plot
Description
uses additional options specified by one or more name-value arguments.h
= plot(eeObj
,Name=Value
)
Examples
Perform GSA by Computing Elementary Effects
Load the tumor growth model.
sbioloadproject tumor_growth_vpop_sa.sbproj
Get a variant with estimated parameters and the dose to apply to the model.
v = getvariant(m1);
d = getdose(m1,'interval_dose');
Get the active configset and set the tumor weight as the response.
cs = getconfigset(m1);
cs.RuntimeOptions.StatesToLog = 'tumor_weight';
Simulate the model and plot the tumor growth profile.
sbioplot(sbiosimulate(m1,cs,v,d));
Perform global sensitivity analysis (GSA) on the model to find the model parameters that the tumor growth is sensitive to.
First, define model parameters of interest, which are involved in the pharmacodynamics of the tumor growth. Define the model response as the tumor weight.
modelParamNames = {'L0','L1','w0','k1'}; outputName = 'tumor_weight';
Then perform GSA by computing the elementary effects using sbioelementaryeffects
. Use 100
as the number of samples and set ShowWaitBar
to true
to show the simulation progress.
rng('default');
eeResults = sbioelementaryeffects(m1,modelParamNames,outputName,Variants=v,Doses=d,NumberSamples=100,ShowWaitbar=true);
Show the median model response, the simulation results, and a shaded region covering 90% of the simulation results.
plotData(eeResults,ShowMedian=true,ShowMean=false);
You can adjust the quantile region to a different percentage by specifying Alphas
for the lower and upper quantiles of all model responses. For instance, an alpha
value of 0.1 plots a shaded region between the 100*alpha
and 100*(1-alpha)
quantiles of all simulated model responses.
plotData(eeResults,Alphas=0.1,ShowMedian=true,ShowMean=false);
Plot the time course of the means and standard deviations of the elementary effects.
h = plot(eeResults);
% Resize the figure.
h.Position(:) = [100 100 1280 800];
The mean of effects explains whether variations in input parameter values have any effect on the tumor weight response. The standard deviation of effects explains whether the sensitivity change is dependent on the location in the parameter domain.
From the mean of effects plots, parameters L1
and w0
seem to be the most sensitive parameters to the tumor weight before the dose is applied at t = 7. But, after the dose is applied, k1
and L0
become more sensitive parameters and contribute most to the after-dosing stage of the tumor weight. The plots of standard deviation of effects show more deviations for the larger parameter values in the later stage (t > 35) than for the before-dose stage of the tumor growth.
You can also display the magnitudes of the sensitivities in a bar plot. Each color shading represents a histogram representing values at different times. Darker colors mean that those values occur more often over the whole time course.
bar(eeResults);
You can also plot the parameter grids and samples used to compute the elementary effects.
plotGrid(eeResults)
You can specify more samples to increase the accuracy of the elementary effects, but the simulation can take longer to finish. Use addsamples
to add more samples.
eeResults2 = addsamples(eeResults,200);
The SimulationInfo
property of the result object contains various information for computing the elementary effects. For instance, the model simulation data (SimData) for each simulation using a set of parameter samples is stored in the SimData
field of the property. This field is an array of SimData
objects.
eeResults2.SimulationInfo.SimData
SimBiology SimData Array : 1500-by-1 Index: Name: ModelName: DataCount: 1 - Tumor Growth Model 1 2 - Tumor Growth Model 1 3 - Tumor Growth Model 1 ... 1500 - Tumor Growth Model 1
You can find out if any model simulation failed during the computation by checking the ValidSample
field of SimulationInfo
. In this example, the field shows no failed simulation runs.
all(eeResults2.SimulationInfo.ValidSample)
ans = logical
1
You can add custom expressions as observables and compute the elementary effects of the added observables. For example, you can compute the effects for the maximum tumor weight by defining a custom expression as follows.
% Suppress an information warning that is issued. warnSettings = warning('off', 'SimBiology:sbservices:SB_DIMANALYSISNOTDONE_MATLABFCN_UCON'); % Add the observable expression. eeObs = addobservable(eeResults2,'Maximum tumor_weight','max(tumor_weight)','Units','gram');
Plot the computed simulation results showing the 90% quantile region.
h2 = plotData(eeObs,ShowMedian=true,ShowMean=false); h2.Position(:) = [100 100 1500 800];
You can also remove the observable by specifying its name.
eeNoObs = removeobservable(eeObs,'Maximum tumor_weight');
Restore the warning settings.
warning(warnSettings);
Input Arguments
eeObj
— Results containing means and standard deviations of elementary effects
SimBiology.gsa.ElementaryEffects
object
Results containing the means and standard deviations of elementary effects, specified
as a SimBiology.gsa.ElementaryEffects
object.
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.
Example: h = plot(results,Observables='tumor_weight')
plots the mean
and standard deviation of elementary effects corresponding to the tumor weight
response.
Parameters
— Input parameters to plot
character vector | string | string vector | cell array of character vectors | vector of positive integers
Input parameters to plot, specified as a character vector, string, string vector, cell
array of character vectors, or vector of positive integers indexing into the columns of
the resultsObject.ParameterSamples
table. Use this name-value
argument to select parameters and plot their corresponding GSA results. By default, all
input parameters are included in the plot.
Data Types: double
| char
| string
| cell
Observables
— Model responses or observables to plot
character vector | string | string vector | cell array of character vectors | vector of positive integers
Model responses or observables to plot, specified as a character vector, string, string
vector, cell array of character vectors, or vector of positive integers indexing into
resultsObject.Observables
. By default, the function plots GSA
results for all model responses or observables.
Data Types: double
| char
| string
| cell
ShowMean
— Flag to plot means of elementary effects
true
(default) | false
Flag to plot the means of elementary effects, specified as true
or false
.
Data Types: logical
ShowStandardDeviation
— Flag to plot standard deviations of elementary effects
true
(default) | false
Flag to plot the standard deviations of elementary effects, specified as
true
or false
.
Data Types: logical
MeanColor
— Color of means of elementary effects
three-element row vector | hexadecimal color code | color name
Color of the means of elementary effects, specified as a three-element row vector,
hexadecimal color code, color name, or a short name. By default, the function uses
the first MATLAB® default color. To view the default color order, enter
get(groot,'defaultAxesColorOrder')
or see the ColorOrder property.
For details on valid color names and corresponding RGB triplets and hexadecimal codes, see Specify Plot Colors.
Data Types: double
StandardDeviationColor
— Color of standard deviation of elementary effects
three-element row vector | hexadecimal color code | color name
Color of the standard deviation of elementary effects, specified as a
three-element row vector, hexadecimal color code, color name, or a short name. By
default, the function uses the second MATLAB default color. To view the default color order, enter
get(groot,'defaultAxesColorOrder')
or see the ColorOrder property.
For details on valid color names and corresponding RGB triplets and hexadecimal codes, see Specify Plot Colors.
Data Types: double
Output Arguments
h
— Handle
figure handle
Handle to the figure, specified as a figure handle.
Version History
Introduced in R2021b
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