Description Usage Arguments Value Examples

View source: R/Simulation_Based_Calibration.R

To validate that the MCMC procedure is correct or not, we show the histogram of rank statistics. If the resulting histogram is uniformly distributed, then we can conclude that the MCMC sampling is correct. If the histogram is far from uniformity, then the MCMC sampling or specification of priors is not correct or not appropriate.

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`N` |
samples size of the rank statistics. |

`sd` |
Standard Deviation of priors |

`C` |
No. of Confidence levels |

`initial.seed.for.drawing.a.rank.statistics` |
seed |

`fun` |
An one dimensional real valued function defined on the parameter space. This is used in the definition of the rank statistics. Generally speaking, the element of the parameter space is a vector, so the function should be defined on vectors. In my model parameter is mean, standard deviation, C thresholds of the latent Gaussian, so this function should be defined on the C+2 dimensional Euclidean space. |

`NI` |
No. of images |

`NL` |
No. of Lesions |

`initial.seed.for.drawing.a.data` |
seed |

`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 |

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

`DrawCurve` |
Logical: |

samples of rank statistics

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## Not run:
g <-Simulation_Based_Calibration_histogram(N=2,ite = 2222)
graphics::hist(g$rank.statistics)
g <- Simulation_Based_Calibration_histogram(
NI=1111111,
NL=1111111,
# N =100 would be better more than N =10
# But this is only example, we take very small N
N=10,
ite=3333,
sd=1,
initial.seed.for.drawing.a.rank.statistics = 123456789,
DrawCurve = TRUE
)
g <- Simulation_Based_Calibration_histogram(
NI=1111111,
NL=1111111,
# N =100 would be better more than N =10
# But this is only example, we take very small N
N=10,
ite=3333,
sd=1,initial.seed.for.drawing.a.rank.statistics = 123456789,
DrawCurve = TRUE,
C=11)
#======= The Second Example: =================================================
# If you want to see the replicated data, then the following code is available.
# In the following, I extract the dataset which is very small rank statistics, e.g.
# less than 10. And draw the CFP and CTP for observation of dataset.
gggg <- Simulation_Based_Calibration_histogram(
NI=1111111,
NL=1111111,
N=22,
ite=2222)
a <- gggg$rank.statistics<10
aa <- the_row_number_of_logical_vector(a)
draw.CFP.CTP.from.dataList(gggg$fit.list[[ aa[1] ]]@dataList)
## End(Not run)#\dontrun
``` |

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