# b.scal: Calculation of beta scaling parameters

## Description

Calculates the scaling parameter for `betascale`

.

## 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].

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.

## Value

A list containing

modelEstimated parameters for `betascale`

.

epsValue of `eps`

from the call.

memberScaled membership values.

## 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.

## References

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

## 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)
# }