# Depot One Comp PK ignoring BLQ observations.

[Generated automatically as a Fitting summary]

## Model Description

Name:

  blq_pk_norm_fit_ignore

Title:

  Depot One Comp PK ignoring BLQ observations.

Author:

  PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification)
observations removed from data set.

Keywords:

  tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

  `blq_pk_norm_fit_ignore.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 0.2348 | 0.7652 | 0.7652
f[CL] | 1.0000 | 1.8185 | 0.8185 | 0.8185
f[V1] | 20.0000 | 54.7515 | 34.7515 | 1.7376

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.1422 | 0.0422 | 0.4223

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 0.0000 | 0.0500 | 0.9997
f[KA_isv;CL_isv] | 0.0100 | -0.0003 | 0.0103 | 1.0329
f[KA_isv;V1_isv] | 0.0100 | -0.0001 | 0.0101 | 1.0083
f[CL_isv;KA_isv] | 0.0100 | -0.0003 | 0.0103 | 1.0329
f[CL_isv] | 0.0500 | 0.0317 | 0.0183 | 0.3658
f[CL_isv;V1_isv] | 0.0100 | -0.0061 | 0.0161 | 1.6135
f[V1_isv;KA_isv] | 0.0100 | -0.0001 | 0.0101 | 1.0083
f[V1_isv;CL_isv] | 0.0100 | -0.0061 | 0.0161 | 1.6135
f[V1_isv] | 0.0500 | 0.1028 | 0.0528 | 1.0553

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

(No population graphs were requested.)

## Outputs

### Final objective value

    -889.8945

which required 1.30 iterations and took 52.27 seconds

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

    f[KA] = 0.2348
    f[CL] = 1.8185
    f[V1] = 54.7515
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0000, -0.0003, -0.0001 ],
        [ -0.0003, 0.0317, -0.0061 ],
        [ -0.0001, -0.0061, 0.1028 ],
    ]
    f[PNOISE] = 0.1422
    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