The cross-validation (CV) score
Compute the leave-one-out cross-validation score.
CV.S(y, S, W = NULL, trim = 0, draw = FALSE, metric = metric.lp, ...)
y |
Matrix of set cases with dimension ( |
S |
|
W |
Matrix of weights. |
trim |
The alpha of the trimming. |
draw |
=TRUE, draw the curves, the sample median and trimmed mean. |
metric |
Metric function, by default |
... |
Further arguments passed to or from other methods. |
A.-If trim=0:
CV(h)=1/n\, ∑_i ((y_i\, -\, r_{i}(x_i))\, /\, (1\, -\, S_ii))^2\, w(x_i),\, i=1,...,n
Sii is the ith diagonal element of the smoothing matrix S.
B.-If trim>0:
CV(h)=1/n\ ∑_i ((y_i-r_{i}(x_i))/(1-S_ii))^2 w(x_i),\, i=1,...,l
Sii
is the ith diagonal element of the smoothing matrix S and l the
index of (1-trim) curves with less error.
Returns CV score calculated for input parameters.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.
## Not run: data(tecator) x<-tecator$absorp.fdata np<-ncol(x) tt<-1:np S1 <- S.NW(tt,3,Ker.epa) S2 <- S.LLR(tt,3,Ker.epa) S3 <- S.NW(tt,5,Ker.epa) S4 <- S.LLR(tt,5,Ker.epa) cv1 <- CV.S(x, S1) cv2 <- CV.S(x, S2) cv3 <- CV.S(x, S3) cv4 <- CV.S(x, S4) cv5 <- CV.S(x, S4,trim=0.1,draw=TRUE) cv1;cv2;cv3;cv4;cv5 S6 <- S.KNN(tt,1,Ker.unif,cv=TRUE) S7 <- S.KNN(tt,5,Ker.unif,cv=TRUE) cv6 <- CV.S(x, S6) cv7 <- CV.S(x, S7) cv6;cv7 ## End(Not run)
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