hits_rate_creator: MRMC Hit Rates Creator from Thresholds, Mean and S.D.

In BayesianFROC: FROC Analysis by Bayesian Approaches

Description

From thresholds, data of hit rate are created.

Note that the return values has changed from p (in R notation:ppp) to

hit rate_c := \frac{p_c(θ)}{1- p_C(θ)-p_{C-1}(θ)- ... - p_{c+1}(θ)}

Usage

hits_rate_creator(
  z.truth = BayesianFROC::z_truth,
  mu.truth = BayesianFROC::mu_truth,
  v.truth = BayesianFROC::v_truth,
  is_hit_rate_adjusted = FALSE
)

Arguments

z.truth	Vector of dimension = C represents the thresholds of bi-normal assumption.
mu.truth	array of dimension (M,Q). Mean of the signal distribution of bi-normal assumption.
v.truth	array of dimension (M,Q). Standard Deviation of represents the signal distribution of bi-normal assumption.
is_hit_rate_adjusted	whether the return value is a vector of p_c(θ) or hit rate_c := \frac{p_c(θ)}{1- p_C(θ)-p_{C-1}(θ)- ... - p_{c+1}(θ)} . The former is the default (FALSE) and the later is returned if is_hit_rate_adjusted=TRUE.

Value

A vector of the hit rate:

hit rate_c := \frac{p_c(θ)}{1- p_C(θ)-p_{C-1}(θ)- ... - p_{c+1}(θ)}

Do not confuse the old version ppp which is an array with three indices: ppp[C,M,Q].

Examples

## Not run: 
#================The first example======================================

#     Using default values for hit rates, we can create a data of hits as follows:

        hits.rate <-hits_rate_creator()

#================The second example======================================

#     Using the hit rate from the hits_rate_creator(), we can get the hits data:

        hits_creator_from_rate(p.truth =hits_rate_creator() )

#================The remark for example======================================

# The author does not show how to specify the hit rates or threshods.
# For the details of it, please see the default values of such a quantities.


#================The 4-th example======================================

    p.truth.array <- hits_rate_creator()



#========================================================================================
#2019 Sept 6
#========================================================================================








## End(Not run)# dottest

BayesianFROC documentation built on Jan. 23, 2022, 9:06 a.m.