| mahal {emuR} | R Documentation |
Classify using Mahalanobis distance
Description
Classifies using Mahalanobis distance
Usage
mahal(data, train)
Arguments
data |
A vector or matrix of data |
train |
A Gaussian model generated by |
Details
The model argument contains the mean and inverse covariance matrix
(or standard deviation if the data is one-dimensional) for each class in
the training set as well as the class labels. This function calculates the
Mahalanobis distance of each row of data from each class mean and
assigns the label of the closest mean to that row. The result is a vector
of labels corresponding to the rows of data.
The Mahalanobis distance between a data point and a class is the Euclidean distance between the point and the class mean divided by the covariance matrix for the class. This means that classes with large covariances will attract data points from a larger area than those with small covariances.
Value
A label vector with one element per row of data
References
O'Shaughnessy, D. Speech Communication (Addison-Wesley: Reading, MA. 1987)
See Also
train