Learn R Programming
⚠️There's a newer version (1.0.0) of this package.Take me there.

__________________________________

  • First of all, you know, I love you.
  • Next, You love me too. I know.
  • Anyway, this pkg is nothing but the following mad function. It is sufficient to understand this fucking pkg.

                           library(BayesianFROC)
                           BayesianFROC::fit_GUI_Shiny() #or fit_GUI_Shiny_MRMC()

The following description is redundant, so I should omit it.

Because I am a fuking homeless,

To avoid that readers are bothered to read the following, the author puts Buffer zone.

___________________________________

####__ Honesty,

####__ Such a lonely word ###__ Everyone is so untrue ###__ ###__ Honesty ###__ Hardly ever heard ###__ ###__ mostly what i need from you ###__

__________________________________

###__________________________________ ###__________________________________ ###__________________________________ ###__________________________________ ###__________________________________ ###__________________________________ ###__________________________________ ###__________________________________ ####__________________________________

Installation

Available from CRAN .


              install.packages("BayesianFROC")
              
              
#     Please execute it from the R console (or the R studio console), which installs the released version of `BayesianFROC`

GUIs via Shiny

A single reader and a single modality (SRSM) case


                           library(BayesianFROC)
                           BayesianFROC::fit_GUI_Shiny()

To fit a model to the SRSM data, fit_a_model_to() would be adequate for the purpose.

Multiple Readers and Multiple Modalities Case


                           library(BayesianFROC)
                           BayesianFROC::fit_GUI_Shiny_MRMC()

For details

  • See vignette (Because package size restriction (< 5Mb), it is omitted.)

  • A pre-print: Bayesian Models for Free-response Receiver Operating Characteristic Analysis

Goal of this package BayesianFROC

Comparison of imaging modality. In some context, modality is imaging methods: MRI, CT, PET,…etc, and the another context, if images are taken for treatment (case) group and untreatment (or another treatment) (control) group, then modality means efficacy of treatment.

  • Fit a model to data whose types are the following:
    • Single Reader and Single Modality case.
    • Multiple Reader and Multiple modality case (MRMC)
  • Comparison of the modalities by AUC (the area under the AFROC curve).

Work flow

  • Data
    • make data from Jafroc data
    • make data from scratch
  • Fit
    • draw FROC curves using a fitted model object
    • Calculates AUCs
  • Validation
    • goodness of fit
    • posterior predictive p value (PPP)
    • SBC
    • comparison of truth using synthesized datasets from a fixed model as a truth

An example dataset to be fitted a model

Confidence LevelNumber of HitsNumber of False alarms
3 = definitely present971
2 = equivocal3214
1 = questionable3174

where hit means the number of True Positive, briefly TP, and false alarm the number False Positive, FP, respectively.


#0) To avoid the following error in Readme file,
#I have to attach the Rcpp. 
#I do not know why such error occur withou Rcpp. 
#This error occurs only when I run the following R scripts from readme.

#Error
#in do.call(rbind,sampler_params) :second argument must be a list Calles:<Anonymous>...get_divergent_iterations ->sampler_param_vector =. do.call Execution halted

 library(Rcpp)  # This code can remove the above unknown error, if someone know why the error occur, please tell me.
 library(BayesianFROC)


#1) Build  data for single reader and single modality  case.




  dataList <- list(c=c(3,2,1),     # c is ignored, can omit.
              h=c(97,32,31),
              f=c(1,14,74),
              NL=259,
              NI=57,
              C=3)





#  where,
#        c denotes confidence level, each components indicates that 
#                3 = Definitely lesion,
#                2 = subtle,  
#                1 = very subtle
#        h denotes number of hits 
#          (True Positives: TP) for each confidence level,
#        f denotes number of false alarms
#          (False Positives: FP) for each confidence level,
#        NL denotes number of lesions (signal),
#        NI denotes number of images,


  
  
  
  
  
  
  
  
  
  
  
  
  
#2) Fit the FROC model.


 
   fit <- BayesianFROC::fit_Bayesian_FROC(
     
            # data to which we fit a model                 
                dataList = dataList,
                                        
            # The number of MCMC chains                         
                     cha = 1,
            
            # The number of MCMC samples for each chains                         
                    ite  = 555,
                    
            # The number of warming up of MCMC simulation for each chains           
                     war = 111,
            
            # Show verbose summary and MCMC process
                 summary = TRUE  )

                  
                  
