:orphan: 





.. _gen_diag_fit_full_fit:



Diagonal matrix generation full matrix fit
##########################################

[Generated automatically as a Fitting summary]

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


:Name: gen_diag_fit_full

:Title: Diagonal matrix generation full matrix fit

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with absorption compartment and CL/V parametrisation.
| This script uses a diagonal covariance matrix to generate the data and a full covariance matrix to fit.

:Keywords: dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix

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

:Diagram: 


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


Comparison
**********



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




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




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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[CL_isv]                  0.0100          0.1803        0.1703        17.0335
f[CL_isv;V_isv]            0.0001          0.0087        0.0086        86.2809
f[V_isv;CL_isv]            0.0001          0.0087        0.0086        86.2809
f[V_isv]                   0.0100          0.0886        0.0786         7.8610
===============  ================  ==============  ============  =============

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:`gen_diag_fit_full_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

    -2173.3582


which required 1.11 iterations and took 308.79 seconds

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


.. 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,V_isv] = [
        [ 0.1803, 0.0087 ],
        [ 0.0087, 0.0886 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <gen_diag_fit_full_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[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv,V_isv] = [
        [ 0.0100, 0.0001 ],
        [ 0.0001, 0.0100 ],
    ]

