# Depot One Comp PK with BLQ observations set to 0.5*LLQ

[Generated automatically as a Fitting summary]

## Model Description

Name:

  blq_pk_norm_fit_half

Title:

  Depot One Comp PK with BLQ observations set to 0.5*LLQ

Author:

  PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification)
observations set to 0.5*LLQ (lower limit of quantification).

Keywords:

  tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

  `blq_pk_norm_fit_half.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 1.0467 | 0.0467 | 0.0467
f[CL] | 1.0000 | 1.6694 | 0.6694 | 0.6694
f[V1] | 20.0000 | 81.9258 | 61.9258 | 3.0963

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.3225 | 0.2225 | 2.2247

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 0.2479 | 0.1979 | 3.9573
f[KA_isv;CL_isv] | 0.0100 | 0.0438 | 0.0338 | 3.3760
f[KA_isv;V1_isv] | 0.0100 | 0.1067 | 0.0967 | 9.6689
f[CL_isv;KA_isv] | 0.0100 | 0.0438 | 0.0338 | 3.3760
f[CL_isv] | 0.0500 | 0.0077 | 0.0423 | 0.8452
f[CL_isv;V1_isv] | 0.0100 | 0.0188 | 0.0088 | 0.8834
f[V1_isv;KA_isv] | 0.0100 | 0.1067 | 0.0967 | 9.6689
f[V1_isv;CL_isv] | 0.0100 | 0.0188 | 0.0088 | 0.8834
f[V1_isv] | 0.0500 | 0.0459 | 0.0041 | 0.0814

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

(No population graphs were requested.)

## Outputs

### Final objective value

    28107.5222

which required 1.30 iterations and took 78.22 seconds

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

    f[KA] = 1.0467
    f[CL] = 1.6694
    f[V1] = 81.9258
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.2479, 0.0438, 0.1067 ],
        [ 0.0438, 0.0077, 0.0188 ],
        [ 0.1067, 0.0188, 0.0459 ],
    ]
    f[PNOISE] = 0.3225
    f[ANOISE] = 0.0100

### 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:

  `synthetic_data.csv`

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

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100