                  
                  
                  
                  

#  validation of fit via calculation of p -value of the chi square goodness of fit, which is 
#  calculated by integrating with  predictive posterior measure.
                  
                  
plot_dataset_of_ppp( fit )
                   
# The author thinks it is probably coded  correctly, so it needs validation of program

Jafroc (a software)

In order to apply the functions in this package to an xlsx file representing a dataset formulated for Jafroc, use the following code;

     dataList <- convertFromJafroc(
                                  No.of.Modalities =5,
                                  No.of.readers    =4,
                                  No.of.confidence.levels = 5
                                    )

where it requires to specify the number of modalities, readers, confidence levels.

Using the above code, an object is created from an xlsx file.

The FROC curve

Using the fitted model object fit of class stanfitExtended, we can draw the FROC curve (or AFROC curve) as follows;

# new.imaging.device = FALSE  is used to include the output image 
# in this README file, so I recommand new.imaging.device = TRUE
BayesianFROC::DrawCurves(fit,
                         new.imaging.device = FALSE)

To draw the curve in white background, use the followings

# new.imaging.device = FALSE  is used to include the output image 
# in this README file, so I recommand new.imaging.device = TRUE.

BayesianFROC::DrawCurves(fit,
                         Colour = FALSE,
                         new.imaging.device = FALSE)

Executing the above code, an imaging device will appears in which there are circles indicating the so-called False Positive Fractions (FPFs) and True Positive Fractions (TPFs). In addition, an FROC curve is plotted. FROC curve thorough exactly the expected points of FPFs and TPFs. Thus we can intuitively confirm the goodness of fit by comparing the circles and the curve. Ha,… someone reads this boring vignettes? My right arm ache bothering me for 20 months. Ha,… I want to die. I developed theory and package, but this research cannot make me happy, cannot change anything about my poor life… ha.

Latent Distributions

Hit rates are determined the areas of signal Gaussian between thresholds,

and false alarm rate are defined by the areas of differential logarithmic cumulative Gaussian between thresholds.

False Rate

# new.imaging.device = FALSE  is used to include the output image 
# in this README file, so I recommend new.imaging.device = TRUE

BayesianFROC::draw_bi_normal_version_UP(
    fit,
    new.imaging.device      = F,
    dark_theme              = T,
    hit.rate                = F,
    false.alarm.rate        = T,
    both.hit.and.false.rate = F)

Hit Rate

# new.imaging.device = FALSE  is used to include the output image 
# in this README file, so I recommend new.imaging.device = TRUE

BayesianFROC::draw_bi_normal_version_UP(
    fit,
    new.imaging.device      = F,
    dark_theme              = T,
    hit.rate                = T,
    false.alarm.rate        = F,
    both.hit.and.false.rate = F)

One will see that the bi normal assumption is wrong in the FROC context, and instead of bi normal assumption, we use two latent distributions, one is Gaussian for signal and another is the differential logarithmic Gaussian introduced first by the author of this package. For details, see vignettes of this package.

Modality Comparison

By fitting hierarchical Bayesian model, we can get the characteristics such as AUCs for each imaging modality (MRI,PET,CT,etc) to compare modalities.

Using the data object named BayesianFROC::dataList.Chakra.Web representing multiple modality data, we will fit the model to data by the following R script. For letting the running time be short, we take small MCMC iteration, that is, ite =222 which is too small to obtain reliable estimates. I think it should be ite =33333 for actual data analysis or compatible result with Jafroc.

The author try to remove eval=FALSE, but it cause stopping of knitr, so I can not include the following code. The following code sometimes crash R session, so,… it is heavy for README file??



#0) To avoid the following error I have to attach the Rcpp. I do not know why such error occur withou Rcpp.
#Error in do.call(rbind,sampler_params) :second argument must be a list Calles:<Anonymous>...get_divergent_iterations ->sampler_param_vector =. do.call Execution halted

library(Rcpp)  # This code can remove the above unknown error, if someone know why the error occur, please tell me.


library(BayesianFROC)



dataList <- dataList.Chakra.Web

fitt <- BayesianFROC::fit_Bayesian_FROC(
  
  # data of multiple reader and multiple case (modalities)
 dataList =   dataList,
  
  # iteration of MCMC
  ite = 1111 # Should be ite = 33333
 )

Now, we obtain the fitted model object named fit which is an S4 object of class stanfitExtended inherited from stanfit of the rstan package..

