FROC Analysis by Bayesian Approaches


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Documentation for package ‘BayesianFROC’ version 0.4.0

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A B C D E F G H I M N P R S T V W Z misc

-- A --

AFROC AF*ROC* curve (alternative free-response *ROC* curve)
AFROC_curve FROC curve as an embedding map
argMax Arg Max: Extract a subscript corresponding component is a max
argMin Arg Min: Extract a subscript corresponding component is a minimal
array Transform from an * _array_* to a * _vector_*
array_easy_example Example array
array_of_hit_and_false_alarms_from_vector Array of hits and false alarms; 2019 Jun 18
Author_vs_classic_for_AUC validation of AUC calculation

-- B --

BayesianFROC Theory of FROC Analysis via Bayesian Approaches

-- C --

check_hit_is_less_than_NL Chech total hit is less than NL for each reader and each modality
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.
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)
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.
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
clearWorkspace Clear Work Space
Close_all_graphic_devices Close the Graphic Device
color_message message with colored item
compare model comparison
comparison model comparison
compile_all_models_in_pkg_BayesianFROC Compile all stanfiles in pkg BayesianFROC
ConfirmConvergence Check R hat criterion
Confirm_hit_rates_are_correctly_made_in_case_of_MRMC Check whether each hit-rate is defined correctly
convertFromJafroc Convert '.xlsx' File of *_Jafroc_* into R object
CoronaVirus_Disease_2019 Who should be inspected?
CoronaVirus_Disease_2019_prevalence Who should be inspected?
create_dataList_MRMC Creates a _Single_ Dataset in Case of MRMC
create_dataset Creates a dataset
Credible_Interval_for_curve Draw FROC curves which means credible interval.

-- D --

d Data: A Single Reader and A Single Modality
dark_theme Dark Theme
data.bad.fit Data: Single reader and Single modality
data.hier.ficitious Multiple reader and Multiple modality data
data.MultiReaderMultiModality Multiple reader and Multiple modality data
data.nonconverge.srsc *Non-Convergent* Data: Single reader and Single modality
data.SingleReaderSingleModality Data: A Single Reader and A Single Modality
dataList.Chakra.1 Data: A Single Reader and A Single Modality
dataList.Chakra.1.with.explantation 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
dataList.Chakra.4 Data: A Single Reader and A Single Modality
dataList.Chakra.Web An FROC Data of Multiple-Reader and Multiple-Modality
dataList.Chakra.Web.orderd An FROC Data of Multiple-Reader and Multiple-Modality
dataList.divergent.transition.in.case.of.srsc An FROC Dataset with *_Divergent Transitions_* in case of A Single reader and A Single modality
dataList.High Data: Single reader and Single modality
dataList.high.ability Data: A Single Reader and A Single Modality
dataList.Low Data: Single reader and Single modality
dataList.low.ability Data: A Single Reader and A Single Modality
dataList.one.modality dataset of Multiple reader and one modality
dataset_creator_by_specifying_only_M_Q Creates dataset
dataset_creator_for_many_Readers create data for MRMC
dataset_creator_new_version Create a Dataset (version 2) Interactively
data_2modaities_2readers_3confidence data: 2 readers, 2 modalities and 3 confideneces
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
data_of_36_readers_and_a_single_modality 36 readers and a sinle modality data
dd Multiple Reader and Multiple Modality Data
dd.orderd Multiple Reader and Multiple Modality Data
ddd Multiple reader and Multiple modality data
dddd One reader and Multiple modality data
ddddd Data of MRMC; Model * _ does _* converge.
dddddd Multiple reader and single modality data
ddddddd Multiple reader and 2 modalities data such that all modalities have same AUC.
demo_Bayesian_FROC demonstration
demo_Bayesian_FROC_without_pause demonstration without pausing
draw.CFP.CTP.from.dataList Plot the pairs of CFPs and CTPs
DrawCurves Draw the FROC curves
DrawCurves_MRMC Draw the FROC curves for all modalities and readers
DrawCurves_MRMC_pairwise Draw the FROC curves with Colour
DrawCurves_MRMC_pairwise_BlackWhite Draw the FROC curves without colour
DrawCurves_MRMC_pairwise_col Draw the FROC curves with Colour
DrawCurves_srsc Draw the FROC curves
Draw_an_area_of_AUC_for_srsc Draw a Region of the area under the AFROC curve
Draw_AUC Draw the Region of AUC of AFROC
Draw_a_prior_sample Draw One Sample from Prior
Draw_a_simulated_data_set Draw a simulated dataset from model distributions with specified parameters from priors
Draw_a_simulated_data_set_and_Draw_posterior_samples Draw a dataset and MCMC samples
draw_latent_noise_distribution Visualization of the Latent Gaussian for false rates
draw_latent_signal_distribution Visualization of Latent Gaussians ( Signal Distribution)
dz Threshold: parameter of an MRMC model

