:orphan: 





.. _gen_full_fit_diag_fit:



Full matrix generation diagonal matrix fit
##########################################

[Generated automatically as a Fitting summary]

Inputs
******



Description
===========

:Name: gen_full_fit_diag

:Title: Full matrix generation diagonal matrix fit

:Author: Wright Dose Ltd

:Abstract: 

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

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

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

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

:Diagram: 


.. thumbnail:: gen_full_fit_diag_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. code-block:: pyml

    f[KA] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv,V_isv] = [
        [ 0.01, 0 ],
        [ 0, 0.01 ]
    ]



Outputs
*******



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

.. code-block:: pyml

    -2187.54430299


which required 9 iterations and took 84.20 seconds

Final fitted fixed effects
==========================

.. code-block:: pyml

    f[KA] = 0.3
    f[CL] = 3
    f[V] = 20
    f[PNOISE_STD] = 0.1
    f[ANOISE_STD] = 0.05
    f[CL_isv,V_isv] = [
        [ 0.11772, 0 ],
        [ 0, 0.12139 ]
    ]



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


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

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

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

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

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



Plots
*****



Dense sim plots
===============



.. thumbnail:: images/fit_dense/000000.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000001.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000002.svg
    :width: 200px


Alternatively see :ref:`gen_full_fit_diag_dense_sim_plots`

Comparison
**********



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




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




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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]              0.117716              0.01        10.7716      0.107716
f[CL_isv;V_isv]        0                     0                        0
f[V_isv;CL_isv]        0                     0                        0
f[V_isv]               0.121386              0.01        11.1386      0.111386
===============  ==============  ================  =============  ============