Nothing
message("
In this demo, some R code is very heavy.
The author provides the all codes here.
")
# Print all codes -----------------
pause(T)
message("
# Non intuitive AUC
dat <- list(
c=c(3,2,1), #Confidence level
h=c(0,5,5), #Number of hits for each confidence level
f=c(1,1,1), #Number of false alarms for each confidence level
NL=10, #Number of lesions
NI=1, #Number of images
C=3) #Number of confidence level
f1 <- fit_Bayesian_FROC( dataList = dat ) #NX=NI
f2 <- fit_Bayesian_FROC( dataList = dat ,ModifiedPoisson = T)#NX=NL
# Error or variance of estimates with respect to sample size $N_L$ -----
a <-error_srsc(NLvector = c(
100,
10000,
1000000,
10000000
),
ratio=2,
replicate.datset =100,
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38),
ite =5000
)
error_srsc_error_visualization(a)
error_srsc_variance_visualization(a)
# Example
fit <- fit_Bayesian_FROC( ite = 1111,
summary = T,
dataList = dataList.Chakra.1.with.explantation,
)
ppp(fit,Colour = F,dark_theme = F)
DrawCurves(fit,title = F,Colour = F,DrawAUC = T,DrawAFROCcurve = T,DrawCFPCTP = T)
draw_latent_noise_distribution( fit,dark_theme = F,color = T)
draw_latent_signal_distribution(fit,dark_theme = F,color = T)
# Modality comparison
f <- fit_Bayesian_FROC( ite = 4111, cha = 1, summary = T, dataList = dd,DrawCurve = T)
ppp(f)
#
citation(\"rstan\")
")
pause(T)
# Non intuitive AUC ------------
dat <- list(
c=c(3,2,1), #Confidence level
h=c(0,5,5), #Number of hits for each confidence level
f=c(1,1,1), #Number of false alarms for each confidence level
NL=10, #Number of lesions
NI=1, #Number of images
C=3) #Number of confidence level
f1 <- fit_Bayesian_FROC( dataList = dat ,ModifiedPoisson = F) #NX=NI
f2 <- fit_Bayesian_FROC( dataList = dat ,ModifiedPoisson = T)#NX=NL
# Error or variance of estimates with respect to sample size $N_L$ -----
a <-error_srsc(NLvector = c(
100,
10000,
1000000,
10000000
),
ratio=2,
replicate.datset =100,
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38),
ite =5000
)
error_srsc_error_visualization(a)
error_srsc_variance_visualization(a)
# Single reader and single modality ----------------
fit <- fit_Bayesian_FROC( ite = 1111,
summary = T,
dataList = dataList.Chakra.1.with.explantation,
)
# Posterior predicitve p-value for single reader and single modality -----
ppp(fit,Colour = F,dark_theme = F)
DrawCurves(fit,title = F,Colour = F,DrawAUC = T,DrawAFROCcurve = T,DrawCFPCTP = T)
draw_latent_noise_distribution( fit,dark_theme = F,color = T)
draw_latent_signal_distribution(fit,dark_theme = F,color = T)
# Modality comparison ----------------
f <- fit_Bayesian_FROC( ite = 4111, cha = 1, summary = T, dataList = dd,DrawCurve = T)
ppp(f)
# Reference -----
citation("rstan")
message("
In this demo, some R code is very heavy.
The author provides the all codes here.
")
# Print all codes -----------------
message("
# Non intuitive AUC ---------------------
dat <- list(
c=c(3,2,1), #Confidence level
h=c(0,5,5), #Number of hits for each confidence level
f=c(1,1,1), #Number of false alarms for each confidence level
NL=10, #Number of lesions
NI=1, #Number of images
C=3) #Number of confidence level
f1 <- fit_Bayesian_FROC( dataList = dat ) #NX=NI
f2 <- fit_Bayesian_FROC( dataList = dat ,ModifiedPoisson = T)#NX=NL
# Error or variance of estimates with respect to sample size $N_L$ -----
a <-error_srsc(NLvector = c(
100,
10000,
1000000,
10000000
),
ratio=2,
replicate.datset =100,
ModifiedPoisson = FALSE,
mean.truth=0.6,
sd.truth=5.3,
z.truth =c(-0.8,0.7,2.38),
ite =5000
)
error_srsc_error_visualization(a)
error_srsc_variance_visualization(a)
# Example ---------------------------------
fit <- fit_Bayesian_FROC( ite = 1111,
summary = T,
dataList = dataList.Chakra.1.with.explantation,
)
ppp(fit,Colour = F,dark_theme = F)
DrawCurves(fit,title = F,Colour = F,DrawAUC = T,DrawAFROCcurve = T,DrawCFPCTP = T)
draw_latent_noise_distribution( fit,dark_theme = F,color = T)
draw_latent_signal_distribution(fit,dark_theme = F,color = T)
# Modality comparison -----------------------------
f <- fit_Bayesian_FROC( ite = 4111, cha = 1, summary = T, dataList = dd,DrawCurve = T)
ff <- fit_Bayesian_FROC( ite = 11111, cha = 1, summary = T, dataList = dd,see = 1234 )
ppp(f)
#
citation(\"rstan\")
")
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