:orphan: 





.. _circ_combined_tut:



Combined circadian model
########################

[Generated automatically as a Tutorial summary]

Model Description
*****************


:Name: circ_combined

:Title: Combined circadian model

:Author: PoPy for PK/PD

:Abstract: 

| A PD Model based on the concentration of drug in the plasma.
| The PD model uses three sine functions with different amplitudes and frequencies, which simulates a circadian rhythm for the generation of a biomarker.
| The amount in the central compartment is determined by CL, V1, Q and V2, PK parameters, which have been previously estimated for each individual in the data file.
| The amount in the central compartment influences the rate of production of a biomarker.

:Keywords: PD; Pharmacodynamics; sine function; Circadian rhythm

:Input Script: :download:`circ_combined_tut.pyml <circ_combined_tut.pyml>`

:Diagram: 


.. thumbnail:: compartment_diagram.svg
    :width: 200px


Comparison
**********



True objective value
====================


.. code-block:: pyml

    1072.0713



Final fitted objective value
============================


.. code-block:: pyml

    1638.2986



Compare Main f[X]
=================



.. csv-table:: 
    :file: fx_comp_main.csv
    :header-rows: 1


Compare Noise f[X]
==================



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


Compare Variance f[X]
=====================


No Variance f[X] values to compare.

Outputs
*******



Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[AMP1] = 2.5428
    f[INT1] = 15.3892
    f[AMP2] = 0.6906
    f[INT2] = 0.1000
    f[AMP3] = 3.1310
    f[INT3] = 60.8236
    f[KOUT] = 0.0478
    f[ANOISE] = 14.6244



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`circ_combined_gen` (gen)
* Fit: :ref:`circ_combined_fit` (fit)

Inputs
******



True f[X] values (for simulation)
=================================

.. code-block:: pyml

    f[AMP1] = 1.2000
    f[INT1] = 3.5000
    f[AMP2] = 2.5000
    f[INT2] = 6.0000
    f[AMP3] = 3.2000
    f[INT3] = 13.0000
    f[KOUT] = 0.0500
    f[ANOISE] = 5.0000



Starting f[X] values (before fitting)
=====================================

.. code-block:: pyml

    f[AMP1] = 3.0000
    f[INT1] = 5.0000
    f[AMP2] = 3.0000
    f[INT2] = 5.0000
    f[AMP3] = 3.0000
    f[INT3] = 5.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 10.0000

