:orphan: 





.. _blq_pk_norm_fit_half_fit:



Depot One Comp PK with BLQ observations set to 0.5*LLQ
######################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: blq_pk_norm_fit_half

:Title: Depot One Comp PK with BLQ observations set to 0.5*LLQ

:Author: J.R. Hartley

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification)
| observations set to 0.5*LLQ (lower limit of quantification).

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

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

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

:Diagram: 


.. thumbnail:: blq_pk_norm_fit_half.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

    28174.8044


which required N. iterations and took 389.29 seconds

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

.. code-block:: pyml

    f[KA] = 1.3657
    f[CL] = 1.6485
    f[V1] = 78.8853
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.1806, 0.0094, 0.0283 ],
        [ 0.0094, 0.0123, 0.0281 ],
        [ 0.0283, 0.0281, 0.0647 ],
    ]
    f[PNOISE] = 0.3372
    f[ANOISE] = 0.0100



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <blq_pk_norm_fit_half.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_half_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                    1.3657            1.0000         0.3657        0.3657
f[CL]                    1.6485            1.0000         0.6485        0.6485
f[V1]                   78.8853           20.0000         2.9443       58.8853
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]                0.3372            0.1000         2.3722        0.2372
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]                 0.1806            0.0500         2.6125        0.1306
f[KA_isv;CL_isv]          0.0094            0.0100         0.0581        0.0006
f[KA_isv;V1_isv]          0.0283            0.0100         1.8318        0.0183
f[CL_isv;KA_isv]          0.0094            0.0100         0.0581        0.0006
f[CL_isv]                 0.0123            0.0500         0.7542        0.0377
f[CL_isv;V1_isv]          0.0281            0.0100         1.8137        0.0181
f[V1_isv;KA_isv]          0.0283            0.0100         1.8318        0.0183
f[V1_isv;CL_isv]          0.0281            0.0100         1.8137        0.0181
f[V1_isv]                 0.0647            0.0500         0.2941        0.0147
================  ==============  ================  =============  ============