Description Usage Arguments Value Examples
View source: R/chi_square_goodness_of_fit.R
Given parameter and data, the chi square is calculated.
1 2 3 4 5 6  chi_square_goodness_of_fit_from_input_all_param_MRMC(
ppp,
dl,
dataList,
summary = TRUE
)

ppp 
An array of  
dl 
An vector of length  
dataList 
A list, specifying an FROC data to be fitted a model. It consists of data of numbers of TPs, FPs, lesions, images. .In addition, if in case of mutiple readers or mutiple modalities, then modaity ID and reader ID are included also. The For the single reader and a single modality data, the
Using this object To make this R object
Before fitting a model,
we can confirm our dataset is correctly formulated
by using the function ————————————————————————————— A Single reader and a single modality (SRSC) case. ————————————————————————————— In a single reader and a single modality case (srsc),
The detail of these dataset, see the datasets endowed with this package.
'Note that the maximal number of confidence level, denoted by data Format: A single reader and a single modality case ——————————————————————————————————
————————————————————————————————— * false alarms = False Positives = FP * hits = True Positives = TP Note that in FROC data, all confidence level means present (diseased, lesion) case only, no confidence level indicating absent. Since each reader marks his suspicious location only if he thinks lesions are present, and marked positions generates the hits or false alarms, thus each confidence level represents that lesion is present. In the absent case, reader does not mark any locations and hence, the absent confidence level does not relate this dataset. So, if reader think it is no lesion, then in such case confidence level is not needed. Note that the first column of confidence level vector ————————————————————————————— Multiple readers and multiple modalities case, i.e., MRMC case ————————————————————————————— In case of multiple readers and multiple modalities, i.e., MRMC case,
in order to apply the function
Note that the maximal number of confidence level (denoted by the function Example data. Multiple readers and multiple modalities ( i.e., MRMC) —————————————————————————————————
————————————————————————————————— * false alarms = False Positives = FP * hits = True Positives = TP  
summary 
Logical: 
A list, contains χ^2(Dataθ), where Data and θ are specified by user.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38  ## Not run:
#========================================================================================
# 0)
#========================================================================================
# Chi square depends on data and model parameter, thus what we have to do is:
# prepare data and parameter
# In the follwoing, we use data named ddd as an example to be fitted a model,
# and use posterior mean estimates as model parameter.
# To do so, we execute the following code
# to run the HMC algorithm for the data named ddd
fit < fit_Bayesian_FROC( dataList = ddd, ite = 51 )
# In the resulting object named fit, the posterior samples are retained.
#========================================================================================
# 1) hit rate and false alarm rate
#========================================================================================
e < extract_estimates_MRMC(fit);
dl < e$dl.EAP;
ppp < e$ppp.EAP;
#========================================================================================
# 2) Calculates chi square using above hit rate and false alarm rate and data named ddd
#========================================================================================
chi_square_goodness_of_fit_from_input_all_param_MRMC(ppp,dl,ddd)
## End(Not run)# dontrun

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