CV.S: The cross-validation (CV) score In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 CV.S R Documentation

The cross-validation (CV) score

Description

Compute the leave-one-out cross-validation score.

Usage

```CV.S(y, S, W = NULL, trim = 0, draw = FALSE, metric = metric.lp, ...)
```

Arguments

 `y` 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. `S` Smoothing matrix, see `S.NW`, `S.LLR` or S.KNN. `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 `metric.lp`. `...` Further arguments passed to or from other methods.

Details

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.

Value

Returns CV score calculated for input parameters.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

References

Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.

See Also as `optim.np`
Alternative method: `GCV.S`

Examples

```## 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)

```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.