FuNopaRe: Nonparametric Regression for Functional Data

In sipemu/Nonparametric-Functional-Data-Analysis: Nonparametric Functional Data Analysis

Description

FuNopaRe is a function that estimates optimal bandwidth of the kernel estimate based on the learning data.

Usage

FuNopaRe(X, Y, semimetric, semimetric.params, bandwidth = "CV")

Arguments

X	Matrix with the functional data (curves) each row one
Y	Vector of the scalar responses
semimetric	A string of choosing the semimetric; allowed are: "Deriv" and "PCA"
semimetric.params	Parameters for the semimetric function.
bandwidth	Method for choosing the bandwidth; allowed are: "CV"(default), "kNNgCV", and "kNNlCV"

Value

FuNopaRe returns an object of the class FuNopaRe;

Author(s)

Simon Mueller [e-mail]

References

Ferraty, F. and Vieu, P. Nonparametric Functional Data Analysis. Springer 2006.

Rachdi, M. and Vieu, P. Nonparametric regression for functional data: automatic smoothing parameter selection. Journal of Statistical Planning and Inference 137, 9 (2007), 2784-2801.

Benhenni, K., Ferraty, F., Rachdi, M., and Vieu, P. Local smoothing regression with functional data. Computational Statistics 22, 3 (2007) 353???369.

See Also

Semimetric, predict.FuNopaRe

Examples

# functional datasets
require (fds)

# fat spectrum dataset
Y <- Fatvalues
X <- t(Fatspectrum$y)

# setup semimetric params
semimetric.params <- c()
semimetric.params$q <- 2
semimetric.params$nknot  <- 20
semimetric.params$range.grid  <- c (min (Fatspectrum$x), 
                                    max (Fatspectrum$x))

# learn and testsample
learn <- 1:160
test <- 161:215

# parameter estimation and prediction by cross-validation
Learn.Fat.CV <- FuNopaRe (X[learn, ], 
                          Y[learn], 
                          semimetric = "Deriv", 
                          semimetric.params, 
                          bandwidth = "CV")

sipemu/Nonparametric-Functional-Data-Analysis documentation built on May 29, 2019, 10:10 p.m.