CreateStringingPlot: Create plots for observed and stringed high dimensional data

In fdapace: Functional Data Analysis and Empirical Dynamics

Create plots for observed and stringed high dimensional data

Description

The function produces the following three plots: 1) A plot of predictors (standardized if specified so during stringing) in original order for a subset of observations; 2) A plot of predictors in stringed order for the same subset of observations; 3) A plot of the stringing function, which is the stringed order vs. the original order.

Usage

CreateStringingPlot(stringingObj, subset, ...)

Arguments

stringingObj	A stringing object of class "Stringing", returned by the function Stringing.
subset	A vector of indices or a logical vector for subsetting the observations. If missing, first min(n,50) observations will be plotted where n is the sample size.
...	Other arguments passed into matplot for plotting options

Details

This approach is based on Chen, K., Chen, K., Müller, H.G., Wang, J.L. (2011). Stringing high-dimensional data for functional analysis. J. American Statistical Association 106, 275–284.

Examples

set.seed(1)
n <- 50
wiener = Wiener(n = n)[,-1]
p = ncol(wiener)
rdmorder = sample(size = p, x=1:p, replace = FALSE)
stringingfit = Stringing(X = wiener[,rdmorder], disOptns = "correlation")
diff_norev = sum(abs(rdmorder[stringingfit$StringingOrder] - 1:p))
diff_rev = sum(abs(rdmorder[stringingfit$StringingOrder] - p:1))
if(diff_rev <= diff_norev){
  stringingfit$StringingOrder = rev(stringingfit$StringingOrder)
  stringingfit$Ly = lapply(stringingfit$Ly, rev)
}
CreateStringingPlot(stringingfit, 1:20)

fdapace documentation built on July 3, 2024, 5:08 p.m.