• Language: en
  • Documentation version: 1.3.1

Diagonal matrix generation full matrix fit

[Generated automatically as a Fitting summary]

Model Description

Name:

gen_diag_fit_full

Title:

Diagonal matrix generation full 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 full covariance matrix to fit.
Keywords:

dep_one_cmp_cl; one compartment model; diagonal matrix; full matrix

Input Script:

gen_diag_fit_full_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

0.1928

0.1828

18.2837

f[CL_isv;V_isv]

0.0001

-0.0166

0.0167

167.1828

f[V_isv;CL_isv]

0.0001

-0.0166

0.0167

167.1828

f[V_isv]

0.0100

0.1155

0.1055

10.5498

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS_vs_TIME

Outputs

Final objective value

-2217.2438

which required 1.9 iterations and took 188.80 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] = [
    [ 0.1928, -0.0166 ],
    [ -0.0166, 0.1155 ],
]

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, 0.0001 ],
    [ 0.0001, 0.0100 ],
]
Back to Top