UtilAnalyticalAucsRSM.RdReturns the ROC, AFROC and wAFROC AUCs corresponding to
specified RSM parameters. See also UtilAucPROPROC,
UtilAucBIN and UtilAucCBM
UtilAnalyticalAucsRSM(mu, lambda, nu, zeta1 = -Inf, lesDistr, relWeights = 0)The mean of the Gaussian distribution for the ratings of latent LLs (continuous ratings of lesions that are found by the search mechanism). The NLs are assumed to be distributed as N(0,1).
The RSM lambda parameter.
The RSM nu parameters.
The lowest reporting threshold, the default is -Inf.
The lesion distribution 1D array, i.e., the probability mass function (pmf) of the numbers of lesions for diseased cases.
The relative weights of the lesions; a vector of
length maxLL; if zero, the default, equal weights are assumed.
The ROC, AFROC and wAFROC AUCs corresponding to the specified parameters
Chakraborty DP (2017) Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, CRC Press, Boca Raton, FL. https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840
Chakraborty DP (2006) A search model and figure of merit for observer data acquired according to the free-response paradigm, Phys Med Biol 51, 3449-3462.
Chakraborty DP (2006) ROC Curves predicted by a model of visual search, Phys Med Biol 51, 3463--3482.
mu <- 1;lambda <- 1;nu <- 0.9
lesDistr <- c(0.9, 0.1)
## i.e., 90% of dis. cases have one lesion, and 10% have two lesions
relWeights <- c(0.05, 0.95)
## i.e., lesion 1 has weight 5 percent while lesion two has weight 95 percent
UtilAnalyticalAucsRSM(mu, lambda, nu, zeta1 = -Inf, lesDistr)
#> $aucROC
#> [1] 0.8147209
#>
#> $aucAFROC
#> [1] 0.7448364
#>
#> $aucwAFROC
#> [1] 0.7448364
#>
UtilAnalyticalAucsRSM(mu, lambda, nu, zeta1 = -Inf, lesDistr, relWeights)
#> $aucROC
#> [1] 0.8147209
#>
#> $aucAFROC
#> [1] 0.7448364
#>
#> $aucwAFROC
#> [1] 0.785221
#>