SparsePlot: Sparse plot

In KFPCA: Kendall Functional Principal Component Analysis

Description

Create sparse plot to see the sparsity of the data.

Usage

SparsePlot(Lt, interval, ...)

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.
interval	A vector of length two denoting the supporting interval.
...	Other arguments passed into plot.

Details

For the sparse plot, x-axis is the observation time while y-axis represents various subjects.

Value

Create the corresponding sparse plot.

Examples

# 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)))
# DataNew1 and DataNew2 have different sparsity
DataNew1 <- GenDataKL(n, interval = interval, sparse = 6:8, regular = FALSE,
                      meanfun = function(x){0}, score = score,
                      eigfun = eigfun, sd = sqrt(0.1))
DataNew2 <- GenDataKL(n, interval = interval, sparse = 2:4, regular = FALSE,
                      meanfun = function(x){0}, score = score,
                      eigfun = eigfun, sd = sqrt(0.1))
# Create sparse plots
par(mfrow = c(1, 2))
SparsePlot(DataNew1$Lt, interval = interval)
SparsePlot(DataNew2$Lt, interval = interval)
par(mfrow = c(1, 1))

KFPCA documentation built on Feb. 4, 2022, 5:07 p.m.