`Util2Physical.Rd`

Convert **intrinsic** RSM parameters \(lambda_i\) and \(nu_i\)
correspond to the **physical** RSM parameters \(lambda_i'\)
and \(nu_i'\). The physical parameters are more meaningful but they
depend on \(mu\). The intrinsic parameters are independent of
\(mu\). See book for details.

`Util2Physical(mu, lambda_i, nu_i)`

- mu
The mean of the Gaussian distribution for the ratings of latent LLs, i.e. continuous ratings of lesions that were found by the search mechanism ~ N(\(\mu\),1). The corresponding distribution for the ratings of latent NLs is N(0,1).

- lambda_i
The

*intrinsic*Poisson lambda_i parameter.- nu_i
The

*intrinsic*Binomial nu_i parameter.

A list containing \(\lambda\) and \(\nu\)

RSM is the Radiological Search Model described in the book.
See also `Util2Intrinsic`

.

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.

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

```
mu <- 2;lambda_i <- 20;nu_i <- 1.1512925
lambda <- Util2Physical(mu, lambda_i, nu_i)$lambda
nu <- Util2Physical(mu, lambda_i, nu_i)$nu
## note that only the physical values are only constrained to be positive
## the physical variable nu must obey 0 <= nu <= 1
```