slda {ipred} | R Documentation |
Stabilised Linear Discriminant Analysis
Description
Linear discriminant analysis based on left-spherically distributed linear scores.
Usage
## S3 method for class 'formula'
slda(formula, data, subset, na.action=na.rpart, ...)
## S3 method for class 'factor'
slda(y, X, q=NULL, ...)
Arguments
y |
the response variable: a factor vector of class labels. |
X |
a data frame of predictor variables. |
q |
the number of positive eigenvalues the scores are derived from, see below. |
formula |
a formula of the form |
data |
optional data frame containing the variables in the model formula. |
subset |
optional vector specifying a subset of observations to be used. |
na.action |
function which indicates what should happen when
the data contain |
... |
additional parameters passed to |
Details
This function implements the LDA for -dimensional linear scores of
the original
predictors derived from the
rule by Laeuter
et al. (1998). Based on the product sum matrix
the eigenvalue problem is solved. The first
columns
of
are used as a weight matrix for the
original
predictors:
. By default,
is the number
of eigenvalues greater one. The
-dimensional linear scores are
left-spherically distributed and are used as predictors for a classical
LDA.
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).
Value
An object of class slda
, a list with components
scores |
the weight matrix. |
mylda |
an object of class |
References
Fang Kai-Tai and Zhang Yao-Ting (1990), Generalized Multivariate Analysis, Springer, Berlin.
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.
See Also
Examples
library("mlbench")
library("MASS")
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))