S.np {fda.usc} | R Documentation |
Smoothing matrix by nonparametric methods
Description
Provides the smoothing matrix S
for the discretization points tt
Usage
S.LLR(tt, h, Ker = Ker.norm, w = NULL, cv = FALSE)
S.LPR(tt, h, p = 1, Ker = Ker.norm, w = NULL, cv = FALSE)
S.LCR(tt, h, Ker = Ker.norm, w = NULL, cv = FALSE)
S.KNN(tt, h = NULL, Ker = Ker.unif, w = NULL, cv = FALSE)
S.NW(tt, h = NULL, Ker = Ker.norm, w = NULL, cv = FALSE)
Arguments
tt |
Vector of discretization points or distance matrix |
h |
Smoothing parameter or bandwidth. In S.KNN, number of k-nearest neighbors. |
Ker |
Type of kernel used, by default normal kernel. |
w |
Optional case weights. |
cv |
If |
p |
Polynomial degree. be passed by default to create.basis |
Details
Options:
-
S.NW
: Nadaraya-Watson kernel estimator with bandwidth parameterh
. -
S.LLR
: Local Linear Smoothing with bandwidth parameterh
. -
S.KNN
: K nearest neighbors estimator with parameterknn
. -
S.LPR
: Polynomial Local Regression Estimator with parameter of polynomialp
and of kernelKer
. -
S.LCR
: Local Cubic Regression Estimator with kernelKer
.
Value
Return the smoothing matrix S
.
-
S.LLR
: Local Linear Smoothing. -
S.NW
: Nadaraya-Watson kernel estimator. -
S.KNN
: k nearest neighbors estimator. -
S.LPR
: Local Polynomial Regression Estimator. -
S.LCR
: Cubic Polynomial Regression.
Author(s)
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es
References
Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.
Wasserman, L. All of Nonparametric Statistics. Springer Texts in Statistics, 2006.
Opsomer, J. D., and Ruppert, D. (1997). Fitting a bivariate additive model by local polynomial regression. The Annals of Statistics, 25(1), 186-211.
See Also
See Also as S.basis
Examples
## Not run:
tt <- 1:101
S <- S.LLR(tt,h=5)
S2 <- S.LLR(tt,h=10,Ker=Ker.tri)
S3 <- S.NW(tt,h=10,Ker=Ker.tri)
S4 <- S.KNN(tt,h=5,Ker=Ker.tri)
par(mfrow=c(2,3))
image(S)
image(S2)
image(S3)
image(S4)
S5 <- S.LPR(tt,h=10,p=1, Ker=Ker.tri)
S6 <- S.LCR(tt,h=10,Ker=Ker.tri)
image(S5)
image(S6)
## End(Not run)