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Simple Tut Example

[Generated automatically as a Fitting summary]

Model Description

Name:

tut_example1

Title:

Simple Tut Example

Author:

PoPy for PK/PD

Abstract:

One compartment model with elimination rate constant KE.
Keywords:

one compartment model; iv_one_cmp_k

Input Script:

tut_example1_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KE]

0.0500

0.1062

0.0562

1.1233

Compare Noise f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[PNOISE]

0.1000

0.0450

0.0550

0.5503

Compare Variance f[X]

Variable Name

Starting Value

Fitted Value

Abs Change

Prop Change

f[KE_isv]

0.1000

0.0260

0.0740

0.7398

Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

Population simulated (sim) plots

allOBS(DV_CENTRAL)_vs_IPRED(CEN)

allOBS(DV_CENTRAL)_vs_PRED(CEN)

allOBS_vs_TIME

CWRES(DV_CENTRAL)_vs_IPRED(CEN)

IRES(DV_CENTRAL)_vs_IPRED(CEN)

IWRES(DV_CENTRAL)_vs_IPRED(CEN)

WRES(DV_CENTRAL)_vs_PPRED(CEN)

Outputs

Final objective value

-48.4038

which required 1.11 iterations and took 84.10 seconds

Fitted f[X] values (after fitting)

f[KE] = 0.1062
f[PNOISE] = 0.0450
f[KE_isv] = 0.0260

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[KE] = 0.0500
f[PNOISE] = 0.1000
f[KE_isv] = 0.1000
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