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	0.0999	0.0499	0.9974

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[PNOISE]	0.1000	0.0503	0.0497	0.4966

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KE_isv]	0.1000	0.0183	0.0817	0.8170

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_PPRED(CEN)
	allOBS_vs_TIME
	CWRES(DV_CENTRAL)_wrt_IPRED(CEN)
	RES(DV_CENTRAL)_wrt_IPRED(CEN)
	WRES(DV_CENTRAL)_wrt_IPRED(CEN)
	WRES(DV_CENTRAL)_wrt_PPRED(CEN)

Outputs

Final objective value

-9.2911

which required 1.12 iterations and took 40.90 seconds

Fitted f[X] values (after fitting)

f[KE] = 0.0999
f[PNOISE] = 0.0503
f[KE_isv] = 0.0183

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