Transform of S4 Class for other packages

To apply the functions of other package such as rstan or ggmcmc, …, etc in which there are functions for object of class stanfit, e.g., rstan::stan_trace(), rstan::stan_dens(),rstan::check_hmc_diagnostics(),…etc, we have to change the class of the fitted model object by the following manner:

 fit.stan <- methods::as(fit, "stanfit")

Then the above object fit.stan is an object of the class stanfit and thus we can apply the function of rstan package, e.g. in the following manner; rstan::stan_dens(fit.stan).

Prepare pipe operator (redundant)

# First, get pipe operator
# `%>%` <- utils::getFromNamespace("%>%", "magrittr")

Change the class to stanfit

# Change the class from stanfitExtended to stanfit
fit.stan <- methods::as(fit,"stanfit")

trace plot for object of class stanfit


# Change the class from stanfitExtended to stanfit
#fit.stan <- methods::as(fit,"stanfit")


# Plot about MCMC samples of paremeter name "A", reperesenting AUC
# ggmcmc::ggs(fit.stan) %>% ggmcmc::ggs_traceplot(family    = "A")

posterior density of parameter A stored in an object of class stanfit

The following plot indicates that maximal posterior estimator (MAP) is very unstable in each chain in this iteration. By drawing more samples, it become stable?


# Change the class from stanfitExtended to stanfit
#fit.stan <- methods::as(fit,"stanfit"
                        
                        
# ggmcmc::ggs(fit.stan) %>% ggmcmc::ggs_density(family  = "A")

Auto correlation for an object of class stanfit



# Change the class from stanfitExtended to stanfit
# fit.stan <- methods::as(fit,"stanfit")


# ggmcmc::ggs(fit.stan) %>% ggmcmc::ggs_autocorrelation(family  = "A")

For fitted model object fit.stan of class stanfit, there is a GUI viewer


# Change the class from stanfitExtended to stanfit
fit.stan <- methods::as(fit,"stanfit")


# shinystan::launch_shinystan(fit.stan)

Goodness of fit via posterior predictive p value

Evaluates a p value of chi square goodness of fit. In addition, the scatter plot are drawn which shows the replicated datasets from the posterior predictive p value of the data which is used to create a fitted model object fit.

# Makes a fitted model object, i.e., a stanfit object, in which one can figure out there is a pretty cute p values for each MCMC samples calculated in generatid quatinties block of Stan file/

f <- fit_Bayesian_FROC( ite  = 1111, summary = TRUE,  cha = 1, dataList = dataList.Chakra.1 );

# Plot datasets for calculations of the posterior prediciteve p value of the chi square goodness of fit
plot_dataset_of_ppp(f)

In previous release, my program for ppp was wrong, so in the current version I fixed.

SBC

Validation of model via Simulation Based Calibration (SBC)

Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018). Validating Bayesian Inference Algorithms with Simulation-Based Calibration. arXiv preprint arXiv:1804.06788

BayesianFROC::Simulation_Based_Calibration_single_reader_single_modality_via_rstan_sbc()

Errors of Estimator

Errors of estimates decrease monotonically with respect to sample size.

The author investigate the relation between the sample size and the error of estimates. Accuracy of estimates are depend on the sample size. Large sample size leads us to small error. However, in practical perspective, the number of images or lesions has limitation. The author thinks it is better to obtain 100 images or lesions. And 100 images or lesions gives us the error 0.01 in AUC.

library(BayesianFROC)

a <-BayesianFROC::error_srsc(NLvector = c(
33L,
50L,
111L,
11111L,
1111111L,
111111111L,
999999999L),
# NIvector,
ratio=2,
replicate.datset =3,# This should be more large, e.g. 100 or 200. Larger is better.
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38),
ite =222
)

X axis is sample size and Y axis is error of estimates.

BayesianFROC::error_srsc_error_visualization(a)

X axis is sample size and Y axis is variance of estimates.

