flexdaCMA {CMA} | R Documentation |
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. flexdaCMA-methods
.
flexdaCMA(X, y, f, learnind, comp = 1, plot = FALSE, models=FALSE, ...)
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 re-coded to
range from |
f |
A two-sided 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 ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
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
### 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)