:orphan: 





.. _circ_cos_fit:



Cosine circadian model
######################

[Generated automatically as a Fitting summary]

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


:Name: circ_cos

:Title: Cosine circadian model

:Author: PoPy for PK/PD

:Abstract: 

| A PD Model based on the concentration of drug in the body.
| The PD model uses a cosine 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 parameters, which have been estimated for each individual.
| The concentration in the central compartment influences the rate of removal of a biomarker (KOUT).

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

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[AMP]                     3.0000          2.0077        0.9923         0.3308
f[INT]                    16.0000         20.0368        4.0368         0.2523
f[KOUT]                    0.1000          0.0502        0.0498         0.4985
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                  5.0000          3.0888        1.9112         0.3822
===============  ================  ==============  ============  =============

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




Population simulated (sim) plots
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    813.7698


which required 1.19 iterations and took 11.45 seconds

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


.. code-block:: pyml

    f[AMP] = 2.0077
    f[INT] = 20.0368
    f[KOUT] = 0.0502
    f[ANOISE] = 3.0888



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <circ_cos_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <circ_cos_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <circ_cos_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <circ_cos_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <circ_cos_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <circ_cos_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`cx_obs_params.csv <cx_obs_params.csv>`


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


.. code-block:: pyml

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

