Calculation of beta scaling parameters
Calculates the scaling parameter for betascale.
b.scal(member, grouping, dis = FALSE, eps = 1e-04)
member |
Membership values of an argmax classification method.
Eg. posterior probabilities of |
grouping |
Class vector. |
dis |
Logical, whether to optimize the dispersion parameter in |
eps |
Minimum variation of membership values. If variance is smaller than |
With betascale and b.scal, membership values of an argmax classifier
are scaled in such a way, that the mean membership value of those values which are assigned
to each class reflect the mean correctness rate of that values.
This is done via qbeta and pbeta with the appropriate shape parameters.
If dis is TRUE, it is tried that the variation of membership values
is optimal for the accuracy relative to the correctness rate.
If the variation of the membership values is less than eps,
they are treated as one point and shifted towards the correctness rate.
A list containing
model |
Estimated parameters for |
eps |
Value of |
member |
Scaled membership values. |
Karsten Luebke (karsten.luebke@fom.de), Uwe Ligges
Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789
library(MASS) data(B3) pB3 <- predict(lda(PHASEN ~ ., data = B3))$posterior pbB3 <- b.scal(pB3, B3$PHASEN, dis = TRUE) ucpm(pB3, B3$PHASEN) ucpm(pbB3$member, B3$PHASEN)
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