:orphan: 





.. _circ_sin_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: circ_sin

:Title: Sine circadian model

:Author: PoPy for PK/PD

: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_fit.pyml <circ_sin_fit.pyml>`

:Diagram: 


.. thumbnail:: circ_sin_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          2.0018        0.9982         0.3327
f[INT]                    16.0000          7.8714        8.1286         0.5080
f[KOUT]                    0.1000          0.0500        0.0500         0.4998
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                  5.0000          3.0759        1.9241         0.3848
===============  ================  ==============  ============  =============

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

    811.9569


which required 1.26 iterations and took 12.17 seconds

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


.. code-block:: pyml

    f[AMP] = 2.0018
    f[INT] = 7.8714
    f[KOUT] = 0.0500
    f[ANOISE] = 3.0759



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <circ_sin_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