BayesianFROC::error_srsc_variance_visualization(a)

Appendix

The author add the program to calculate the event that one is diseased under the condition that diagnosis is positive.



#========================================================================================
#  If Sensitivity and Specificity is larger, then, the probability is also larger
#========================================================================================


x <- stats::runif(100,0,1)
y <- CoronaVirus_Disease_2019_prevalence(0.1,x,x)

dark_theme(4)
plot(x,y)


#========================================================================================
#  If the prevalence is larger, then, the probability is also larger
#========================================================================================



x <- stats::runif(100,0,1)
y <- CoronaVirus_Disease_2019_prevalence(x,0.9,0.9)

dark_theme(4)
plot(x,y)

Acknowledge

Dona nobis pacem

R.I.P. Leibriya Riyakoboch

Now, ….

The author is a homeless, so, please employ me,,, send me a mail whose address is in the page :’-D.

The author also diseased from multiple chemical sensitivity caused the NO/ONOO- cycle and the initiating toxicant is the synthetic detergent (i.e., syndet) which makes very many prurigo nodularises in all of my body for more than two years and a half.

My nervous system and the immune system have seriously damaged by the synthetic detergent (i.e., syndet). However the company making the synthetic detergent (i.e., syndet) never

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Version

Install

install.packages('BayesianFROC')

Monthly Downloads

88

Version

0.4.0

License

MIT + file LICENSE

Maintainer

Issei Tsunoda

Last Published

November 26th, 2020

Functions in BayesianFROC (0.4.0)

Confirm_hit_rates_are_correctly_made_in_case_of_MRMC

Check whether each hit-rate is defined correctly
Chi_square_goodness_of_fit_in_case_of_MRMC_Posterior_Mean

Chi square statistic (goodness of fit) in the case of MRMC at the pair of given data and each MCMC sample
CoronaVirus_Disease_2019_prevalence

Who should be inspected?
CoronaVirus_Disease_2019

Who should be inspected?
AFROC_curve

FROC curve as an embedding map
Close_all_graphic_devices

Close the Graphic Device
AFROC

AFROC curve (alternative free-response ROC curve)
ConfirmConvergence

Check R hat criterion
Author_vs_classic_for_AUC

validation of AUC calculation
BayesianFROC

Theory of FROC Analysis via Bayesian Approaches
Draw_a_prior_sample

Draw One Sample from Prior
DrawCurves_MRMC

Draw the FROC curves for all modalities and readers
DrawCurves_MRMC_pairwise

Draw the FROC curves with Colour
Draw_a_simulated_data_set

Draw a simulated dataset from model distributions with specified parameters from priors
DrawCurves_MRMC_pairwise_col

Draw the FROC curves with Colour
DrawCurves_MRMC_pairwise_BlackWhite

Draw the FROC curves without colour
DrawCurves_srsc

Draw the FROC curves
Draw_AUC

Draw the Region of AUC of AFROC
Credible_Interval_for_curve

Draw FROC curves which means credible interval.
Draw_a_simulated_data_set_and_Draw_posterior_samples

Draw a dataset and MCMC samples
DrawCurves

Draw the FROC curves
argMin

Arg Min: Extract a subscript corresponding component is a minimal
Draw_an_area_of_AUC_for_srsc

Draw a Region of the area under the AFROC curve
array_easy_example

Example array
ROC_data_creator

Synthesize ROC data
Phi

The Cumulative distribution function \(\Phi(x)\) of the Standard Gaussian, namely, mean = 0 and variance =1.
Phi_inv

Inverse function of the Cumulative distribution function \(\Phi(x)\) of the Standard Gaussian. where \(x\) is a real number.
array_of_hit_and_false_alarms_from_vector

Array of hits and false alarms; 2019 Jun 18
R_hat_max

Max R hat
Simulation_Based_Calibration_single_reader_single_modality_via_rstan_sbc

Simulation Based Calibration (SBC) for a single reader and a single modality case
Simulation_Based_Calibration_histogram

Draw a histogram of the rank statistics
StatisticForANOVA

Statistic for ANOVA
TRUE.Counter.in.vector

Count TRUE in a Vector whose components are all Logical R objects
chi_square_goodness_of_fit

Chi square goodness of fit statistics at each MCMC sample w.r.t. a given dataset.
chi_square_goodness_of_fit_from_input_all_param

