:orphan: 





.. _circ_sin_tut:



Sine circadian model
####################

[Generated automatically as a Tutorial summary]

Inputs
******



Description
===========

:Name: circ_sin

:Title: Sine circadian model

:Author: Wright Dose Ltd

:Abstract: 

| A PD Model based on the amount of drug in the body.
| The PD model uses a sine function which simulates a circadian rhythm for the generation of a biomarker.
| The amount in the central compartment is determined by CL and V, PK patameters , which have been previosly estimated for each individual.
| 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_sin_tut.pyml <circ_sin_tut.pyml>`

:Diagram: 


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


True f[X] values
================

.. code-block:: pyml

    f[AMP] = 2.0000
    f[INT] = 8.0000
    f[KOUT] = 0.0500
    f[ANOISE] = 3.0000



Starting f[X] values
====================

.. code-block:: pyml

    f[AMP] = 3.0000
    f[INT] = 16.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 5.0000



Outputs
*******



Generating and Fitting Summaries
================================

* Gen: :ref:`circ_sin_gen` (gen)
* Fit: :ref:`circ_sin_fit` (fit)

Fitted f[X] values
==================

.. code-block:: pyml

    f[AMP] = 1.9936
    f[INT] = 19.9038
    f[KOUT] = 0.0497
    f[ANOISE] = 10.0000



Plots
*****



Dense comp plots
================



.. thumbnail:: images/comp_dense/000001.svg
    :width: 200px


Alternatively see :ref:`circ_sin_dense_comp_plots`

Comparison
**********



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


.. code-block:: pyml

    815.0562



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


.. code-block:: pyml

    1175.2009



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.