FPCscoreLSE(Lt, Ly, kern, bw, FPC_dis, RegGrid, more = FALSE)

# 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 = 3:5,
                     meanfun = function(x){0}, score = score,
                     eigfun = eigfun, sd = sqrt(0.1))
basis <- fda::create.bspline.basis(interval, nbasis = 13, norder = 4,
                              breaks = seq(0, 10, length.out = 11))
Klist <- KFPCA(DataNew$Lt, DataNew$Ly, interval, nK = 2, bw = 1,
               nRegGrid = 51, fdParobj = basis)
# Just an example to explain the use of FPCscoreLSE().
# One can obtain FPC scores estimates for KFPCA method
# by KFPCA() directly. Note that FPCscoreLSE() can also be used
# to estimate FPC scores for methods except KFPCA.
scoreKFPCA <- FPCscoreLSE(DataNew$Lt, DataNew$Ly, kern = "epan",
                          bw = Klist$bwmean, FPC_dis = Klist$FPC_dis,
                          RegGrid = seq(interval[1], interval[2], length.out = 51))