README.md

In BayesianFROC: FROC Analysis by Bayesian Approaches

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

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

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

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 Level | Number of Hits | Number of False alarms | |:-----------------------|:--------------:|:----------------------:| | 3 = definitely present | 97 | 1 | | 2 = equivocal | 32 | 14 | | 1 = questionable | 31 | 74 |

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)

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

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