# NOT RUN {
#REAL-LIFE EXAMPLES
#
#PLEASE NOTE THAT BOTH EXAMPLES BELOW ASSUME THE EXISTANCE OF POSTERIOR
#SAMPLES OBTAINED FROM THE 'HSROC' FUNCTION.
#IN OTHER WORDS' ONE NEEDS TO RUN THE 'HSROC' FUNCTION BEFORE USING THE
#'HSROCSUmmary' FUNCTION.
#
#Example 1
#To get descriptive statistics and graphical summaries for the MRI data
#(Scheidler et al. 1997) after dropping the first 5,000 iterations.
data(MRI) #load the data
# }
# NOT RUN {
HSROCSummary(data = MRI, burn_in=5000, print_plot=TRUE )
# }
# NOT RUN {
#Example 2
#To get descriptive statistics and graphical summaries for the In.house
#data (Pai et al. 2004) coming from 2 different chains.
#We provide the path to each chain's directory, i.e. the directory where
#all files created during the Gibbs sampler process are stored for
#each chain. Let's assume there are two fictional directoies
#chain_path = list("C:/path_to_chain_1", "C:/path_to_chain_2").
#Let's assume we drop the first 5,000 iterations and we use a thinning
#interval of 10.
data(In.house) #load the data
# }
# NOT RUN {
HSROCSum1<- HSROCSummary(data = In.house, burn_in=5000, Thin=10,
chain=chain_path, print_plot=TRUE,
sub_rs=REFSTD )
#For more help on this function, see the tutorial pdf file availalbe
#at http://www.nandinidendukuri.com/filesonjoomlasite/HSROC_R_Tutorial.pdf
# }
# NOT RUN {
# }
# NOT RUN {
# }
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