:orphan: 





.. _blq_pk_norm_fit_fit:



Depot One Comp PK with BLQ observations set to LLQ
##################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: blq_pk_norm_fit

:Title: Depot One Comp PK with BLQ observations set to LLQ

:Author: J.R. Hartley

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations set to LLQ (lower limit of quantification).

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

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

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

:Diagram: 


.. thumbnail:: blq_pk_norm_fit.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

    122016.6973


which required N. iterations and took 91.02 seconds

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

.. code-block:: pyml

    f[KA] = 1.2697
    f[CL] = 0.9437
    f[V1] = 89.5814
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.1630, -0.0014, -0.0034 ],
        [ -0.0014, 0.0021, -0.0112 ],
        [ -0.0034, -0.0112, 0.0666 ],
    ]
    f[PNOISE] = 0.2321
    f[ANOISE] = 0.0100



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <blq_pk_norm_fit.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_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                    1.2697            1.0000         0.2697        0.2697
f[CL]                    0.9437            1.0000         0.0563        0.0563
f[V1]                   89.5814           20.0000         3.4791       69.5814
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.2321            0.1000         1.3205        0.1321
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.1630            0.0500         2.2609        0.1130
f[KA_isv;CL_isv]         -0.0014            0.0100         1.1411        0.0114
f[KA_isv;V1_isv]         -0.0034            0.0100         1.3385        0.0134
f[CL_isv;KA_isv]         -0.0014            0.0100         1.1411        0.0114
f[CL_isv]                 0.0021            0.0500         0.9582        0.0479
f[CL_isv;V1_isv]         -0.0112            0.0100         2.1168        0.0212
f[V1_isv;KA_isv]         -0.0034            0.0100         1.3385        0.0134
f[V1_isv;CL_isv]         -0.0112            0.0100         2.1168        0.0212
f[V1_isv]                 0.0666            0.0500         0.3318        0.0166
================  ==============  ================  =============  ============