:orphan: 





.. _d1cmp_cl_iov_fit:



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

[Generated automatically as a Fitting 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_fit.pyml <d1cmp_cl_iov_fit.pyml>`

:Diagram: 


.. thumbnail:: d1cmp_cl_iov_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          0.2711        0.2289         0.4577
f[CL]                      1.0000          2.4007        1.4007         1.4007
f[V]                      15.0000         18.3926        3.3926         0.2262
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE_STD]              0.2000          0.0935        0.1065         0.5325
f[ANOISE_STD]              0.2000          0.0487        0.1513         0.7566
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[CL_isv]                  0.0100          0.0829        0.0729         7.2875
f[CL_iov]                  0.0100          0.1073        0.0973         9.7323
===============  ================  ==============  ============  =============

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_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

    -353.3355


which required 1.26 iterations and took 80.08 seconds

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



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


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_cl_iov_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_cl_iov_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_cl_iov_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_cl_iov_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_cl_iov_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <d1cmp_cl_iov_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] = 0.0100

