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.

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

Examples

Estimate Two-Compartment PK Parameters

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.

plotParameterStats(fitResults,"box");

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");

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.

fitMetrics = summary(fitResults);

Display the table that contains parameter estimates summary statistics.

tableToShow = "Parameter Estimates Summary Statistics";
tableNames = {fitMetrics.Name};
tfShowTable = matches(tableNames,tableToShow);
disp(fitMetrics(tfShowTable).Table);

          Statistic           Central    Peripheral     Q12      Cl_Central
    ______________________    _______    __________    ______    __________

    {'Minimum'           }     1.422       0.5291      1.5619      0.2764  
    {'Lower Quartile'    }     1.483      0.61191      2.5055     0.32521  
    {'Median'            }    1.6657      0.86035      5.3364     0.47163  
    {'Upper Quartile'    }    1.7906      0.98252      5.5065     0.70562  
    {'Maximum'           }    1.8322       1.0233      5.5632     0.78361  
    {'Mean'              }      1.64      0.80423      4.1539     0.51055  
    {'Standard Deviation'}    0.2063      0.25181      2.2476     0.25583  

Input Arguments

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