-- E --

Empirical_FROC_via_ggplot Empirical FROC curve via ggplot2
error_message Error Message for Data Format
error_message_on_imaging_device_rhat_values Error message *on a plot plane* (imaging device)
error_MRMC Comparison of Estimates and Truth in case of MRMC
error_srsc Validation via replicated datasets from a model at a given model parameter
error_srsc_error_visualization Visualization for Error of Estimator
error_srsc_variance_visualization Visualization Of variance Analysis
explanation_about_package_BayesianFROC Explanation of this package
explanation_for_what_curves_are_drawn Print out about what curves are drawn
extractAUC Extract AUC
extract_data_frame_from_dataList_MRMC Extract sub data frame from list of FROC data
extract_data_frame_from_dataList_srsc extract data frame from datalist in case of srsc
extract_EAP_by_array Extract Etimates Preserving Array Format.
extract_EAP_CI Extracts Estimates as vectors from stanfit objects
extract_estimates_MRMC MRMC: Extract All Posterior Mean Estimates from stanfitExtended object
extract_parameters_from_replicated_models Extract Estimates From Replicated MRMC Model

-- F --

false_and_its_rate_creator False Alarm Creator for both cases of MRMC and srsc
false_and_its_rate_creator_MRMC MRMC: False Alarm Creator For each Modality and each Reader.
fffaaabbb Package Development tools and memo.
file_remove Execute before submission to delete redandunt files.
fit_a_model_to Fit a model to data
fit_Bayesian_FROC Fit a model to data
fit_GUI Fit with GUI via Shiny
fit_GUI_dashboard Fit with GUI via Shiny (Simple version)
fit_GUI_MRMC Fit with GUI via Shiny in case of MRMC
fit_GUI_MRMC_new Fit an MRMC model to data with Shiny GUI
fit_GUI_Shiny Fit a model with GUI of Shiny
fit_GUI_Shiny_MRMC Fit with GUI via Shiny (in case of MRMC)
fit_GUI_simple_from_apppp_file Fit with GUI via Shiny
fit_MRMC Fit and Draw the FROC models (curves)
fit_MRMC_versionTWO Fit and Draw the FROC models (curves) version2.
fit_Null_hypothesis_model_to_ Fit the null model
fit_srsc fit a model to data in the case of A Single reader and A Single modality (srsc).
flatnames from rstan package
flat_one_par Makes array names
foo without double quote
fooo taboo or
foo_of_a_List_of_Arrays Variance of a List of Arrays
FROC_curve FROC curve as an embedding map
from_array_to_vector Transform from an * _array_* to a * _vector_*

-- G --

get_posterior_variance Alternative of 'rstan::get_posterior_mean()'
get_samples_from_Posterior_Predictive_distribution Synthesizes Samples from Predictive Posterior Distributions (PPD).
get_treedepth_threshold get treedepth threshold
ggplotFROC Draw FROC curves by two parameters a and b
ggplotFROC.EAP Draw FROC curves by two parameters a and b
give_name_srsc_CFP_CTP_vector Give a Name For CTP CFP vector
give_name_srsc_data Give a name for srsc data list component

-- H --

hits_creator_from_rate MRMC Dataset Creator From Hit Rate.
hits_false_alarms_creator_from_thresholds Hits and False Alarms Creator
hits_from_thresholds MRMC Hit Creator from thresholds, mean and S.D.
hits_rate_creator MRMC Hit Rates Creator from Thresholds, Mean and S.D.
hit_generator_from_multinomial Under Const
hit_rate_adjusted_from_the_vector_p hit rate adjusted from a vector p

-- I --

initial_values_specification_for_stan_in_case_of_MRMC Initial values for HMC (Hamiltonian Moncte Carlo Markov Chains)
install_imports Installer.
inv_Phi Inverse function of the Cumulative distribution function Phi(x) of the Standard Gaussian. where x is a real number.
is_length_zero Is argument of length zero ?
is_logical_0 is.logical(0)
is_stanfitExtended Check whether class is _stanfitExtended_ for any R object

-- M --

make_TeX Make a TeX file for summary
make_true_parameter_MRMC Make a true model parameter and include it in this package
metadata_srsc_per_image Create metadata for MRMC data.
metadata_to_DrawCurve_MRMC Create metadata for MRMC data
metadata_to_fit_MRMC Create metadata for MRMC data
mu Mean of signal: parameter of an MRMC model
mu_truth Mean of signal: parameter of an MRMC model
mu_truth_creator_for_many_readers_MRMC_data mu of MRMC model paramter
m_q_c_vector_from_M_Q_C Creats vectors: 'm,q,c' from integers: 'M,Q,C'

-- N --

names_argMax Extract name from a real vector whose component is the maximal one
name_of_param_whose_Rhat_is_maximal Extract a name of parameter from StanfitExtended object (or stanfit object.)

