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Generate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)


phi = sbiosampleparameters(covexpr,fe,omega,ds)
phi = sbiosampleparameters(covexpr,fe,omega,n)
[phi,covmodel] = sbiosampleparameters(_)



phi = sbiosampleparameters(covexpr,fe,omega,ds) generates a matrix phi containing sampled parameter values using the covariate model specified by the covariate expression covexpr, fixed effects fe, covariance matrix omega, and covariate data ds.


phi = sbiosampleparameters(covexpr,fe,omega,n) uses a scalar n that specifies the number of rows in phi when the parameters are not dependent on any covariate.


[phi,covmodel] = sbiosampleparameters(_) returns a matrix phi and a covariate model object covmodel using any of the input arguments from previous syntaxes.


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This example uses data collected on 59 preterm infants given phenobarbital during the first 16 days after birth. Each infant received an initial dose followed by one or more sustaining doses by intravenous bolus administration. A total of between 1 and 6 concentration measurements were obtained from each infant at times other than dose times, for a total of 155 measurements. Infant weights and APGAR scores (a measure of newborn health) were also recorded. Data was described in [1], a study funded by the NIH/NIBIB grant P41-EB01975.

Load the data.

load pheno.mat ds

Visualize the data.

t = sbiotrellis(ds,'ID','TIME','CONC','marker','o','markerfacecolor',[.7 .7 .7],'markeredgecolor','r','linestyle','none');
t.plottitle = 'States versus Time';

Create a one-compartment PK model with bolus dosing and linear clearance to model such data.

pkmd = PKModelDesign;
onecomp = pkmd.construct;

Suppose there is a correlation between the volume of the central compartment (Central) and the weight of infants. You can define this parameter-covariate relationship using a covariate model that can be described as


where, for each ith infant, V is the volume, θs (thetas) are fixed effects, η (eta) represents random effects, and WEIGHT is the covariate.

covM = CovariateModel;
covM.Expression = {'Central = exp(theta1+theta2*WEIGHT+eta1)'};

Define the fixed and random effects.

thetas = [1.4 0.05];
eta1 = [0.2];

Change the group label of ds to 'GROUP' as required by the sbiosampleparameters function.

ds.Properties.VarNames{1} = 'GROUP';

Generate parameter values for the volumes of central compartments Central based on the covariate model for all infants in the data set.

phi = sbiosampleparameters(covM.Expression,thetas,eta1,ds);

You can then simulate the model using the sampled parameter values. For convenience, use the function-like interface provided by a SimFunction object.

First, construct a SimFunction object using the createSimFunction method, specifying the volume (Central) as the parameter, and the drug concentration in the compartment (Drug_Central) as the output of the SimFunction object, and the dosed species.

f = createSimFunction(onecomp,covM.ParameterNames,'Drug_Central','Drug_Central');

The data set ds contains dosing information for each infant, and the groupedData object provides a convenient way to extract such dosing information. Convert ds to a groupedData object and extract dosing information.

grpData = groupedData(ds);
doses = createDoses(grpData,'DOSE');

Simulate the model using the sampled parameter values from phi and the extracted dosing information of each infant, and plot the results. The ith run uses the ith parameter value in phi and dosing information of the ith infant.

sbiotrellis(f(phi,200, doses.getTable), [],'TIME','Drug_Central');

Input Arguments

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Covariate expressions, specified as a cell array of character vectors that defines the parameter-covariate relationships.


See CovariateModel object to learn more about covariate expressions.

Fixed effects, specified as a vector, dataset, or table containing values for fixed effect parameters defined in the covariate expressions covexpr. Fixed effect parameter names must start with 'theta'.

When fe is specified as a vector, it must be in the increasing order of the suffixes in 'theta' if they are numeric such as 'theta1', 'theta2', etc. If the suffixes are nonnumeric or mixed, fe must be specified in ascending ASCII dictionary order.


Use the sort function to sort a cell array of character vectors to see the ASCII dictionary order.

ans = 

    'theta2'    'theta4'    'thetaone'    'thetax'

Then specify parameter values in the same order.

thetas = [0.1 1.3 0.3 4.1];

where 'theta2' has the value of 0.1, 'theta4', 1.3, etc.

Covariance matrix of random effects, specified as a matrix or dataset or table. Random effect parameter names must start with 'eta'.

When omega is specified as a matrix, it must be in the increasing order of suffixes in 'eta' such as 'eta1', 'eta2', etc. If the suffixes are nonnumeric or mixed, omega must be specified in ascending ASCII dictionary order. An example of a diagonal covariance matrix with three random effect parameters (eta1, eta2, and eta3) is


If omega is a dataset, omega.Properties.VarNames must match the names of the random effects.

If omega is a table, omega.Properties.VariableNames must match the names of the random effects.

Covariate data, specified as a dataset or table containing the covariate data for all groups.

ds must have a column named 'Group' or 'GROUP' specifying the group labels as well as a column each for all covariates used in the covariate model. The column names must match the names of the corresponding covariates used in the covariate expressions.

Number of rows in phi, specified as a scalar.

Output Arguments

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Sampled parameter values, returned as a matrix of size S-by-P, where S is the number of groups specified in ds or specified by n and P is the number of parameters which is equal to the number of elements in covexpr.

Covariate model, returned as a CovariateModel object which represents the model defined by covexpr.


[1] Grasela Jr, T.H., Donn, S.M. (1985) Neonatal population pharmacokinetics of phenobarbital derived from routine clinical data. Dev Pharmacol Ther. 8(6), 374–83.

Introduced in R2014a

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