:orphan: 





.. _advan3trans6_pop_fit:



Nonmem ADVAN3 TRANS6 example
############################

[Generated automatically as a Fitting summary]

Model Description
*****************


:Name: advan3trans6_pop

:Title: Nonmem ADVAN3 TRANS6 example

:Author: PoPy for PK/PD

:Abstract: 

| ADVAN 3 TRANS6 problem. Central compartment with a peripheral compartment.
| trans 6 parameters:-
| * K21 is the rate from the peripheral to the central compartment
| * ALPHA
| * BETA
| map to advan3 parameters:-
| * K21 = K21
| * K12 = ALPHA+BETA-K21-K
| * KE = APLHA*BETA/K21
| ALPHA<K21<BETA. This must be true for model to work.

:Keywords: advan3; trans6; two compartment model

:Input Script: :download:`advan3trans6_pop_fit.pyml <advan3trans6_pop_fit.pyml>`

:Diagram: 


.. thumbnail:: advan3trans6_pop_fit.pyml_output/compartment_diagram.svg
    :width: 200px


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ALPHA]                   0.1000          0.1970        0.0970         0.9705
f[DIFFALPHAK21]            0.2000          0.2996        0.0996         0.4978
f[DIFFK21BETA]             0.2000          0.3022        0.1022         0.5108
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0500          0.0099        0.0401         0.8015
f[ANOISE]                  0.0500          0.0096        0.0404         0.8077
===============  ================  ==============  ============  =============

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


===================================  ================  ==============  ============  =============
Variable Name                          Starting Value    Fitted Value    Abs Change    Prop Change
===================================  ================  ==============  ============  =============
f[ALPHA_isv]                                   0.0400          0.0968        0.0568         1.4192
f[ALPHA_isv;DIFFALPHAK21_isv]                  0.0000         -0.0011        0.0011       INF
f[ALPHA_isv;DIFFK21BETA_isv]                   0.0000         -0.0008        0.0008       INF
f[DIFFALPHAK21_isv;ALPHA_isv]                  0.0000         -0.0011        0.0011       INF
f[DIFFALPHAK21_isv]                            0.0800          0.0525        0.0275         0.3436
f[DIFFALPHAK21_isv;DIFFK21BETA_isv]            0.0000          0.0028        0.0028       INF
f[DIFFK21BETA_isv;ALPHA_isv]                   0.0000         -0.0008        0.0008       INF
f[DIFFK21BETA_isv;DIFFALPHAK21_isv]            0.0000          0.0028        0.0028       INF
f[DIFFK21BETA_isv]                             0.0700          0.0124        0.0576         0.8232
===================================  ================  ==============  ============  =============

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`advan3trans6_pop_simulated_sim_plots`

Population simulated (sim) plots
================================


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -3364.1589


which required 1.27 iterations and took 159.10 seconds

Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[ALPHA] = 0.1970
    f[DIFFALPHAK21] = 0.2996
    f[DIFFK21BETA] = 0.3022
    f[PNOISE] = 0.0099
    f[ANOISE] = 0.0096
    f[ALPHA_isv,DIFFALPHAK21_isv,DIFFK21BETA_isv] = [
        [ 0.0968, -0.0011, -0.0008 ],
        [ -0.0011, 0.0525, 0.0028 ],
        [ -0.0008, 0.0028, 0.0124 ],
    ]



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


:Fixed Effects: :download:`fx_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <advan3trans6_pop_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


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


Starting f[X] values (before fitting)
=====================================


.. code-block:: pyml

    f[ALPHA] = 0.1000
    f[DIFFALPHAK21] = 0.2000
    f[DIFFK21BETA] = 0.2000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.0500
    f[ALPHA_isv,DIFFALPHAK21_isv,DIFFK21BETA_isv] = [
        [ 0.0400, 0.0000, 0.0000 ],
        [ 0.0000, 0.0800, 0.0000 ],
        [ 0.0000, 0.0000, 0.0700 ],
    ]

