Description Usage Arguments Details Value Examples

View source: R/fit_Bayesian_FROC.R

Build a *fitted model object* in case of **single reader
and single modality** data `dataList`

. FPF is **per image**.

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 35 | ```
fit_srsc(
dataList,
prior = -1,
new.imaging.device = TRUE,
dataList.Name = "",
ModifiedPoisson = FALSE,
model_reparametrized = FALSE,
verbose = TRUE,
type_to_be_passed_into_plot = "l",
multinomial = FALSE,
DrawCurve = TRUE,
PreciseLogLikelihood = TRUE,
Drawcol = TRUE,
make.csv.file.to.draw.curve = FALSE,
mesh.for.drawing.curve = 10000,
summary = TRUE,
DrawFROCcurve = TRUE,
DrawAFROCcurve = FALSE,
DrawCFPCTP = TRUE,
cha = 4,
ite = 3000,
dig = 5,
war = floor(ite/5),
see = 1234,
prototype = FALSE,
ww = -0.81,
www = 0.001,
mm = 0.65,
mmm = 0.001,
vv = 5.31,
vvv = 0.001,
zz = 1.55,
zzz = 0.001,
...
)
``` |

`dataList` |
A list, to be fitted a model.
For example, in case of a single reader and a single modality,
it consists of |

`prior` |
positive integer, to select the prior |

`new.imaging.device` |
Logical: |

`dataList.Name` |
This is not for user, but the author for this package development. |

`ModifiedPoisson` |
Logical, that is If .
per lesionSimilarly, If .
per imageFor more details, see the author's paper in which I explained If
where On the other hand, if
where The model is fitted so that
the estimated FROC curve can be ragraded
as the expected pairs of FPF per image and TPF per lesion ( or as the expected pairs of FPF per image and TPF per lesion ( If On the other hand, if So,data of FPF and TPF are changed thus, a fitted model is also changed whether Revised 2019 Dec 8 Revised 2019 Nov 25 Revised 2019 August 28 |

`model_reparametrized` |
A logical, if TRUE, then a model under construction is used. |

`verbose` |
A logical, if |

`type_to_be_passed_into_plot` |
"l" or "p". |

`multinomial` |
A logical, if |

`DrawCurve` |
Logical: |

`PreciseLogLikelihood` |
Logical, that is |

`Drawcol` |
Logical: |

`make.csv.file.to.draw.curve` |
Logical: |

`mesh.for.drawing.curve` |
A positive large integer, indicating number of dots drawing the curves, Default =10000. |

`summary` |
Logical: |

`DrawFROCcurve` |
Logical: |

`DrawAFROCcurve` |
Logical: |

`DrawCFPCTP` |
Logical: |

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

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

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

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

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

`prototype` |
A logical, if
However, this model ( if
This model is theoretically perfect. However, in the practically, the calculation will generates some undesired results which caused by the so-called floo .... I forget English :'-D. The flood point??? I forgeeeeeeeeeeeeet!! Ha. So, prior synthesizes very small hit rates such as 0.0000000000000001234 and it cause the non accurate calculation such as 0.00000,,,00000123/0.000.....000012345= 0.0012 which becomes hit rate and thus OH No!. Then it synthesizes Bernoulli success rate which is not less than 1 !! To avoid this, the author should develop the theory of prior to avoid this very small numbers, however the author has idea but now it does not success. If
On the other hand,
if
Each number of lesions is adjusted
so that the sum of hits
where Using the above two equations, we can establish the alternative Bayesian FROC theory preserving classical notions and formulas. For the details, please see the author's pre print: Bayesian Models for ,,, for?? I forget my paper title .... :'-D. What the hell!? I forget,... My health is so bad to forget , .... I forget. The author did not notice that the prototype is not a generative model. And hence the author revised the model so that the model is exactly generative model. But the reason why the author remains the prototype model( SO, now, the author try to avoid such phenomenon by using priors but it now does not success. Here of course we interpret the terms
such as |

`ww` |
Each of which is a real number specifying one of the parameter of prior |

`www` |
Each of which is a real number specifying one of the parameter of prior |

`mm` |
Each of which is a real number specifying one of the parameter of prior |

`mmm` |
Each of which is a real number specifying one of the parameter of prior |

`vv` |
Each of which is a real number specifying one of the parameter of prior |

`vvv` |
Each of which is a real number specifying one of the parameter of prior |

`zz` |
Each of which is a real number specifying one of the parameter of prior |

`zzz` |
Each of which is a real number specifying one of the parameter of prior |

`...` |
Additional arguments |

Revised 2019.Jun. 17

An S4 object of class `stanfitExtended`

,
which is an inherited S4 class from `stanfit`

.

To change the S4 class, use

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 | ```
## Not run:
#First, prepare the example data from this package.
dat <- get(data("dataList.Chakra.1"))
#Second, fit a model to data named "dat"
fit <- fit_srsc(dat)
# Close the graphic device to avoid errors in R CMD check.
Close_all_graphic_devices()
## End(Not run)# dottest
``` |

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