:orphan: 





.. _d1cmp_cl_isv_naive_tut:



One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0
###############################################################################

[Generated automatically as a Tutorial summary]

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


:Name: d1cmp_cl_isv_naive

:Title: One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

:Author: PoPy for PK/PD

:Abstract: 

| Population one Compartment Model with Absorption and Inter-subject Variance
| Here f[CL_isv] is not estimated it is set to zero.

:Keywords: one compartment model; dep_one_cmp_cl

:Input Script: :download:`d1cmp_cl_isv_naive.pyml <d1cmp_cl_isv_naive.pyml>`

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    2777.3504



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


.. code-block:: pyml

    -163.1359



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[KA] = 0.1818
    f[CL] = 2.5519
    f[V] = 20.1441
    f[PNOISE_STD] = 0.4965
    f[ANOISE_STD] = 0.1279
    f[CL_isv] = 0.0000



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


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


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


* Gen: :ref:`d1cmp_cl_isv_naive_gen` (gen)
* Fit: :ref:`d1cmp_cl_isv_naive_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



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] = 0.0000

