:orphan: 





.. _circ_combined_fit:



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

[Generated automatically as a Fitting 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_fit.pyml <circ_combined_fit.pyml>`

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[AMP1]                    3.0000          2.5428        0.4572         0.1524
f[INT1]                    5.0000         15.3892       10.3892         2.0778
f[AMP2]                    3.0000          0.6906        2.3094         0.7698
f[INT2]                    5.0000          0.1000        4.9000         0.9800
f[AMP3]                    3.0000          3.1310        0.1310         0.0437
f[INT3]                    5.0000         60.8236       55.8236        11.1647
f[KOUT]                    0.1000          0.0478        0.0522         0.5219
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                 10.0000         14.6244        4.6244         0.4624
===============  ================  ==============  ============  =============

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

    1638.2986


which required 1.30 iterations and took 15.75 seconds

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



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <circ_combined_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[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

