:orphan: 





.. _d1cmp_cl_iov_05_tut:



One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5
###############################################################################

[Generated automatically as a Tutorial summary]

Inputs
******



Description
===========

:Name: d1cmp_cl_iov_05

:Title: One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance
| Here f[CL_isv] true value is 0.5

:Keywords: one compartment model; dep_one_cmp_cl; iov

:Input Script: :download:`d1cmp_cl_iov_05.pyml <d1cmp_cl_iov_05.pyml>`

:Diagram: 

Failed to create compartment diagram

True f[X] values
================

.. 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] = 0.5000
    f[CL_iov] = 0.0100



Starting f[X] values
====================

.. 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



Outputs
*******



Generating and Fitting Summaries
================================

* Gen: :ref:`d1cmp_cl_iov_05_gen` (gen)
* Fit: :ref:`d1cmp_cl_iov_05_fit` (fit)

Fitted f[X] values
==================

.. code-block:: pyml

    f[KA] = 0.3156
    f[CL] = 2.3649
    f[V] = 19.7233
    f[PNOISE_STD] = 0.0984
    f[ANOISE_STD] = 0.0486
    f[CL_isv] = 0.3028
    f[CL_iov] = 0.0029



Plots
*****



Dense comp plots
================



.. thumbnail:: images/comp_dense/000001.svg
    :width: 200px


.. thumbnail:: images/comp_dense/000002.svg
    :width: 200px


.. thumbnail:: images/comp_dense/000003.svg
    :width: 200px


Alternatively see :ref:`d1cmp_cl_iov_05_dense_comp_plots`

Comparison
**********



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


.. code-block:: pyml

    -375.4831



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


.. code-block:: pyml

    -384.2726



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
