Im sure you've already found your solutions as this post is very old, but for those of us who are still looking for solutions - I have found http://youtu.be/-Cp_KP9mq94 is a great source for instructions on how to run a multinomial logistic regression model in R using mlogit package. If you go to the econonometrics academy website she has all the scripts, data for R and SAS and STATA I think or SPSS one of those.
Which kind of explains how/why and what to do about transforming your data into the format of the "long" format vs "wide". Most likely you have a wide format, which requires transformation.
https://sites.google.com/site/econometricsacademy/econometrics-models/multinomial-probit-and-logit-models
To elaborate on Frank Harrell's answer, what the Epi
package did was to fit a logistic regression, and make a ROC curve with outcome predictions of the following form:
$$
outcome = \frac {1}{1+e^{-(\beta_0 + \beta_1 s100b + \beta_2 ndka)}}
$$
In your case, the fitted values are $\beta_0$ (intercept) = -2.379, $\beta_1$ (s100b) = 5.334 and $\beta_2$ (ndka) = 0.031. As you want your predicted outcome to be 0.312 (the "optimal" cutoff), you can then substitute this as (hope I didn't introduce errors here):
$$
0.312 = \frac {1}{1+e^{-(-2.379 + 5.334 s100b + 0.031 ndka)}}
$$
$$
1.588214 = 5.334 s100b + 0.031 ndka
$$
or:
$$
s100b = \frac{1.588214 - 0.031 ndka}{5.334}
$$
Any pair of (s100b, ndka) values that satisfy this equality is "optimal". Bad luck for you, there are an infinity of these pairs. For instance, (0.29, 1), (0, 51.2), etc. Even worse, most of them don't make any sense. What does the pair (-580, 10000) mean? Nothing!
In other words, you can't establish cut-offs on the inputs - you have to do it on the outputs, and that's the whole point of the model.
Best Answer
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[2] Hand, D. J., & Till, R. J. (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning, 45(2), 171-186.