Nothing
library(BayesianFROC)
# 1) First, we prepare the dataset for a single reader and a single modality.
showGM()
viewdata(dataList.Chakra.1.with.explantation)
# pause()#1 /10 ---- we fit our model to the above data
fit <- fit_Bayesian_FROC( ite = 51, summary = TRUE, cha=1, dataList = dataList.Chakra.1,new.imaging.device = TRUE,DrawAFROCcurve = TRUE )
DrawCurves(fit,Colour = FALSE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE)
DrawCurves(fit,Colour = TRUE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE)
DrawCurves(fit,Colour = TRUE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE, upper_y = 0.7)
DrawCurves(fit,Colour = FALSE)
DrawCurves(fit,Colour = FALSE,DrawAFROCcurve = TRUE)
DrawCurves(fit,Colour = TRUE,DrawFROCcurve = TRUE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE, upper_y = 0.6)
# pause()#2 /10 ---- Examine the bias
# datasets <-error_srsc(
# NLvector = c(100,10000000,1000000000),
# ite = 2222
# )
# pause()#3 /10 ---- p-value
ppp(fit)
# pause()#4 /10 ---- Bi normal assumption ----- High AUC--------
viewdata(dataList.High)
# pause()#5 /10 ----- Fitting (in the high ability case).
fit.High <- fit_Bayesian_FROC(dataList.High,ite = 1111)
# pause()#6 /10 ----- draw a bi normal assumption----- High AUC--------
draw_latent_noise_distribution(fit.High)
# pause()#7 /10 ---- Bi normal assumption ----- Low AUC--------
viewdata(dataList.Low)
# pause()#8 /10 ----- Fitting (in the low ability case).
fit.Low <- fit_Bayesian_FROC(dataList.Low,ite = 1111)
# pause()#9 /10 ----- draw a bi normal assumption----- Low AUC--------
draw_latent_noise_distribution(fit.Low)
message("
# The R scripts used in the demo.
# Graphical representation of the model.
", crayon::bgBlack$cyan$bold$italic$underline(" showGM() "),"
# Show an example dataset
", crayon::bgBlack$cyan$bold$italic$underline(" viewdata(dataList.Chakra.1.with.explantation)"),"
# fit our model to the above data
", crayon::bgBlack$cyan$bold$italic$underline(" fit <- fit_Bayesian_FROC( ite = 51, summary = TRUE, cha=3, dataList = dataList.Chakra.1,new.imaging.device = TRUE,DrawAFROCcurve = TRUE )"),"
# Draw FROC curves using the fitted model object
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = FALSE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE)"),"
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = TRUE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE)"),"
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = TRUE,DrawFROCcurve = FALSE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE, upper_y = 0.7)"),"
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = FALSE)"),"
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = FALSE,DrawAFROCcurve = TRUE)"),"
", crayon::bgBlack$cyan$bold$italic$underline(" DrawCurves(fit,Colour = TRUE,DrawFROCcurve = TRUE ,DrawAFROCcurve = FALSE,DrawCFPCTP = TRUE, upper_y = 0.6)"),"
# Examine the bias in the case of convergence model only.
", crayon::bgBlack$cyan$bold$italic$underline("# datasets <-validation.dataset_srsc_for_different_NI_NL("),"
", crayon::bgBlack$cyan$bold$italic$underline("# NLvector = c(100,10000000,1000000000),"),"
", crayon::bgBlack$cyan$bold$italic$underline("# ite = 2222"),"
", crayon::bgBlack$cyan$bold$italic$underline("# )"),"
# p-value in the Bayesian sence
", crayon::bgBlack$cyan$bold$italic$underline(" ppp(fit)"),"
# From Signal detection theory
# Now we fit model for two distinct datasets.
# One is high obeserver performance, and the another is low.
# Then compare the noise and signal distrubution.
# High ability case separate the noise and signal,
# On the other hand,
# the low ability case, the noise and signal distritbution
# is mixed.
# ---- Bi normal assumption ----- High AUC--------
", crayon::bgBlack$cyan$bold$italic$underline(" viewdata(dataList.High)"),"
# ----- Fitting (in the high ability case).
", crayon::bgBlack$cyan$bold$italic$underline(" fit.High <- fit_Bayesian_FROC(dataList.High,ite = 1111)"),"
# ----- draw a bi normal assumption----- High AUC--------
", crayon::bgBlack$cyan$bold$italic$underline(" draw_latent_noise_distribution(fit.High)"),"
# ---- Bi normal assumption ----- Low AUC--------
", crayon::bgBlack$cyan$bold$italic$underline(" viewdata(dataList.Low)"),"
# ----- Fitting (in the low ability case).
", crayon::bgBlack$cyan$bold$italic$underline(" fit.Low <- fit_Bayesian_FROC(dataList.Low,ite = 1111)"),"
# ----- draw a bi normal assumption----- Low AUC--------
", crayon::bgBlack$cyan$bold$italic$underline(" draw_latent_noise_distribution(fit.Low)"),"
")
# Demo finished !!
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.