Flip Flop tutorial with low initial estimates of KA, V and CL

[Generated automatically as a Fitting summary]

Model Description

Name:

flip_flop_good

Title:

Flip Flop tutorial with low initial estimates of KA, V and CL

Author:

PoPy for PK/PD

Abstract:

One compartment model with a depot leading into a central compartment.

Here initial values of KE, KA, V are close to the true values, so we get a good fit near the true global minima.

Keywords:

flip flop; dep_one_cmp_k; one compartment model; good start values

Input Script:

flip_flop_good_fit.pyml

Diagram:

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[KA]	0.5000	0.1526	0.3474	0.6949
f[KE]	0.5000	0.3014	0.1986	0.3972

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[ANOISE_STD]	5.0000	0.9207	4.0793	0.8159

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

83.4768

which required 1.19 iterations and took 10.88 seconds

Fitted f[X] values (after fitting)

f[KA] = 0.1526
f[KE] = 0.3014
f[ANOISE_STD] = 0.9207

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] = 0.5000
f[KE] = 0.5000
f[ANOISE_STD] = 5.0000