rrda | R Documentation |
Discrimination (DA) based on ridge regression (RR).
The training variable y
(univariate class membership) is transformed to a dummy table containing nclas
columns, where nclas
is the number of classes present in y
. Each column is a dummy variable (0/1). Then, a ridge regression (RR) is run on the X-
data and the dummy table, returning predictions of the dummy variables. For a given observation, the final prediction is the class corresponding to the dummy variable for which the prediction is the highest.
rrda(X, y, weights = NULL, lb = 1e-5)
## S3 method for class 'Rrda'
predict(object, X, ..., lb = NULL)
X |
For the main functions: Training X-data ( |
y |
Training class membership ( |
weights |
Weights ( |
lb |
A value of regularization parameter |
object |
A fitted model, output of a call to the main functions. |
... |
Optional arguments. Not used. |
See the examples.
n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE)
#ytrain <- sample(c("a", "10", "d"), size = n, replace = TRUE)
m <- 5
Xtest <- Xtrain[1:m, ] ; ytest <- ytrain[1:m]
lb <- 1
fm <- rrda(Xtrain, ytrain, lb = lb)
predict(fm, Xtest)
pred <- predict(fm, Xtest)$pred
err(pred, ytest)
predict(fm, Xtest, lb = 0:2)
predict(fm, Xtest, lb = 0)
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