:orphan: 





.. _gen_indep_fit_indep_tut:



Diagonal matrix generation diagonal matrix fit using separate univariate normals
################################################################################

[Generated automatically as a Tutorial summary]

Inputs
******



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

:Name: gen_indep_fit_indep

:Title: Diagonal matrix generation diagonal matrix fit using separate univariate normals

: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.
| Note here the 'diagonal matrix' is implemented as two separate univariate normal distributions, which is equivalent.

:Keywords: dep_one_cmp_cl; one compartment model; diagonal matrix

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

:Diagram: 

Failed to create compartment diagram

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

.. 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] = 0.2
    f[V_isv] = 0.1



Starting f[X] values
====================

.. 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] = 0.01
    f[V_isv] = 0.01



Outputs
*******



Generating and Fitting Summaries
================================

* Gen: :ref:`gen_indep_fit_indep_gen` (gen)
* Fit: :ref:`gen_indep_fit_indep_fit` (fit)

Fitted f[X] values
==================

.. 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] = 0.17503
    f[V_isv] = 0.087207



Plots
*****



Dense comp plots
================



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


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


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


Alternatively see :ref:`gen_indep_fit_indep_dense_comp_plots`

Comparison
**********



True objective value
====================


.. code-block:: pyml

    -2170.87328898



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


.. code-block:: pyml

    -2172.76865244



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


No Main f[X] values to compare.

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


No Noise f[X] values to compare.

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



.. csv-table:: 
    :file: fx_comp_variance.csv
    :header-rows: 1
