:orphan: 





.. _blq_pk_norm_fit_ignore_fit:



Depot One Comp PK ignoring BLQ observations.
############################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: blq_pk_norm_fit_ignore

:Title: Depot One Comp PK ignoring BLQ observations.

:Author: J.R. Hartley

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations removed from data set.

:Keywords: tutorial; pk; advan4; dep_two_cmp; blq

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

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

:Diagram: 


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


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

.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100



Outputs
*******



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

.. code-block:: pyml

    -834.3309


which required N. iterations and took 204.98 seconds

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

.. code-block:: pyml

    f[KA] = 0.2299
    f[CL] = 1.8348
    f[V1] = 53.1476
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0177, 0.0129, 0.0214 ],
        [ 0.0129, 0.0140, -0.0088 ],
        [ 0.0214, -0.0088, 0.1722 ],
    ]
    f[PNOISE] = 0.1436
    f[ANOISE] = 0.0100



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


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

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

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

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

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



Plots
*****



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



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


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


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


Alternatively see :ref:`blq_pk_norm_fit_ignore_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                    0.2299            1.0000         0.7701        0.7701
f[CL]                    1.8348            1.0000         0.8348        0.8348
f[V1]                   53.1476           20.0000         1.6574       33.1476
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1436            0.1000         0.4358        0.0436
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.0177            0.0500         0.6459        0.0323
f[KA_isv;CL_isv]          0.0129            0.0100         0.2928        0.0029
f[KA_isv;V1_isv]          0.0214            0.0100         1.1421        0.0114
f[CL_isv;KA_isv]          0.0129            0.0100         0.2928        0.0029
f[CL_isv]                 0.0140            0.0500         0.7201        0.0360
f[CL_isv;V1_isv]         -0.0088            0.0100         1.8827        0.0188
f[V1_isv;KA_isv]          0.0214            0.0100         1.1421        0.0114
f[V1_isv;CL_isv]         -0.0088            0.0100         1.8827        0.0188
f[V1_isv]                 0.1722            0.0500         2.4436        0.1222
================  ==============  ================  =============  ============