BayesianFROC (version 0.2.1)

Simulation_Based_Calibration_via_rstan_sbc_MRMC: Simiulation Based Calibration (SBC) for a single reader and a single modality case

Description

Implements the SBC algorithm for the a single reader and a single modality case.

Usage

Simulation_Based_Calibration_via_rstan_sbc_MRMC(ww = -0.81,
  www = 0.001, mm = 0.65, mmm = 0.001, vv = 5.31, vvv = 0.001,
  zz = 1.55, zzz = 0.001, A_mean = 0.6, A_variance = 0.1,
  vv_hyper_v = 0.05, vvv_hyper_v = 0.01, NL = 259, NI = 57,
  C = 3, M = 5, Q = 4)

Arguments

ww

A real number representing parameter of prior, indicating mean of prior for the first threshold

www

A real number representing parameter of prior, variance of prior for the first threshold

mm

A real number representing parameter of prior, mean of prior for the mean of signal distribution

mmm

A real number representing parameter of prior, variance of prior for the variance of signal distribution

vv

A real number representing parameter of prior, mean of prior for the mean of signal distribution

vvv

A real number representing parameter of prior, variance of prior for the variance of signal distribution

zz

A real number representing parameter of prior, mean of prior for the differences of thresholds

zzz

A real number representing parameter of prior, variance of prior for the differences of thresholds

A_mean

A real number representing parameter of prior, indicating mean of prior for the A

A_variance

A real number representing parameter of prior, indicating mean of prior for the A

vv_hyper_v

A real number representing parameter of prior, indicating mean of prior for the hyper_v

vvv_hyper_v

A real number representing parameter of prior, indicating variance of prior for the hyper_v

NL

number of lesions

NI

numver of images

C

number of confidence levels

M

number of modalities

Q

number of readers

Value

A list of S3 class "sbc", which is an outputs of the sbc function in rstan.

Details

The implementation is done using the rstan::sbc. The stan file is SBC.stan

References

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

See Also

rstan::sbc, which implements SBC.

Stan file: SBC_MRMC.stan

Examples

# NOT RUN {

#----------------------------------------------------------------------------------------
#               MRMC              SBC via rstan::sbc
#----------------------------------------------------------------------------------------


#  Provides an Simulatiation Based Calibration for validation of our sampling.
#  We can confirmed that my model has very exact MCMC sampling.
#  SBC require suitable priors, and for the author, it seems very informative priors.
#  If we do not use the informative priors, then the odd data are generated from
#  the likelihood with the parameters drawn from priors. Such odd data has not fitted
#  our model, causing odd sampling.
#  If we do not choose the informative priors in suitable way, then it causes bias
#  in model. Even if the MCMC sampling is good in the sence of SBC, but the choise of
#  priors has no reasen, then it will cause bias. So, the author of this package
#  consider that  the bias of MCMC sampling and the bias of priors are trade off.
#  I write this program with no good condition of health or not good environment,
#  I want to die, I want to die, with great pain pain pain pain pain pain die
#  not enough money. So I write this with pain, pain in body, pain in life, pain in
#  money. So, this program let me be happy? I have to live. I must live.
#  All my pains let me take a CT images of my brain, my body, my teeth.
#  So, I have many CT images of mine, so I want to include them, but thier size
#  is very big, thus, I cannot. So, some section of CT iamge will be aploaded.
#  Healthy condition gives us wings for life. Pains gives us pain and small life.
#  I need wings to work or walk or write or calculation of mathematics. I must live.
#  Many reviewer gives me wrong or misunderstand comments. I won't hear them anymore.
#  So, I upload this program to avoid such damm comments. Fack. My life is damm damm.

# The default is three confidence levels,

# Now, I have no internet environment, thus I cannot gives the reference.
# Please search with internet for the details of SBC.
# I won't, won't, won't, ... 2019 July 18 with pain.

# I have no money for research no envoronment, no books, damm. Amateur. Amateur.

# }
# NOT RUN {
fit<-
Simulation_Based_Calibration_via_rstan_sbc_MRMC(
               ww=-0.81,www =0.001,
               mm=0.65,mmm=0.001,
               vv=5.31,vvv=0.001,
               zz= 1.55,zzz=0.001 )


# }
# NOT RUN {
#donttest


# }