: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

    -834.2742


which required N. iterations and took 240.84 seconds

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

.. code-block:: pyml

    f[KA] = 0.2292
    f[CL] = 1.8316
    f[V1] = 53.1015
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0175, 0.0115, 0.0270 ],
        [ 0.0115, 0.0142, -0.0103 ],
        [ 0.0270, -0.0103, 0.1778 ],
    ]
    f[PNOISE] = 0.1437
    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]                    0.2292            1.0000         0.7708        0.7708
f[CL]                    1.8316            1.0000         0.8316        0.8316
f[V1]                   53.1015           20.0000         1.6551       33.1015
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.1437            0.1000         0.4367        0.0437
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.0175            0.0500         0.6491        0.0325
f[KA_isv;CL_isv]          0.0115            0.0100         0.1534        0.0015
f[KA_isv;V1_isv]          0.0270            0.0100         1.7038        0.0170
f[CL_isv;KA_isv]          0.0115            0.0100         0.1534        0.0015
f[CL_isv]                 0.0142            0.0500         0.7164        0.0358
f[CL_isv;V1_isv]         -0.0103            0.0100         2.0296        0.0203
f[V1_isv;KA_isv]          0.0270            0.0100         1.7038        0.0170
f[V1_isv;CL_isv]         -0.0103            0.0100         2.0296        0.0203
f[V1_isv]                 0.1778            0.0500         2.5558        0.1278
================  ==============  ================  =============  ============