eLago {lago} | R Documentation |
This is the original LAGO procedure, called “eLAGO” now to differentiate it from later variations such as “sLAGO”.
eLago(Y, X) eLago(Y, X, D=preLago(Y,X), alpha = 0.5, K = 5, kernel="g") eLago(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 eLago 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 it important to note that a uniform kernel is not very effective. |
C1 |
an n1 -by-p matrix whose rows are the centers of the LAGO radial basis function network. |
R |
an n1 -by-p matrix whose rows specify the radius vector 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.
sLago, preLago, view.eLago, rank.eLago