fit_GUI_Shiny_MRMC: Fit with GUI via Shiny (in case of MRMC)

In BayesianFROC: FROC Analysis by Bayesian Approaches

Description

Fit a Bayesian model with GUI.

Revised 2019 Nov.

Usage

fit_GUI_Shiny_MRMC(
  DF = data.frame(m = as.integer(BayesianFROC::dd$m), q =
    as.integer(BayesianFROC::dd$q), c = as.integer(BayesianFROC::dd$c), h =
    as.integer(BayesianFROC::dd$h), f = as.integer(BayesianFROC::dd$f)),
  DF_MQC = data.frame(M = max(DF$m), Q = max(DF$q), C = max(DF$c)),
  NL.max = 1111,
  NI.max = 1111,
  NL.initial = 142,
  NI.initial = 199,
  seed.initial.of.MCMC = 237410,
  MCMC.chains.max = 4
)

Arguments

DF	A dataframe, cosisting of five vectors: reader ID, modality ID, confidence levels, hits, false alarms. initial data to be fited
DF_MQC	A data frame, consisting of three numbers, i.e., the number of modalities, readers, confidence levels. Of course, these numbers should be compatible with the variable DF.
NL.max	max number of bins indicating the maximal number in which the number of lesions can move
NI.max	max number of bins indicating the maximal number in which the number of imagegs can move
NL.initial	Natural number indicating the initial number of lesions, Default value =142.
NI.initial	Natural number indicating the initial number of images, Default value =199.
seed.initial.of.MCMC	positive integers indicating the initial seed of MCMC sampling. Default is 1234.
MCMC.chains.max	max number of bins indicating number of MCMC chains

Details

In what follows, we assume that our dataset has more than two readers or modalities, namely, our dataset is MRMC case. The term imaging modality, we mean a set of imaging methods such as MRI, CT, PET, etc.

Revised 2019 Nov 25. Revised 2020 Jan

Value

None

Examples

## Not run: 

#'## Only run examples in interactive R sessions
if (interactive()) {
#========================================================================================
#            1)           Use the default User Interface
#========================================================================================
#'

 #No need to consider the variables, it is sufficient in  default values.


 fit_GUI_Shiny()




#========================================================================================
#            2)          From exsisting dataset, named dddddd or ddddd or ddd
#========================================================================================



 fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(dddddd))
 fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(ddddd))
 fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(ddd))




#========================================================================================
#            2)       data of  11 readers and a single modality
#========================================================================================



  d <- dataset_creator_for_many_Readers(1,11)

  fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(d))










#========================================================================================
#                     see = 2345678       convergence 37readers, 1 modality
#========================================================================================



v  <- v_truth_creator_for_many_readers_MRMC_data(M=1,Q=37)
m  <- mu_truth_creator_for_many_readers_MRMC_data(M=1,Q=37)
d  <- create_dataList_MRMC(mu.truth = m,v.truth = v)



fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(d),
                   seed.initial.of.MCMC = 2345678,
                   NL.initial = d$NL,
                   NI.initial = d$NI)








#========================================================================================
#            2)          From exsisting dataset, named dddd
#========================================================================================



 fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(dddd))


  # This dataset named dddd is a dataset consisting of
  #  only a single reader and mutiple modality.
  # Such a single reader and mutiple modality case had error caused
  # by some reduction of array to vector.
  # So, the program was fixed so that such special case is also available
  # 2020 Feb 24

  # To reflect the information of the number of lesions and images,
  # use the following.

  fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(dddd),
  NL.initial = dddd$NL,
  NI.initial = dddd$NI)


#========================================================================================
#                                example
#========================================================================================




v  <- v_truth_creator_for_many_readers_MRMC_data(M=2,Q=7)
m  <- mu_truth_creator_for_many_readers_MRMC_data(M=2,Q=7)
d  <- create_dataList_MRMC(mu.truth = m,v.truth = v)
fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(d))



#========================================================================================
#                             non-convergent   example
#========================================================================================


v  <- v_truth_creator_for_many_readers_MRMC_data(M=3,Q=7)
m  <- mu_truth_creator_for_many_readers_MRMC_data(M=3,Q=7)
d  <- create_dataList_MRMC(mu.truth = m,v.truth = v)
fit_GUI_Shiny_MRMC(DF=extract_data_frame_from_dataList_MRMC(d),seed.initial.of.MCMC = 23)




}### Only run examples in interactive R sessions



## End(Not run)

BayesianFROC documentation built on Jan. 23, 2022, 9:06 a.m.