Calculates the Goodness of Fit (Chi Square)
Simulation_Based_Calibration_via_rstan_sbc_MRMC

Simiulation Based Calibration (SBC) for a single reader and a single modality case
data.nonconverge.srsc

Non-Convergent Data: Single reader and Single modality
data.hier.ficitious

Multiple reader and Multiple modality data
check_hit_is_less_than_NL

Chech total hit is less than NL for each reader and each modality
clearWorkspace

Clear Work Space
chi_square_goodness_of_fit_from_input_all_param_MRMC

Chi square in the case of MRMC at a given dataset and a given parameter.
dark_theme

Dark Theme
comparison

model comparison
compile_all_models_in_pkg_BayesianFROC

Compile all stanfiles in pkg BayesianFROC
data.MultiReaderMultiModality

Multiple reader and Multiple modality data
Stan_code_validation

stan code
check_rhat

Diagnosis of MCMC sampling
chi_square_at_replicated_data_and_MCMC_samples_MRMC

chi square at replicated data drawn (only one time) from model with each MCMC samples.
dataList.Low

Data: Single reader and Single modality
create_dataList_MRMC

Creates a Single Dataset in Case of MRMC
convertFromJafroc

Convert .xlsx File of Jafroc into R object
dataList.Chakra.4

Data: A Single Reader and A Single Modality
dataList.Chakra.2

Data: A Single Reader and A Single Modality
dataList.Chakra.3

Data: A Single Reader and A Single Modality
data_low_p_value

low p-value = 0.012 Data: Single reader and Single modality
data_much_low_p_value

low p-value = 0.002 A Single Reader and A Single Modality
create_dataset

Creates a dataset
dataList.divergent.transition.in.case.of.srsc

An FROC Dataset with Divergent Transitions in case of A Single reader and A Single modality
data_of_36_readers_and_a_single_modality

36 readers and a sinle modality data
dataset_creator_by_specifying_only_M_Q

Creates dataset
dataList.Chakra.Web

An FROC Data of Multiple-Reader and Multiple-Modality
dataset_creator_for_many_Readers

create data for MRMC
d

Data: A Single Reader and A Single Modality
demo_Bayesian_FROC

demonstration
dataList.high.ability

Data: A Single Reader and A Single Modality
ddddddd

Multiple reader and 2 modalities data such that all modalities have same AUC.
dataList.Chakra.Web.orderd

An FROC Data of Multiple-Reader and Multiple-Modality
dataList.High

Data: Single reader and Single modality
demo_Bayesian_FROC_without_pause

demonstration without pausing
error_srsc_variance_visualization

Visualization Of variance Analysis
Empirical_FROC_via_ggplot

Empirical FROC curve via ggplot2
error_message

Error Message for Data Format
FROC_curve

FROC curve as an embedding map
draw_latent_noise_distribution

Visualization of the Latent Gaussian for false rates
draw.CFP.CTP.from.dataList

Plot the pairs of CFPs and CTPs
dataset_creator_new_version

Create a Dataset (version 2) Interactively
ddddd

Data of MRMC; Model does converge.
explanation_for_what_curves_are_drawn

Print out about what curves are drawn
explanation_about_package_BayesianFROC

Explanation of this package
extract_EAP_by_array

Extract Etimates Preserving Array Format.
extract_EAP_CI

Extracts Estimates as vectors from stanfit objects
error_message_on_imaging_device_rhat_values

Error message on a plot plane (imaging device)
draw_latent_signal_distribution

Visualization of Latent Gaussians ( Signal Distribution)
ddd

Multiple reader and Multiple modality data
extractAUC

Extract AUC
dataList.low.ability

Data: A Single Reader and A Single Modality
dddddd

Multiple reader and single modality data
file_remove

Execute before submission to delete redandunt files.
fffaaabbb

Package Development tools and memo.
Test_Null_Hypothesis_that_all_modalities_are_same

Test the Null hypothesis that all modalities are same
fit_GUI_Shiny

Fit a model with GUI of Shiny
fit_GUI_Shiny_MRMC

Fit with GUI via Shiny (in case of MRMC)
fit_Null_hypothesis_model_to_

Fit the null model
fit_Bayesian_FROC

Fit a model to data
foo_of_a_List_of_Arrays

Variance of a List of Arrays
fit_GUI

Fit with GUI via Shiny
error_srsc

Validation via replicated datasets from a model at a given model parameter
fit_srsc

fit a model to data in the case of A Single reader and A Single modality (srsc).
error_srsc_error_visualization

