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# How to use a new method for generic function "print".
#=============================The First Example======================================
#(1)First, we prepare the example data from this package.
dat <- BayesianFROC::dataList.Chakra.1
# The R object named dat is a list which contains the hits and false alarms representing
# an FROC dataset. To confirm it, the function viewdata() can be used;
viewdata(dat)
#(2)Second, we run fit_Bayesian_FROC() in which the rstan::sampling() is implemented.
#Fit to data named "dat" the author's Bayesian model by
fit <- fit_Bayesian_FROC(dat)
#(3)Thirdly, we obtain the R object fit of S4 class
# named stanfitExtended that is an inherited class from the S4 class stanfit
# defined in the package rstan.
# For the S4 class stanfitExtended defined in this package, we can use
# the generic function print for this new S4 class.
print(fit)
# To use the generic functin print() as a object of class "stanfit",
# we coerce class of fit into stanfit from stanfitExtended as follows;
fitt <- methods::as(fit,"stanfit")
# THe R object "fitt" is a fitted model object of class stanfit,
# thus we can also apply the generic function print() as follows:
print(fitt)
#=============================The Second Example======================================
#(1)First, we prepare the example data from this package.
dat <- BayesianFROC::dataList.Chakra.Web
#(2)Second, we run fit_Bayesian_FROC() in which the rstan::sampling() is implemented.
#Fit to data named "dat" the author's Bayesian model by
fit <- fit_Bayesian_FROC(dat)
#(3)Thirdly, we obtain the R object fit of S4 class
# named stanfitExtended that is an inherited class from the S4 class stanfit
# defined in the package rstan.
# For the S4 class stanfitExtended defined in this package, we can use
# the generic function print for this new S4 class.
print(fit)
# 2019.05.21 Revised.
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# dottest
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