
Compute the leave-one-out cross-validation score.
CV.S(y, S, W = NULL, trim = 0, draw = FALSE, metric = metric.lp, ...)
Matrix of set cases with dimension (n
x m
), where
n
is the number of curves and m
are the points observed in
each curve.
Matrix of weights.
The alpha of the trimming.
=TRUE, draw the curves, the sample median and trimmed mean.
Metric function, by default metric.lp
.
Further arguments passed to or from other methods.
Returns CV score calculated for input parameters.
A.-If trim=0
:
B.-If trim>0
: (1-trim)
curves with less error.
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
# }
# NOT RUN {
# }
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