Diagonal matrix generation diagonal matrix fit

[Generated automatically as a Fitting summary]

Model Description

Name:: gen_diag_fit_diag
Title:: Diagonal matrix generation diagonal matrix fit
Author:: PoPy for PK/PD
Abstract:

One compartment model with absorption compartment and CL/V parametrisation.

This script uses a diagonal covariance matrix to generate the data and a diagonal covariance matrix to fit.

Keywords:: one compartment model; dep_one_cmp_cl; diagonal matrix
Input Script:: gen_diag_fit_diag_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[CL_isv]	0.0100	0.1784	0.1684	16.8414
f[CL_isv;V_isv]	0.0000	0.0000	0.0000	INF
f[V_isv;CL_isv]	0.0000	0.0000	0.0000	INF
f[V_isv]	0.0100	0.0881	0.0781	7.8090

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-2172.8028

which required 1.10 iterations and took 177.96 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
    [ 0.1784, 0.0000 ],
    [ 0.0000, 0.0881 ],
]

Fitted parameter .csv files

Fixed Effects:: fx_params.csv (fit)
Random Effects:: rx_params.csv (fit)
Model params:: mx_params.csv (fit)
State values:: sx_params.csv (fit)
Predictions:: px_params.csv (fit)
Likelihoods:: lx_params.csv (fit)

Inputs

Input Data:: cx_obs_params.csv

Starting f[X] values (before fitting)

f[KA] = 0.3000
f[CL] = 3.0000
f[V] = 20.0000
f[PNOISE_STD] = 0.1000
f[ANOISE_STD] = 0.0500
f[CL_isv,V_isv] = [
    [ 0.0100, 0.0000 ],
    [ 0.0000, 0.0100 ],
]