# b.scal

0th

Percentile

##### Calculation of beta scaling parameters

Calculates the scaling parameter for betascale.

Keywords
classif
##### Usage
b.scal(member, grouping, dis = FALSE, eps = 1e-04)
##### Arguments
member

Membership values of an argmax classification method. Eg. posterior probabilities of lda. Row-wise values must sum up to 1 and must be in the interval [0,1].

grouping

Class vector.

dis

Logical, whether to optimize the dispersion parameter in pbeta.

eps

Minimum variation of membership values. If variance is smaller than eps, the values are treated as one point.

##### Details

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.

##### Value

A list containing

model

Estimated parameters for betascale.

eps

Value of eps from the call.

member

Scaled membership values.

##### References

Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789

betascale, e.scal

• b.scal
##### Examples
# NOT RUN {
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)
# }

Documentation reproduced from package klaR, version 0.6-14, License: GPL-2

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