sLago {lago} | R Documentation |
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.
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")
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. |
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. |
Alexandra Laflamme-Sanders and Mu Zhu, University of Waterloo, Canada.
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.
eLago, preLago, view.sLago, rank.sLago