:orphan: 





.. _d1cmp_cl_iov_tut:



One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2
###############################################################################

[Generated automatically as a Tutorial summary]

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


:Name: d1cmp_cl_iov

:Title: One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

:Author: PoPy for PK/PD

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance

:Keywords: one compartment model; dep_one_cmp_cl; iov

:Input Script: :download:`d1cmp_cl_iov.pyml <d1cmp_cl_iov.pyml>`

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    -344.3936



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


.. code-block:: pyml

    -353.3355



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]
=====================



.. csv-table:: 
    :file: fx_comp_variance.csv
    :header-rows: 1


Outputs
*******



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


.. code-block:: pyml

    f[KA] = 0.2711
    f[CL] = 2.4007
    f[V] = 18.3926
    f[PNOISE_STD] = 0.0935
    f[ANOISE_STD] = 0.0487
    f[CL_isv] = 0.0829
    f[CL_iov] = 0.1073



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


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


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


* Gen: :ref:`d1cmp_cl_iov_gen` (gen)
* Fit: :ref:`d1cmp_cl_iov_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv] = 0.2000
    f[CL_iov] = 0.1000



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

.. code-block:: pyml

    f[KA] = 0.5000
    f[CL] = 1.0000
    f[V] = 15.0000
    f[PNOISE_STD] = 0.2000
    f[ANOISE_STD] = 0.2000
    f[CL_isv] = 0.0100
    f[CL_iov] = 0.0100

