One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

[Generated automatically as a Fitting summary]

Inputs

Description

Name:dep_one_cmp_cl_iov_05 Title:One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5 Author:Wright Dose Ltd Abstract:

Population one Compartment Model with Absorption and Inter-occasion Variance

Here f[CL_isv] true value is 0.5

Keywords:one compartment model; dep_one_cmp_cl; iov Input Script:dep_one_cmp_cl_iov_05_fit.pyml Input Data:synthetic_data.csv Diagram:

Initial fixed effect estimates

f[KA] = 0.5000
f[CL] = 1.0000
f[V] = 15.0000
f[PNOISE_STD] = 0.2000
f[ANOISE_STD] = 0.2000
f[CL_isv] = 0.0100
f[CL_iov] = 0.0100

Outputs

Final objective value

-365.3289

which required 1.20 iterations and took 378.19 seconds

Final fitted fixed effects

f[KA] = 0.2686
f[CL] = 2.0908
f[V] = 18.1651
f[PNOISE_STD] = 0.0889
f[ANOISE_STD] = 0.0486
f[CL_isv] = 0.2834
f[CL_iov] = 0.0105

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)

Plots

Dense sim plots

Alternatively see All dense_sim graph plots

Comparison

Compare Main f[X]

Variable Name	Starting Value	Fitted Value	Prop Change	Abs Change
f[KA]	0.5000	0.2686	0.4628	0.2314
f[CL]	1.0000	2.0908	1.0908	1.0908
f[V]	15.0000	18.1651	0.2110	3.1651

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Prop Change	Abs Change
f[PNOISE_STD]	0.2000	0.0889	0.5553	0.1111
f[ANOISE_STD]	0.2000	0.0486	0.7570	0.1514

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Prop Change	Abs Change
f[CL_isv]	0.0100	0.2834	27.3438	0.2734
f[CL_iov]	0.0100	0.0105	0.0518	0.0005