N.I.M.R.O.D.  
Functions/Subroutines

parameterTransformation.f90 File Reference

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Functions/Subroutines

subroutine transfo (t, Y, NEQ)
 Parameters transformation when t or Y have an impact.
subroutine transfoFixedInTime
 Parameters transformation for fixed parameters in time.

Function Documentation

subroutine transfo ( double precision,intent(in)  t,
DOUBLE PRECISION,dimension(neq),intent(in)  Y,
INTEGER,intent(in)  NEQ 
)

Parameters transformation when t or Y have an impact.

AUTHOR : Melanie Prague Daniel Commenges Julia Drylewicz Jeremy guedj Rodolphe Thiebaut

DESCRIPTION :

Reparametrization of the system allows us to take constraints into account: we introduce one-to-one functions $\psi_l(.),$ $l=1 \dots p$ and defined transformed parameters $\tilde{\xi}_l^i(t)=\psi_l(\xi_l^i(t))$. For instance, biological parameters such as rates can be parametrized using a logarithmic transformation to ensure positivity, or parameters between 0 and 1 (such as probabilities or bioavailability) can be parametrized using a logistic transformation. The function used must be a one-to-one functions. Only parameters varying with time are defined here. Explanatory variables associated with the fixed effects must be defined.

CAUTION : Must "certainly/maybe" be modified when the model changes

MODIFICATION:

01/09/2012 - Prague - Refactoring

INFORMATIONS:

Parameters:
[in]tTime for which we want parameters values
[in]YBiomarkers values at time t
[in]NEQNumber of Biomarkers

Definition at line 38 of file parameterTransformation.f90.

Referenced by FEX(), FEXcl(), FEXka(), FEXV0(), initialPoints(), JEX(), JEXcl(), JEXka(), and JEXV0().

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subroutine transfoFixedInTime ( )

Parameters transformation for fixed parameters in time.

AUTHOR : Melanie Prague Daniel Commenges Julia Drylewicz Jeremy guedj Rodolphe Thiebaut

DESCRIPTION :

Reparametrization of the system allows us to take constraints into account: we introduce one-to-one functions $\psi_l(.),$ $l=1 \dots p$ and defined transformed parameters $\tilde{\xi}_l^i(t)=\psi_l(\xi_l^i(t))$. For instance, biological parameters such as rates can be parametrized using a logarithmic transformation to ensure positivity, or parameters between 0 and 1 (such as probabilities or bioavailability) can be parametrized using a logistic transformation. The function used must be a one-to-one functions. Only parameters not varying with time are defined here. Explanatory variables associated with the fixed effects must be defined.

CAUTION : Must "certainly/maybe" be modified when the model changes

MODIFICATION:

01/09/2012 - Prague - Refactoring

INFORMATIONS:

Definition at line 88 of file parameterTransformation.f90.

References WorkingSharedValues::b0, Datareading::dose, and WorkingSharedValues::numpat1.

Referenced by solution().

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