# b.scal

##### 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

Estimated parameters for `betascale`

.

Value of `eps`

from the call.

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

##### See Also

##### 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*