sLago {lago}R Documentation

Spherical LAGO

Description

This is the spherical LAGO procedure, which is a variation of the original LAGO — now called “eLAGO”. It is less adpative than the original LAGO but could actually be more generally useful. However, it is almost certainly true in this case that the covariates should be standardized a priori to have mean zero and variance one.

Usage

sLago(Y, X)
sLago(Y, X, D=preLago(Y,X), alpha = 0.5, K = 5, kernel="g")
sLago(Y, X, alpha = 0.5, K = 5, kernel="g")

Arguments

Y a vector of binary responses; training data.
X a matrix of covariates/predictors; training data.
D if is.null(D)==T, the matrix D will be calculated by the function; however, it's computationally desirable to precompute D using preLago(Y, X), especially if the function sLago will be called repeatedly on the same data, e.g., during cross-validation.
alpha a positive real number; if alpha > 1, the radius/bandwidth is stretched; if alpha < 1, the radius/bandwidth is dampened.
K a positive integer indicating the number of nearest neighbors to use for calculating the radius/bandwidth.
kernel a character input; ‘t’ for “triangular”; ‘g’ for “Gaussian”; otherwise a ‘uniform’ kernel is used, but since the uniform kernel is not very effective, it is actually not fully implemented in this version so do NOT use it. Other kernels are not implemented in this version, but may be added in future versions.

Value

C1 an n1-by-p matrix whose rows are the centers of the LAGO radial basis function network.
R a vector of length n1, each specifying the radius of the n1 radial basis functions.
alpha the stretching/dampening parameter; see above; here passed on to be used for prediction.
kernel either ‘t’ or ‘g’; see above; here passed on to be used for prediction.

Author(s)

Alexandra Laflamme-Sanders and Mu Zhu, University of Waterloo, Canada.

References

Zhu M, Su W, Chipman HA (2006). LAGO: A computationally efficient approach for statistical detection. Technometrics, 48(2), 193 – 205.

Zhu M (2008). Kernels and ensembles: Perspectives on statistical learning. The American Statistician, 62(2), 97 – 109.

See Also

eLago, preLago, view.sLago, rank.sLago


[Package lago version 0.1-1 Index]