:orphan: 





.. _d1cmp_cl_isv_naive_fit:



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

[Generated automatically as a Fitting 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_fit.pyml <d1cmp_cl_isv_naive_fit.pyml>`

:Diagram: 


.. thumbnail:: d1cmp_cl_isv_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          0.1818        0.3182         0.6363
f[CL]                      1.0000          2.5519        1.5519         1.5519
f[V]                      15.0000         20.1441        5.1441         0.3429
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE_STD]              0.2000          0.4965        0.2965         1.4826
f[ANOISE_STD]              0.2000          0.1279        0.0721         0.3605
===============  ================  ==============  ============  =============

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




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_isv_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

    -163.1359


which required 1.17 iterations and took 16.60 seconds

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



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


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_cl_isv_naive_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <d1cmp_cl_isv_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] = 0.0000

