Description Usage Arguments Value Note Author(s) References See Also Examples
This method is experimental.
It is easy to show that, after appropriate scaling of the predictor matrix X
,
Fisher's Linear Discriminant Analysis is equivalent to Discriminant Analysis
in the space of the fitted values from the linear regression of the
nlearn x K
indicator matrix of the class labels on X
.
This gives rise to 'nonlinear discrimant analysis' methods that expand
X
in a suitable, more flexible basis. In order to avoid overfitting,
penalization is used. In the implemented version, the linear model is replaced
by a generalized additive one, using the package mgcv
.
For S4
method information, s. flexdaCMAmethods
.
1 
X 
Gene expression data. Can be one of the following:

y 
Class labels. Can be one of the following:
WARNING: The class labels will be recoded to
range from 
f 
A twosided formula, if 
learnind 
An index vector specifying the observations that
belong to the learning set. May be 
comp 
Number of discriminant coordinates (projections) to compute.
Default is one, must be smaller than or equal to 
plot 
Should the projections onto the space spanned by the optimal
projection directions be plotted ? Default is 
models 
a logical value indicating whether the model object shall be returned 
... 
Further arguments passed to the function 
An object of class cloutput
.
Excessive variable selection has usually to performed before
flexdaCMA
can be applied in the p > n
setting.
Recall that the original predictor dimension is even enlarged,
therefore, it should be applied only with very few variables.
Martin Slawski [email protected]
AnneLaure Boulesteix [email protected]
Ripley, B.D. (1996)
Pattern Recognition and Neural Networks.
Cambridge University Press
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, shrinkldaCMA
, svmCMA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ### load Golub AML/ALL data
data(golub)
### extract class labels
golubY < golub[,1]
### extract gene expression from first 5 genes
golubX < as.matrix(golub[,2:6])
### select learningset
ratio < 2/3
set.seed(111)
learnind < sample(length(golubY), size=floor(ratio*length(golubY)))
### run flexible Discriminant Analysis
result < flexdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1)
### show results
show(result)
ftable(result)
plot(result)

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