Visualization for Error of Estimator
hits_rate_creator

MRMC Hit Rates Creator from Thresholds, Mean and S.D.
fooo

taboo or
dz

Threshold: parameter of an MRMC model
dddd

One reader and Multiple modality data
fit_a_model_to

Fit a model to data
give_name_srsc_CFP_CTP_vector

Give a Name For CTP CFP vector
error_MRMC

Comparison of Estimates and Truth in case of MRMC
ggplotFROC

Draw FROC curves by two parameters a and b
false_and_its_rate_creator

False Alarm Creator for both cases of MRMC and srsc
flat_one_par

Makes array names
ggplotFROC.EAP

Draw FROC curves by two parameters a and b
initial_values_specification_for_stan_in_case_of_MRMC

Initial values for HMC (Hamiltonian Moncte Carlo Markov Chains)
false_and_its_rate_creator_MRMC

MRMC: False Alarm Creator For each Modality and each Reader.
hit_rate_adjusted_from_the_vector_p

hit rate adjusted from a vector p
mu_truth

Mean of signal: parameter of an MRMC model
argMax

Arg Max: Extract a subscript corresponding component is a max
data.bad.fit

Data: Single reader and Single modality
color_message

message with colored item
compare

model comparison
data.SingleReaderSingleModality

Data: A Single Reader and A Single Modality
fit_GUI_dashboard

Fit with GUI via Shiny (Simple version)
give_name_srsc_data

Give a name for srsc data list component
inv_Phi

Inverse function of the Cumulative distribution function \(\Phi(x)\) of the Standard Gaussian. where \(x\) is a real number.
install_imports

Installer.
dataList.Chakra.1

Data: A Single Reader and A Single Modality
mu_truth_creator_for_many_readers_MRMC_data

mu of MRMC model paramter
make_TeX

Make a TeX file for summary
dataList.Chakra.1.with.explantation

Data: A Single Reader and A Single Modality
dd

Multiple Reader and Multiple Modality Data
dataList.one.modality

dataset of Multiple reader and one modality
data_2modaities_2readers_3confidence

data: 2 readers, 2 modalities and 3 confideneces
dd.orderd

Multiple Reader and Multiple Modality Data
plot,stanfitExtended,missing-method

A generic function plot()
plot_curve_and_hit_rate_and_false_rate_simultaneously

Curve and signal distribution and noise d log Phi() for a single reader and a single modality
hits_creator_from_rate

MRMC Dataset Creator From Hit Rate.
plotFROC

Draw FROC curves by two parameters a and b
plot_dataset_of_ppp

plot datasets using calculation of ppp
extract_estimates_MRMC

MRMC: Extract All Posterior Mean Estimates from stanfitExtended object
name_of_param_whose_Rhat_is_maximal

Extract a name of parameter from StanfitExtended object (or stanfit object.)
plot_FPF_TPF_via_dataframe_with_split_factor

Scatter Plot of FPFs and TPFs via Splitting Factor
names_argMax

Extract name from a real vector whose component is the maximal one
make_true_parameter_MRMC

Make a true model parameter and include it in this package
fit_GUI_MRMC_new

Fit an MRMC model to data with Shiny GUI
extract_data_frame_from_dataList_srsc

extract data frame from datalist in case of srsc
extract_data_frame_from_dataList_MRMC

Extract sub data frame from list of FROC data
fit_MRMC

Fit and Draw the FROC models (curves)
fit_GUI_MRMC

Fit with GUI via Shiny in case of MRMC
foo

without double quote
flatnames

from rstan package
extract_parameters_from_replicated_models

Extract Estimates From Replicated MRMC Model
%>>%

Fit a model
plot_FPF_and_TPF_from_a_dataset

Plot FPF and TPF from MRMC data
pairs_plot_if_divergent_transition_occurred

Pairs plot for divergent transition
showGM

the Graphical Model via PKG DiagrammeR for the case of a single reader and a single modality
v

