26.48        12        2.138     25.656
    {'2'}    -7.8379    -4.7475       7.9189        12     0.029012    0.34814
    {'3'}    -1.4336     1.6567       4.7168        12     0.043292     0.5195

ans=3×9 table
    Group    Central Estimate    Central StandardError    Peripheral Estimate    Peripheral StandardError    Q12 Estimate    Q12 StandardError    Cl_Central Estimate    Cl_Central StandardError
    _____    ________________    _____________________    ___________________    ________________________    ____________    _________________    ___________________    ________________________

    {'1'}        0.35208               0.086736                 0.44589                   0.23277              0.47163            0.15196                0.5291                  0.036978        
    {'2'}        0.60551               0.010737                  1.6746                   0.12242               0.2764           0.030799               0.86035                  0.026257        
    {'3'}        0.51027                0.02313                  1.7162                  0.066621              0.78361           0.058657                1.0233                  0.027311

ans=24×4 table
    ID    Time    CentralConc    PeripheralConc
    __    ____    ___________    ______________

    1       0              0               0   
    1       1        0.10646        -0.74394   
    1       4         1.3745          1.2726   
    1       8       -0.68825         -4.2435   
    1      12        0.67383         0.21806   
    1      18        0.88823          1.0269   
    1      24        0.48941         0.66755   
    1      36        0.13632         0.22948   
    2       0              0               0   
    2       1      -0.026731       -0.058311   
    2       4      -0.033299        -0.20544   
    2       8       -0.20466         0.20696   
    2      12       -0.12223        0.045409   
    2      18       0.041224         0.33883   
    2      24      -0.059498       0.0036257   
    2      36      -0.051645         0.27616   
      ⋮

ans=12×6 table
    Group      Parameters        Central      Peripheral        Q12        Cl_Central 
    _____    ______________    ___________    __________    ___________    ___________

    {'1'}    {'Central'   }       0.015213     -0.022539     -0.0086672       0.001159
    {'1'}    {'Peripheral'}      -0.022539       0.13217       0.045746     -0.0073135
    {'1'}    {'Q12'       }     -0.0086672      0.045746       0.023092     -0.0021484
    {'1'}    {'Cl_Central'}       0.001159    -0.0073135     -0.0021484      0.0013674
    {'2'}    {'Central'   }     0.00038701     -0.002161    -0.00010177     9.7448e-05
    {'2'}    {'Peripheral'}      -0.002161       0.42676       0.019101      -0.015755
    {'2'}    {'Q12'       }    -0.00010177      0.019101     0.00094857    -0.00073328
    {'2'}    {'Cl_Central'}     9.7448e-05     -0.015755    -0.00073328     0.00068942
    {'3'}    {'Central'   }      0.0014845    -0.0054648     -0.0013216     0.00016639
    {'3'}    {'Peripheral'}     -0.0054648       0.13737       0.016903     -0.0072722
    {'3'}    {'Q12'       }     -0.0013216      0.016903      0.0034406    -0.00082538
    {'3'}    {'Cl_Central'}     0.00016639    -0.0072722    -0.00082538     0.00074587

ans=3×5 table
    Group     Response      ErrorModel        a        b 
    _____    __________    ____________    _______    ___

    {'1'}    {0x0 char}    {'constant'}     1.2663    NaN
    {'2'}    {0x0 char}    {'constant'}    0.14751    NaN
    {'3'}    {0x0 char}    {'constant'}    0.18019    NaN

Input Arguments

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`resultsObj` — Estimation results
`OptimResults` object | `NLINResults` object | vector of results objects

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

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: plot(fitResults,'PlotStyle','one axes') specifies to plot data from each run into one axes instead of plotting each run individually as a subplot.

`ParameterType` — Type of parameter estimates to plot
`'individual'` (default)

Type of parameter estimates to plot, specified as 'individual'. For LeastSquaresResults objects, 'individual' is the only option indicating to use the individual parameter estimates to plot the simulation results.

Data Types: char | string

`PlotStyle` — Plot style
`'trellis'` (default) | `'one axes'`

Plot style, specified as 'trellis' or 'one axes'. By default, the function plots the data from each run into its own subplot. To plot all data into one plot, use 'one axes'.

Data Types: char | string

`AxesStyle` — Axes properties
structure

Axes properties, specified as a structure. The structure (s) has the following field names and values representing the axes properties.

Field Name	Value
`s.Labels.Title`	Character vector or string scalar.
`s.Labels.XLabel`	Character vector or string scalar.
`s.Labels.YLabel`	Character vector or string scalar.
`s.Properties.XGrid`	`'off'` (default) or `'on'`
`s.Properties.XScale`	`'linear'` (default) or `'log'`
`s.Properties.XDir`	`'normal'` (default) or `'reverse'`
`s.Properties.XLim`	Two-element vector of the form `[min max]`
`s.Properties.YGrid`	`'off'` (default) or `'on'`
`s.Properties.YScale`	`'linear'` (default) or `'log'`
`s.Properties.YDir`	`'normal'` (default) or `'reverse'`
`s.Properties.YLim`	Two-element vector of the form `[min max]`

Data Types: structure

Version History

Introduced in R2014a