: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

    121987.0135


which required 1.30 iterations and took 339.47 seconds

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

.. code-block:: pyml

    f[KA] = 2.6741
    f[CL] = 0.9560
    f[V1] = 86.0580
    f[KA_isv,CL_isv,V1_isv] = [
        [ 1.1328, -0.0132, 0.2360 ],
        [ -0.0132, 0.0002, -0.0028 ],
        [ 0.2360, -0.0028, 0.0504 ],
    ]
    f[PNOISE] = 0.2288
    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      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[KA]                      1.0000          2.6741         1.6741        1.6741
f[CL]                      1.0000          0.9560         0.0440        0.0440
f[V1]                     20.0000         86.0580         3.3029       66.0580
===============  ================  ==============  =============  ============

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


===============  ================  ==============  =============  ============
Variable Name      Starting Value    Fitted Value    Prop Change    Abs Change
===============  ================  ==============  =============  ============
f[PNOISE]                  0.1000          0.2288         1.2885        0.1288
===============  ================  ==============  =============  ============

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


================  ================  ==============  =============  ============
Variable Name       Starting Value    Fitted Value    Prop Change    Abs Change
================  ================  ==============  =============  ============
f[KA_isv]                   0.0500          1.1328        21.6551        1.0828
f[KA_isv;CL_isv]            0.0100         -0.0132         2.3154        0.0232
f[KA_isv;V1_isv]            0.0100          0.2360        22.5972        0.2260
f[CL_isv;KA_isv]            0.0100         -0.0132         2.3154        0.0232
f[CL_isv]                   0.0500          0.0002         0.9968        0.0498
f[CL_isv;V1_isv]            0.0100         -0.0028         1.2760        0.0128
f[V1_isv;KA_isv]            0.0100          0.2360        22.5972        0.2260
f[V1_isv;CL_isv]            0.0100         -0.0028         1.2760        0.0128
f[V1_isv]                   0.0500          0.0504         0.0087        0.0004
================  ================  ==============  =============  ============