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## S3 method for class 'formula':
slda(formula, data, subset, na.action=na.rpart, \dots)
## S3 method for class 'factor':
slda(y, X, q=NULL, \dots)
lhs ~ rhs
where lhs
is the response variable and rhs
a set of
predictors.NA
s. Defaults to
na.rpart
.lda
.slda
, a list with componentslda
.This form of reduction of the dimensionality was developed for discriminant analysis problems by Laeuter (1992) and was used for multivariate tests by Laeuter et al. (1998), Kropf (2000) gives an overview. For details on left-spherically distributions see Fang and Zhang (1990).
Siegfried Kropf (2000), Hochdimensionale multivariate Verfahren in der medizinischen Statistik, Shaker Verlag, Aachen (in german).
Juergen Laeuter (1992), Stabile multivariate Verfahren, Akademie Verlag, Berlin (in german).
Juergen Laeuter, Ekkehard Glimm and Siegfried Kropf (1998), Multivariate Tests Based on Left-Spherically Distributed Linear Scores. The Annals of Statistics, 26(5) 1972--1988.
predict.slda
learn <- as.data.frame(mlbench.twonorm(100))
test <- as.data.frame(mlbench.twonorm(1000))
mlda <- lda(classes ~ ., data=learn)
mslda <- slda(classes ~ ., data=learn)
print(mean(predict(mlda, newdata=test)$class != test$classes))
print(mean(predict(mslda, newdata=test)$class != test$classes))
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