:orphan: 





.. _emax_binary_tut:



binary_emax_PD_model
####################

[Generated automatically as a Tutorial summary]

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


:Name: emax_binary

:Title: binary_emax_PD_model

:Author: PoPy for PK/PD

:Abstract: 

| An emax PK/PD Model, based on the amount of drug in the body.
| The two compartment PK model with previously estimated values of K, K21 and K12 for each individual.
| The amount in the central compartment influences the effect compartment along with EMAX (the maximum effect) and EC50 (the amount at which the effect is half the maximum)

:Keywords: PD; Pharmacodynamics; two compartment model; emax; EC50; binary response

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    89.8551



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


.. code-block:: pyml

    88.9284



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.

Outputs
*******



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


.. code-block:: pyml

    f[EMAX] = 80.1058
    f[E50] = 20.0475
    f[ANOISE] = 0.9466



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`emax_binary_gen` (gen)
* Fit: :ref:`emax_binary_fit` (fit)

Inputs
******



True f[X] values (for simulation)
=================================

.. code-block:: pyml

    f[EMAX] = 80.0000
    f[E50] = 20.0000
    f[ANOISE] = 1.0000



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

.. code-block:: pyml

    f[EMAX] = 100.0000
    f[E50] = 10.0000
    f[ANOISE] = 0.5000

