Description Usage Arguments Details Value See Also Examples

View source: R/draw_latent_distribution.R

Plot the posterior mean
of model parameter *θ* and
and the latent function, i.e.
the differential logarithmic Gaussian *d \log Φ(z)*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |

`StanS4class` |
An S4 object of class `stanfit` .
This R object is a fitted model object
as a return value of the function `fit_Bayesian_FROC()` .
To be passed to |

`dark_theme` |
TRUE or FALSE |

`dig` |
A variable to be passed to the function |

`mesh` |
Mesh for painting the area |

`new.imaging.device` |
Logical: |

`hit.rate` |
whether draws it. Default is |

`false.alarm.rate` |
whether draws it. Default is |

`both.hit.and.false.rate` |
whether draws it. Default is |

`density` |
A natural number, indicating the density of shading lines, in lines per inch. |

`color` |
A color region is selected from black and white only. For more colors, put |

`mathmatical.symbols` |
A logical, whether legend is in plot. |

`type` |
An integer, for the color of background and etc. |

`summary` |
Logical: |

Our FROC model use a latent Gaussian random variable to determine false rates which are defined as follows;

* q_5(z_1,...z_C) = \int_{z5}^{∞} d \log Φ(z)dz*

* q_4(z_1,...z_C) = \int_{z4}^{z5} d \log Φ(z)dz*

* q_3(z_1,...z_C) = \int_{z3}^{z4} d \log Φ(z)dz*

* q_2(z_1,...z_C) = \int_{z2}^{z3} d \log Φ(z)dz*

* q_1(z_1,...z_C) = \int_{z1}^{z2} d \log Φ(z)dz*

For example, in the following data, the number of false alarm data with confidence level 5 **41** which
is considered as an sample from the Poisson distribution of its rate

* q_5(z_1,...z_C) = \int_{z5}^{∞} d \log Φ(z)dz*

So, this Gaussian distribution determines false rate, and this function `draw_latent_noise_distribution()`

plot
this Gaussian distribution *d \log Φ* and
the density * Gaussian(z|μ,σ)* is also plotted to compare hit rates and false rates.
thus, the author implement it in the `draw_latent_signal_distribution()`

,

* Example data:*

* A single reader and single modality case *

——————————————————————————————————

`NI=63,NL=124` | confidence level | No. of false alarms | No. of hits |

In R console -> | ` c` | `f ` | `h` |

----------------------- | ----------------------- | ----------------------------- | ------------- |

definitely present | 5 | 1 | 41 |

probably present | 4 | 2 | 22 |

equivocal | 3 | 5 | 14 |

subtle | 2 | 11 | 8 |

very subtle | 1 | 13 | 1 |

—————————————————————————————————

* *false alarms* = False Positives = FP

* *hits* = True Positives = TP

Information of Latent Gaussians, such as mean and S.D. of the signal distributions and thresholds.

`draw_latent_signal_distribution()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
## Not run:
#========================================================================================
# Shape of signal distribution strongly influences the value of AUC, so in the following
# the author shows how it affects the estimates of AUCs.
# We consider two dataset, one of which is a low AUC and the other is a high AUC.
# In the high AUC case, the Signal Gaussain will be low variance and
# in the low AUC case, the variance will desperse. 2019 August 4, 2019 Dec 17
#========================================================================================
# ----- High AUC case --------
viewdata(dataList.High)
fit.High <- fit_Bayesian_FROC(dataList.High,ite=111)
draw_latent_signal_distribution(fit.High)
# ----- Low AUC case --------
viewdata(dataList.Low)
fit.Low <- fit_Bayesian_FROC(dataList.Low)
draw_latent_signal_distribution(fit.Low)
Close_all_graphic_devices() # 2020 August
## End(Not run)# dottest
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.