Standard Deviation: parameter of an MRMC model
show_codes_in_my_manuscript

Show R codes used in my manuscript
fit_MRMC_versionTWO

Fit and Draw the FROC models (curves) version2.
hits_false_alarms_creator_from_thresholds

Hits and False Alarms Creator
is_stanfitExtended

Check whether class is stanfitExtended for any R object
hit_generator_from_multinomial

Under Const
hits_from_thresholds

MRMC Hit Creator from thresholds, mean and S.D.
fit_GUI_simple_from_apppp_file

Fit with GUI via Shiny
m_q_c_vector_from_M_Q_C

Creats vectors: m,q,c from integers: M,Q,C
is_length_zero

Is argument of length zero ?
get_samples_from_Posterior_Predictive_distribution

Synthesizes Samples from Predictive Posterior Distributions (PPD).
get_treedepth_threshold

get treedepth threshold
p

Hit Rate: parameter of an MRMC model
v_truth

Standard Deviation: parameter of an MRMC model
print,stanfitExtended-method

A method for a generic function print() for class "stanfitExtended"
pause

Pause for Demo
print_minimal_reproducible_code_in_case_of_MRMC

Show minimal code in MRMC
from_array_to_vector

Transform from an array to a vector
size_of_return_value

Size of R object
rank_statistics_with_two_parameters

Rank Statistics
replicate_MRMC_dataList

MRMC: Replicates Datasets From Threshold, Mean and S.D.
prior_print_srsc

Print What Prior Are Used
v_truth_creator_for_many_readers_MRMC_data

v of MRMC model paramter
replicate_model_MRMC

Replicate Models
validation.dataset_srsc

Error of estimates with respect to truth
small_margin

Margin
get_posterior_variance

Alternative of rstan::get_posterior_mean()
stanfitExtended

stanfitExtended, an S4 class inherited from the S4 class stanfit
is_logical_0

is.logical(0)
metadata_to_fit_MRMC

Create metadata for MRMC data
p_value_visualization

Calculation of P values are visualized
metadata_to_DrawCurve_MRMC

Create metadata for MRMC data
p_value_of_the_Bayesian_sense_for_chi_square_goodness_of_fit

P value for goodness of fit : No longer used in 2019 Oct
metadata_srsc_per_image

Create metadata for MRMC data.
stanfit_from_its_inherited_class

Chage S4 class to stanfit
z_from_dz

Thresholds from its difference
z_truth

Threshold : parameter of an MRMC model
p_truth

Hit Rate: parameter of an MRMC model
plot_test

# Definition of a method for the inherited class stanfitExtended from stanfit
plot_empirical_ROC_curves

Empirical ROC curve
pnorm_or_qnorm

pnorm or qnorm
snippet_for_BayesianFROC

Edit Snippet
print_stanfitExtended

Definition of a method for the inherited class stanfitExtended from stanfit
ppp

MRMC or srsc: Posterior Predictive P value (PPP) for MRMC or srsc.
priorResearch

Research for Prior
mu

Mean of signal: parameter of an MRMC model
ppp_srsc

Calculates PPP for Models of a single reader and a single modality (Calculation is correct! :'-D)
ppp_MRMC

MRMC: Posterior Predictive P value (PPP) for MRMC,
plot_dataset_of_ppp_MRMC

plot datasets using calculation of ppp
sbcc

SBC
plot_empirical_FROC_curves

Plot empirical FROC Curves by traditional ways of ggplot2
seq_array_ind

Makes a Matrix from a vector of itegers
sortAUC

Prints a Ranking for AUCs for MRMC Data
stan_trace_of_max_rhat

a trace plot for a paramter whose R hat is largest
stan_model_of_sbc

Creates an object of class stanfit of SBC
the_row_number_of_logical_vector

Extract the row number from a logical vector
viewdata_MRMC

View MRMC data
trace_Plot

Trace plot
validation.draw_srsc

Draw Curves for validation dataset
viewdata_srsc

Build a table of data in the case of A Single reader and A Single modality (srsc)
viewdata

Build a table of FROC data
prior_predictor

Predict some estimates of parameter
waic

WAIC Calculator
summarize_MRMC

Summarize the estimates for MRMC case
summary_EAP_CI_srsc

Summary
z

Threshold: parameter of an MRMC model
prior_print_MRMC

Print What Prior Are Used