mrct (version 0.0.1.0)

mrct.sparse.plot: Plot function for result from mrct.sparse()

Description

A function for descriptive plots for an object resulting from a call to mrct.sparse().

Usage

mrct.sparse.plot(
  x = seq(0, 1, length.out = dim(mrct.sparse.object[[2]])[2]),
  mrct.sparse.object
)

Value

Descriptive plots.

aMHD.plot

Alpha-Mahalanobis distances, corresponding cutoff values and coloring according to estimated outliers (grey regular, black irregular).

aMHD.plot.w

Same as aMHD.plot, with additional re-weighting step.

data.plot

Plot of the smoothed curves, colors corresponding to estimated outliers (gery regular, black irregular). Per default, the x-axis is plotted over an equidistant grid of the interval \([0,1]\).

Arguments

x

Gridpoints on which the smoothed data is to be plotted on. Default is seq(0,1,length.out=new.p) whereas new.p is a parameter set in the call to mrct.sparse().

mrct.sparse.object

A result from a call to mrct.sparse().

Examples

# Similar to example in mrct.sparse() helpfile
# Fix seed for reproducibility
set.seed(123)

# Sample outlying indices
cont.ind <- sample(1:50,size=10)

# Generate 50 sparse curves on the interval [0,1] at 10 timepoints with 20% outliers
y <- mrct.rgauss(x.grid=seq(0,1,length.out=10), N=50, model=1,
                 outliers=cont.ind, method="linear")

# Visualize curves (regular curves grey, outliers black)
colormap <- rep("grey",50); colormap[cont.ind] <- "black"
matplot(x = seq(0,1,length.out=10), y = t(y), type="l", lty="solid",
        col=colormap, xlab="t",ylab="")

# Run sparse MRCT
sparse.mrct.y <- mrct.sparse(data = y, nbasis = 10, h = 0.75, new.p = 50,
                             alpha = 0.1, initializations = 10, criterion = "sum" )

# Visualize alpha-Mahalanobis distances and smoothed curves
# Colorinformation according to estimated outliers (grey regular, black irregular)
mrct.sparse.plot(mrct.sparse.object = sparse.mrct.y)