KFPCA (version 2.0)

MeanEst: Local linear estimates of mean function

Description

Local linear estimates of mean function.

Usage

MeanEst(Lt, Ly, kern, bw, gridout)

Arguments

Lt

A list of n vectors, where n is the sample size. Each entry contains the observation time in ascending order for each subject.

Ly

A list of n vectors, where n is the sample size. Each entry contains the measurements of each subject at the observation time correspond to Lt.

kern

A character denoting the kernel type; 'epan'(Epanechnikov), 'unif'(Uniform), 'quar'(Quartic), 'gauss'(Gaussian).

bw

A scalar denoting the bandwidth.

gridout

A vector denoting the time points that the mean function need to be estimated.

Value

A list containing the following components:

Grid

A vector denoting the time points that the mean function need to be estimated.

mean

A vector containing the mean function estimates.

Examples

# NOT RUN {
# Generate data
n <- 100
interval <- c(0, 10)
lambda_1 <- 9 #the first eigenvalue
lambda_2 <- 1.5 #the second eigenvalue
eigfun <- list()
eigfun[[1]] <- function(x){cos(pi * x/10)/sqrt(5)}
eigfun[[2]] <- function(x){sin(pi * x/10)/sqrt(5)}
score <- cbind(rnorm(n, 0, sqrt(lambda_1)), rnorm(n, 0, sqrt(lambda_2)))
DataNew <- GenDataKL(n, interval = interval, sparse = 6:8, regular = FALSE,
                     meanfun = function(x){x}, score = score,
                     eigfun = eigfun, sd = sqrt(0.1))
# Mean function estimate at all observation time points
bwOpt <- GetGCVbw1D(DataNew$Lt, DataNew$Ly, kern = "epan")
meanest <- MeanEst(DataNew$Lt, DataNew$Ly, kern = "epan", bw = bwOpt,
                   gridout = sort(unique(unlist(DataNew$Lt))))
plot(meanest$Grid, meanest$mean)
# }