One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0

[Generated automatically as a Fitting summary]

Inputs

Description

Name:dep_one_cmp_cl_iov_naive Title:One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0 Author:Wright Dose Ltd Abstract:

Population one Compartment Model with Absorption and Inter-occasion Variance

Here f[CL_iov] is not estimated it is set to zero.

Keywords:one compartment model; dep_one_cmp_cl; iov Input Script:dep_one_cmp_cl_iov_naive_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] = 0.0000

Outputs

Final objective value

-203.5526

which required 1.18 iterations and took 593.03 seconds

Final fitted fixed effects

f[KA] = 0.2912
f[CL] = 2.4772
f[V] = 22.5084
f[PNOISE_STD] = 0.4126
f[ANOISE_STD] = 0.0708
f[CL_isv] = 0.1414
f[CL_iov] = 0.0000

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	0.2912	0.4176	0.2088
f[CL]	1.0000	2.4772	1.4772	1.4772
f[V]	15.0000	22.5084	0.5006	7.5084

Compare Noise f[X]

Variable Name	Starting Value	Fitted Value	Prop Change	Abs Change
f[PNOISE_STD]	0.2000	0.4126	1.0630	0.2126
f[ANOISE_STD]	0.2000	0.0708	0.6458	0.1292

Compare Variance f[X]

Variable Name	Starting Value	Fitted Value	Prop Change	Abs Change
f[CL_isv]	0.0100	0.1414	13.1351	0.1314