# One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

[Generated automatically as a Fitting summary]

## Model Description

Name:

  d1cmp_cl_isv_naive

Title:

  One Compartment Model with Absorption and no inter-subject Variance f[CL_isv]=0

Author:

  PoPy for PK/PD

Abstract:

Population one Compartment Model with Absorption and Inter-subject Variance
Here f[CL_isv] is not estimated it is set to zero.

Keywords:

  one compartment model; dep_one_cmp_cl

Input Script:

  `d1cmp_cl_isv_naive_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 0.5000 | 0.1818 | 0.3182 | 0.6363
f[CL] | 1.0000 | 2.5519 | 1.5519 | 1.5519
f[V] | 15.0000 | 20.1441 | 5.1441 | 0.3429

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2000 | 0.4965 | 0.2965 | 1.4826
f[ANOISE_STD] | 0.2000 | 0.1279 | 0.0721 | 0.3605

### Compare Variance f[X]

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS_vs_TIME

## Outputs

### Final objective value

    -163.1359

which required 1.17 iterations and took 16.60 seconds

### Fitted f[X] values (after fitting)

    f[KA] = 0.1818
    f[CL] = 2.5519
    f[V] = 20.1441
    f[PNOISE_STD] = 0.4965
    f[ANOISE_STD] = 0.1279
    f[CL_isv] = 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)`

Likelihoods:

  `lx_params.csv (fit)`

## Inputs

Input Data:

  `cx_obs_params.csv`

### Starting f[X] values (before fitting)

    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] = 0.0000