dlda {supclust} | R Documentation |
Classification with Wilma's Clusters
Description
The four functions nnr
(nearest neighbor rule),
dlda
(diagonal linear discriminant analysis), logreg
(logistic regression) and aggtrees
(aggregated trees) are used
for binary classification with the cluster representatives of Wilma's
output.
Usage
dlda (xlearn, xtest, ylearn)
nnr (xlearn, xtest, ylearn)
logreg (xlearn, xtest, ylearn)
aggtrees(xlearn, xtest, ylearn)
Arguments
xlearn |
Numeric matrix of explanatory variables ( |
xtest |
A numeric matrix of explanatory variables ( |
ylearn |
Numeric vector of length |
Details
nnr
implements the 1-nearest-neighbor-rule with
Euclidean distance function. dlda
is linear discriminant
analysis, using the restriction that the covariance matrix is diagonal
with equal variance for all predictors. logreg
is default
logistic regression. aggtrees
fits a default stump (a
classification tree with two terminal nodes) by rpart
for every
predictor variable and uses majority voting to determine the final
classifier.
Value
Numeric vector of length m
, containing the predicted class
labels for the test observations. The class labels are coded by 0 and
1.
Author(s)
Marcel Dettling
References
see those in wilma
.
See Also
Examples
## Generating random learning data: 20 observations and 10 variables (clusters)
set.seed(342)
xlearn <- matrix(rnorm(200), nrow = 20, ncol = 10)
## Generating random test data: 8 observations and 10 variables(clusters)
xtest <- matrix(rnorm(80), nrow = 8, ncol = 10)
## Generating random class labels for the learning data
ylearn <- as.numeric(runif(20)>0.5)
## Predicting the class labels for the test data
nnr(xlearn, xtest, ylearn)
dlda(xlearn, xtest, ylearn)
logreg(xlearn, xtest, ylearn)
aggtrees(xlearn, xtest, ylearn)