:orphan: 





.. _d1cmp_cl_iov_naive_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: d1cmp_cl_iov_naive

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

:Author: PoPy for PK/PD

:Abstract: 

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

:Keywords: one compartment model; dep_one_cmp_cl; iov

:Input Script: :download:`d1cmp_cl_iov_naive_fit.pyml <d1cmp_cl_iov_naive_fit.pyml>`

:Diagram: 


.. thumbnail:: d1cmp_cl_iov_naive_fit.pyml_output/compartment_diagram.svg
    :width: 200px


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KA]                      0.5000          0.2913        0.2087         0.4174
f[CL]                      1.0000          2.4780        1.4780         1.4780
f[V]                      15.0000         22.5113        7.5113         0.5008
===============  ================  ==============  ============  =============

Compare Noise f[X]
==================


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE_STD]              0.2000          0.4125        0.2125         1.0625
f[ANOISE_STD]              0.2000          0.0709        0.1291         0.6456
===============  ================  ==============  ============  =============

Compare Variance f[X]
=====================


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[CL_isv]                  0.0100          0.1414        0.1314        13.1363
===============  ================  ==============  ============  =============

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`d1cmp_cl_iov_naive_simulated_sim_plots`

Population simulated (sim) plots
================================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/allOBS_vs_TIME/comb_spag.svg
            :width: 200px
      - allOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    -203.5525


which required 1.19 iterations and took 49.52 seconds

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


.. code-block:: pyml

    f[KA] = 0.2913
    f[CL] = 2.4780
    f[V] = 22.5113
    f[PNOISE_STD] = 0.4125
    f[ANOISE_STD] = 0.0709
    f[CL_isv] = 0.1414
    f[CL_iov] = 0.0000



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <d1cmp_cl_iov_naive_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`cx_obs_params.csv <cx_obs_params.csv>`


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] = 0.0000

