Compute outlying measures
Compute outlying measures based on a proximity matrix.
## Default S3 method: outlier(x, cls=NULL, ...) ## S3 method for class 'randomForest' outlier(x, ...)
x |
a proximity matrix (a square matrix with 1 on the diagonal
and values between 0 and 1 in the off-diagonal positions); or an object of
class |
cls |
the classes the rows in the proximity matrix belong to. If not given, all data are assumed to come from the same class. |
... |
arguments for other methods. |
A numeric vector containing the outlying measures. The outlying measure of a case is computed as n / sum(squared proximity), normalized by subtracting the median and divided by the MAD, within each class.
set.seed(1)
iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)
plot(outlier(iris.rf), type="h",
col=c("red", "green", "blue")[as.numeric(iris$Species)])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.