:orphan: 





.. _gen_diag_fit_diag_gen:



Diagonal matrix generation diagonal matrix fit
##############################################

[Generated automatically as a Generation summary]

Inputs
******



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

:Name: gen_diag_fit_diag

:Title: Diagonal matrix generation diagonal matrix fit

:Author: Wright Dose Ltd

:Abstract: 

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

:Keywords: one compartment model; dep_one_cmp_cl; diagonal matrix

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

:Diagram: 


.. thumbnail:: gen_diag_fit_diag_gen.pyml_output/gen/compartment_diagram.svg
    :width: 200px


True f[X] values
================

.. 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 ]
    ]



Outputs
*******



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


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

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

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

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

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


:Synthetic Data: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Plots
*****



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



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


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


.. thumbnail:: images/gen_dense/000003.svg
    :width: 200px


Alternatively see :ref:`gen_diag_fit_diag_dense_sim_plots`