:orphan: 





.. _gen_diag_fit_full_gen:



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

[Generated automatically as a Generation 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_gen.pyml <gen_diag_fit_full_gen.pyml>`

:Diagram: 


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


Outputs
*******



Individual simulated (sim) plots
================================



.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/gen_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/gen_sim_grph_outputs/allOBS_vs_TIME/comb_spag.svg
            :width: 200px
      - allOBS_vs_TIME

Generated parameter .csv files
==============================


:Fixed Effects: :download:`fx_params.csv (gen) <gen_diag_fit_full_gen.pyml_output/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (gen) <gen_diag_fit_full_gen.pyml_output/rx_params.csv>`

:Model params: :download:`mx_params.csv (gen) <gen_diag_fit_full_gen.pyml_output/mx_params.csv>`

:State values: :download:`sx_params.csv (gen) <gen_diag_fit_full_gen.pyml_output/sx_params.csv>`

:Predictions: :download:`px_params.csv (gen) <gen_diag_fit_full_gen.pyml_output/px_params.csv>`


:Observations: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Inputs
******



True f[X] values (for simulation)
=================================


.. 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] = [
        [ 0.2000, 0.0000 ],
        [ 0.0000, 0.1000 ],
    ]

