The sfsmisc Package - NexTag Supports Open Source Initiatives

20 diagDA
diagDA
Diagonal Discriminant Analysis
Description
This function implements a simple Gaussian maximum likelihood discriminant rule, for diagonal
class covariance matrices.
Usage
dDA(x, cll, pool = TRUE)
## S3 method for class 'dDA':
predict(object, newdata, pool = object$pool, ...)
## S3 method for class 'dDA':
print(x, ...)
diagDA(ls, cll, ts, pool = TRUE)
Arguments
x,ls
cll
object
ts, newdata
pool
learning set data matrix, with rows corresponding to cases (e.g., mRNA samples)
and columns to predictor variables (e.g., genes).
class labels for learning set, must be consecutive integers.
object of class dDA.
test set (prediction) data matrix, with rows corresponding to cases and columns
to predictor variables.
logical flag. If true (by default), the covariance matrices are assumed to be constant
across classes and the discriminant rule is linear in the data. Otherwise
(pool= FALSE), the covariance matrices may vary across classes and the discriminant
rule is quadratic in the data.
... further arguments passed to and from methods.
Value
dDA() returns an object of class dDA for which there are print and predict methods. The
latter returns the same as diagDA():
diagDA() returns an integer vector of class predictions for the test set.
Author(s)
Sandrine Dudoit, 〈sandrine@stat.berkeley.edu〉 and
Jane Fridlyand, 〈janef@stat.berkeley.edu〉 originally wrote stat.diag.da() in CRAN package
sma which was modified for speedup by Martin Maechler 〈maechler@R-project.org〉 who also
introduced dDA etc.