Test_Null_Hypothesis_that_all_modalities_are_same

MRMC is the only case in which the function is available for this function.

dataList

To be passed to the function <code>rstan::</code><code><a rd-options="rstan" href="/link/sampling?package=BayesianFROC&version=0.2.3&to=rstan" data-mini-rdoc="rstan::sampling">sampling</a></code>() in rstan. An argument of <code>rstan::</code><code><a rd-options="rstan" href="/link/sampling?package=BayesianFROC&version=0.2.3&to=rstan" data-mini-rdoc="rstan::sampling">sampling</a></code>() in which it is named <code>iter</code>. A positive integer representing the number of samples synthesized by Hamiltonian Monte Carlo method,
and, Default = 10000. If your model could not converge, then raise this number. Must be greater for more reliable estimates.

To be passed to the function <code>rstan::</code><code><a rd-options="rstan" href="/link/sampling?package=BayesianFROC&version=0.2.3&to=rstan" data-mini-rdoc="rstan::sampling">sampling</a></code>() in rstan. An argument of <code>rstan::</code><code><a rd-options="rstan" href="/link/sampling?package=BayesianFROC&version=0.2.3&to=rstan" data-mini-rdoc="rstan::sampling">sampling</a></code>() in which it is named <code>chains</code>. A positive integer representing the number of chains generated by Hamiltonian Monte Carlo method,
and, Default = 1.

Logical: <code>TRUE</code> of <code>FALSE</code>. Whether to print the verbose summary. If <code>TRUE</code> then verbose summary is printed in the R console. If <code>FALSE</code>, the output is minimal. I regret, this variable name should be verbose.

summary

Test null hypothesis that all modalities
 have same observer performance ablity,
 using Bayes factor.

Execute BayesianFROC::fit_GUI_Shiny() (or fit_GUI_Shiny_MRMC()) for a graphical user interface via Shiny. The free-response receiver operating characteristic (FROC) method is a generalization of receiver operating characteristic (ROC) analysis. However, Chakraborty's classical model is non-generative in the sense that it cannot synthesize data. This package aims to modify his models to be generative using a Bayesian approach, and to verify that our models fit practical datasets. In signal detection theory, the number of true positives never exceeds the number of targets. However, this is not explained by any existing model. Thus, in this package, the author contributes to FROC theory by refining Chakraborty<e2><80><99>s model to obtain models that are generative. This modification allows us to use FROC analysis in a general statistical scheme, and as a benefit, our generative model can be applied to calculations of posterior predictive p values that require generation of synthetic datasets from fitted models. Furthermore, this package presents new models for comparison of modalities. Modality comparison is a common problem in radiology, and has been studied extensively. However, in many medical studies, such problems are addressed with non-Bayesian methods such as ANOVA. As a supplementary topic, this work presents a Bayesian model that includes individual differences. With this model, we can account for differences between individual readers when comparing modalities, using Bayesian rather than ML-methods. The author found the existing FROC model in [1] to be non-generative for calculation of posterior predictive p values. Replacing the ML-based method with a Bayesian approach differs from standard practice but provides insight into the problems of existing methods. Please execute the following R scripts from the R (R studio) console, demo(demo_MRMC, package = "BayesianFROC"); demo(demo_srsc, package = "BayesianFROC"); demo(demo_stan, package = "BayesianFROC"); demo(demo_drawcurves_srsc, package = "BayesianFROC"); demo_Bayesian_FROC(); demo_Bayesian_FROC_without_pause(). References: [1] Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Generative Models for free-response receiver operating characteristic analysis. See the vignettes for more details.

Issei Tsunoda

BayesianFROC

FROC Analysis by Bayesian Approaches

Test_Null_Hypothesis_that_all_modalities_are_same function

To be passed to the function <code>rstan::</code><code><a rd-options='rstan' href='sampling'>sampling</a></code>() in rstan. An argument of <code>rstan::</code><code><a rd-options='rstan' href='sampling'>sampling</a></code>() in which it is named <code>iter</code>. A positive integer representing the number of samples synthesized by Hamiltonian Monte Carlo method,
and, Default = 10000. If your model could not converge, then raise this number. Must be greater for more reliable estimates.

To be passed to the function <code>rstan::</code><code><a rd-options='rstan' href='sampling'>sampling</a></code>() in rstan. An argument of <code>rstan::</code><code><a rd-options='rstan' href='sampling'>sampling</a></code>() in which it is named <code>chains</code>. A positive integer representing the number of chains generated by Hamiltonian Monte Carlo method,
and, Default = 1.

Test_Null_Hypothesis_that_all_modalities_are_same: Test the Null hypothesis that all modalities are same

Description

Usage

Arguments

Value

Details