:orphan: 





.. _d1cmp_ka_stderr_fit:



One Compartment Model with Absorption estimating KA
###################################################

[Generated automatically as a Fitting summary]

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


:Name: d1cmp_ka_stderr

:Title: One Compartment Model with Absorption estimating KA

:Author: PoPy for PK/PD

:Abstract: 

| One Compartment Model with Absorption compartment with rate (KA).
| Elimination rate parametrised by clearance (CL)
| and volume of distribution (V).
| Model specification uses incremental flows

:Keywords: dep_one_cmp_cl; one compartment model

:Input Script: :download:`d1cmp_ka_stderr_fit.pyml <d1cmp_ka_stderr_fit.pyml>`

:Diagram: 


.. thumbnail:: d1cmp_ka_stderr_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]                      0.3000          0.2739        0.0261         0.0869
===============  ================  ==============  ============  =============

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




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




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


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

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

Outputs
*******



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

.. code-block:: pyml

    -44.7678


which required 1.4 iterations and took 10.41 seconds

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


.. code-block:: pyml

    f[KA] = 0.2739
    f[V] = 18.0030
    f[CL] = 3.2389



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


:Fixed Effects: :download:`fx_params.csv (fit) <d1cmp_ka_stderr_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <d1cmp_ka_stderr_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <d1cmp_ka_stderr_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <d1cmp_ka_stderr_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <d1cmp_ka_stderr_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <d1cmp_ka_stderr_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] = 0.3000
    f[V] = 18.0030
    f[CL] = 3.2389

