# 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

    -829.1270

which required 1.30 iterations and took 246.45 seconds

### Final fitted fixed effects

    f[KA] = 0.2308
    f[CL] = 1.8474
    f[V1] = 52.2821
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0000, 0.0002, 0.0004 ],
        [ 0.0002, 0.0399, 0.0067 ],
        [ 0.0004, 0.0067, 0.0987 ],
    ]
    f[PNOISE] = 0.1491
    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 | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 0.2308 | 0.7692 | 0.7692
f[CL] | 1.0000 | 1.8474 | 0.8474 | 0.8474
f[V1] | 20.0000 | 52.2821 | 1.6141 | 32.2821

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.1491 | 0.4912 | 0.0491

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 0.0000 | 0.9999 | 0.0500
f[KA_isv;CL_isv] | 0.0100 | 0.0002 | 0.9820 | 0.0098
f[KA_isv;V1_isv] | 0.0100 | 0.0004 | 0.9641 | 0.0096
f[CL_isv;KA_isv] | 0.0100 | 0.0002 | 0.9820 | 0.0098
f[CL_isv] | 0.0500 | 0.0399 | 0.2015 | 0.0101
f[CL_isv;V1_isv] | 0.0100 | 0.0067 | 0.3270 | 0.0033
f[V1_isv;KA_isv] | 0.0100 | 0.0004 | 0.9641 | 0.0096
f[V1_isv;CL_isv] | 0.0100 | 0.0067 | 0.3270 | 0.0033
f[V1_isv] | 0.0500 | 0.0987 | 0.9748 | 0.0487