# Depot One Comp PK ignoring BLQ observations.

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | blq_pk_norm_fit_ignore
Title: | Depot One Comp PK ignoring BLQ observations.
Author: | J.R. Hartley
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`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

## Outputs

### Final objective value

    -834.3309

which required N. iterations and took 204.98 seconds

### Final fitted fixed effects

    f[KA] = 0.2299
    f[CL] = 1.8348
    f[V1] = 53.1476
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0177, 0.0129, 0.0214 ],
        [ 0.0129, 0.0140, -0.0088 ],
        [ 0.0214, -0.0088, 0.1722 ],
    ]
    f[PNOISE] = 0.1436
    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)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 0.2299 | 1.0000 | 0.7701 | 0.7701
f[CL] | 1.8348 | 1.0000 | 0.8348 | 0.8348
f[V1] | 53.1476 | 20.0000 | 1.6574 | 33.1476

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1436 | 0.1000 | 0.4358 | 0.0436

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0177 | 0.0500 | 0.6459 | 0.0323
f[KA_isv;CL_isv] | 0.0129 | 0.0100 | 0.2928 | 0.0029
f[KA_isv;V1_isv] | 0.0214 | 0.0100 | 1.1421 | 0.0114
f[CL_isv;KA_isv] | 0.0129 | 0.0100 | 0.2928 | 0.0029
f[CL_isv] | 0.0140 | 0.0500 | 0.7201 | 0.0360
f[CL_isv;V1_isv] | -0.0088 | 0.0100 | 1.8827 | 0.0188
f[V1_isv;KA_isv] | 0.0214 | 0.0100 | 1.1421 | 0.0114
f[V1_isv;CL_isv] | -0.0088 | 0.0100 | 1.8827 | 0.0188
f[V1_isv] | 0.1722 | 0.0500 | 2.4436 | 0.1222