-- P --

p Hit Rate: parameter of an MRMC model
pairs_plot_if_divergent_transition_occurred Pairs plot for divergent transition
pause Pause for Demo
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.
plot-method A generic function 'plot()'
plotFROC Draw FROC curves by two parameters a and b
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
plot_dataset_of_ppp plot datasets using calculation of ppp
plot_dataset_of_ppp_MRMC plot datasets using calculation of ppp
plot_empirical_FROC_curves Plot empirical FROC Curves by traditional ways of 'ggplot2'
plot_empirical_ROC_curves Empirical ROC curve
plot_FPF_and_TPF_from_a_dataset Plot FPF and TPF from MRMC data
plot_FPF_TPF_via_dataframe_with_split_factor Scatter Plot of FPFs and TPFs via Splitting Factor
plot_test # Definition of a method for the inherited class stanfitExtended from stanfit
pnorm_or_qnorm pnorm or qnorm
ppp MRMC or srsc: Posterior Predictive P value (PPP) for MRMC or srsc.
ppp_MRMC MRMC: Posterior Predictive P value (PPP) for MRMC,
ppp_srsc Calculates PPP for Models of a single reader and a single modality (Calculation is correct! :'-D)
print-method A method for a generic function 'print()' for class "'stanfitExtended'"
print_minimal_reproducible_code_in_case_of_MRMC Show minimal code in MRMC
print_stanfitExtended Definition of a method for the inherited class stanfitExtended from stanfit
priorResearch Research for Prior
prior_predictor Predict some estimates of parameter
prior_print_MRMC Print What Prior Are Used
prior_print_srsc Print What Prior Are Used
p_truth Hit Rate: parameter of an MRMC model
p_value_of_the_Bayesian_sense_for_chi_square_goodness_of_fit P value for goodness of fit : No longer used in 2019 Oct
p_value_visualization Calculation of P values are visualized

-- R --

rank_statistics_with_two_parameters Rank Statistics
replicate_model_MRMC Replicate Models
replicate_MRMC_dataList MRMC: Replicates Datasets From Threshold, Mean and S.D.
ROC_data_creator Synthesize ROC data
R_hat_max Max R hat

-- S --

sbcc SBC
seq_array_ind Makes a Matrix from a vector of itegers
showGM the Graphical Model via PKG 'DiagrammeR' for the case of a single reader and a single modality
show_codes_in_my_manuscript Show R codes used in my manuscript
Simulation_Based_Calibration_histogram Draw a histogram of the rank statistics
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_via_rstan_sbc_MRMC Simiulation Based Calibration (SBC) for a single reader and a single modality case
size_of_return_value Size of R object
small_margin Margin
snippet_for_BayesianFROC Edit Snippet
sortAUC Prints a Ranking for AUCs for MRMC Data
stanfitExtended 'stanfitExtended', an S4 class inherited from the S4 class *_'stanfit'_*
stanfit_from_its_inherited_class Chage S4 class to stanfit
Stan_code_validation stan code
stan_model_of_sbc Creates an object of class stanfit of SBC
stan_trace_of_max_rhat a trace plot for a paramter whose R hat is largest
StatisticForANOVA Statistic for ANOVA
summarize_MRMC Summarize the estimates for MRMC case
summary_EAP_CI_srsc Summary

-- T --

Test_Null_Hypothesis_that_all_modalities_are_same Test the Null hypothesis that all modalities are same
the_row_number_of_logical_vector Extract the row number from a logical vector
to Transform from an * _array_* to a * _vector_*
trace_Plot Trace plot
TRUE.Counter.in.vector Count 'TRUE' in a Vector whose components are all Logical R objects

-- V --

v Standard Deviation: parameter of an MRMC model
validation.dataset_srsc Error of estimates with respect to truth
validation.draw_srsc Draw Curves for validation dataset
vector Transform from an * _array_* to a * _vector_*
viewdata Build a table of FROC data
viewdata_MRMC View MRMC data
viewdata_srsc Build a table of data in the case of A Single reader and A Single modality (srsc)
v_truth Standard Deviation: parameter of an MRMC model
v_truth_creator_for_many_readers_MRMC_data v of MRMC model paramter

-- W --

waic WAIC Calculator

-- Z --

z Threshold: parameter of an MRMC model
z_from_dz Thresholds from its difference
z_truth Threshold : parameter of an MRMC model

-- misc --

%>>% Fit a model
====== A generic function 'plot()'