:orphan: 





.. _emax_nondiff_tut:



Direct Effect Emax PD Model
###########################

[Generated automatically as a Tutorial summary]

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


:Name: emax_nondiff

:Title: Direct Effect Emax PD Model

:Author: PoPy for PK/PD

:Abstract: 

| A direct effect emax PK/PD Model, based on the concentration of drug in the body.
| The one compartment PK model uses previously estimated values of CL and V for each individual
| The concentration in the central compartment influences the effect.
| The effect is not a compartment and so does not increase over time. It is dependent on the baseline effect, the concentration in the central compartment, the maximum effect (emax) and concentration at which the effect is half the maximum (EC50).

:Keywords: PD; Pharmacodynamics; one compartment model; emax; EC50; baseline effect

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    -52.6006



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


.. code-block:: pyml

    -54.8713



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


No Main f[X] values to compare.

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] = 522.6699
    f[E50] = 322.8391
    f[EBASE] = 10.0149
    f[ANOISE] = 0.4602



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


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


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


* Gen: :ref:`emax_nondiff_gen` (gen)
* Fit: :ref:`emax_nondiff_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[EMAX] = 100.0000
    f[E50] = 60.0000
    f[EBASE] = 10.0000
    f[ANOISE] = 0.5000



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

.. code-block:: pyml

    f[EMAX] = 80.0000
    f[E50] = 50.0000
    f[EBASE] = 5.0000
    f[ANOISE] = 2.0000

