:orphan: 





.. _emax_complex_fit:



Complex Emax PD Model
#####################

[Generated automatically as a Fitting summary]

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


:Name: emax_complex

:Title: Complex Emax PD Model

:Author: PoPy for PK/PD

:Abstract: 

| An complex emax PK/PD Model, based on the concentration of drug in the body.
| The two compartment PK model uses previously estimated values of CL, V1, Q and V2 for each individual
| The concentration in the central compartment influences the effect compartment along with EMAX (the maximum effect), EC50 (the concentration at which the effect is half the maximum) using a hill equation.

:Keywords: pkpd

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[EMAX]                  100.0000        105.0956        5.0956         0.0510
f[E50]                    10.0000         47.6442       37.6442         3.7644
f[GAMMA]                   1.0000          0.4755        0.5245         0.5245
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE]                  5.0000          2.7512        2.2488         0.4498
===============  ================  ==============  ============  =============

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

    301.5421


which required 1.30 iterations and took 12.35 seconds

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


.. code-block:: pyml

    f[EMAX] = 105.0956
    f[E50] = 47.6442
    f[GAMMA] = 0.4755
    f[ANOISE] = 2.7512



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <emax_complex_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[EMAX] = 100.0000
    f[E50] = 10.0000
    f[GAMMA] = 1.0000
    f[ANOISE] = 5.0000

