Linear PD model

[Generated automatically as a Fitting summary]

Model Description

Name:: linear_pd
Title:: Linear PD model
Author:: PoPy for PK/PD
Abstract:

A simple Linear PD Model.

Model consists of a baseline which increases linearly with concentration.

Keywords:: pd; linear; one compartment model
Input Script:: linear_pd_fit.pyml
Diagram:

Comparison

Compare Main f[X]

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Abs Change	Prop Change
f[BL]	15.0000	9.9937	5.0063	0.3338
f[SLOPE]	0.5000	1.0412	0.5412	1.0825
f[ANOISE]	5.0000	0.4626	4.5374	0.9075

Compare Variance f[X]

Population simulated (sim) plots

indOBS_vs_TIME

Outputs

Final objective value

-54.1617

which required 1.15 iterations and took 10.70 seconds

Fitted f[X] values (after fitting)

f[BL] = 9.9937
f[SLOPE] = 1.0412
f[ANOISE] = 0.4626

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[BL] = 15.0000
f[SLOPE] = 0.5000
f[ANOISE] = 5.0000