Tip Use a CovariateModel object as an input argument to sbionlmefit or sbionlmefitsa to fit a model with covariate dependencies. Before using the CovariateModel object with either fitting function, set the FixedEffectValues property to specify the initial estimates for the fixed effects.

Construction

CovModelObj = CovariateModel creates an empty CovariateModel object.

CovModelObj = CovariateModel(Expression) creates a CovariateModel object with its Expression property set to Expression, a string or cell array of strings, where each string represents the relationship between a parameter being estimated and one or more covariates. Expression must denote fixed effects with the prefix theta, and random effects with the prefix eta. Each string in Expression must be in the form:

parameterName = relationship

This example of an expression string defines the relationship between a parameter (volume) and a covariate (weight), with fixed effects, but no random effects:

Expression = {'volume = theta1 + theta2*weight'};

This table illustrates expression formats for some common parameter-covariate relationships.

Parameter-Covariate Relationship	Expression Format
Linear with random effect	`Cl = theta1 + theta2*WEIGHT + eta1`
Exponential without random effect	`Cl = exp(theta_Cl + theta_Cl_WT*WEIGHT)`
Exponential, WEIGHT centered by mean, has random effect	`Cl = exp(theta1 + theta2*(WEIGHT - mean(WEIGHT)) + eta1)`
Exponential, log(WEIGHT), which is equivalent to power model	`Cl = exp(theta1 + theta2*log(WEIGHT) + eta1)`
Exponential, dependent on WEIGHT and AGE, has random effect	`Cl = exp(theta1 + theta2WEIGHT + theta3AGE + eta1)`

Tip To simultaneously fit data from multiple dose levels, use a CovariateModel object as an input argument to sbionlmefit, and omit the random effect (eta) from the Expression property in the CovariateModel object.

Tip Use the getCovariateData method of a PKData object to view the covariate data when writing equations for the Expression input argument.

Note You can also construct a CovariateModel object using the construct method of a PKModelDesign object. However, the Expression property of the CovariateModel object does not include covariate dependencies. You can modify the expressions to add covariate dependencies. For details, see Expression.

Method Summary

constructDefaultFixedEffectValues (covmodel)	Create initial estimate vector needed for fit
verify (covmodel)	Check covariate model for errors

Property Summary

CovariateLabels (CovariateModel)	Labels for covariates in `CovariateModel` object
Expression (CovariateModel)	Define relationship between parameters and covariates
FixedEffectDescription (CovariateModel)	Descriptions of fixed effects in `CovariateModel` object
FixedEffectNames (CovariateModel)	Names of fixed effects in `CovariateModel` object
FixedEffectValues (CovariateModel)	Values for initial estimates of fixed effects in `CovariateModel` object
ParameterNames (CovariateModel)	Names of parameters in `CovariateModel` object
RandomEffectNames (CovariateModel)	Names of random effects in `CovariateModel` object

Examples

Create a CovariateModel object and set the Expression property to define the relationship between two parameters (clearance and volume) and two covariates (weight and age) using fixed effects (thetas) and random effects (etas):

covModelObj = CovariateModel
covModelObj.Expression = {'CL = theta1 + theta2*WT + eta1', 'V = theta3 + theta4*AGE + eta2'};

How To

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CovariateModel object - Define relationship between parameters and covariates

Description