CreateCovRegPlot {frechet}

Plots for Fréchet regression for covariance matrices.

Description

Plots for Fréchet regression for covariance matrices.

Usage

CreateCovRegPlot(x, optns = list())

Arguments

x

A covReg object obtained from CovFMean, GloCovReg or LocCovReg.

optns

A list of control options specified by list(name=value). See 'Details'.

Details

Available control options are

ind.xout

A vector holding the indices of elements in x$Mout at which the plots will be made. Default is

• 1:length(x$Mout) when x$Mout is of length no more than 3;

• c(1,round(length(x$Mout)/2),length(x$Mout)) when x$Mout is of length greater than 3.

nrow

An integer — default: 1; subsequent figures will be drawn in an optns$nrow-by-
ceiling(length(ind.xout)/optns$nrow) array.

plot.type

Character with two choices, "continuous" and "categorical". The former plots the correlations in a continuous scale of colors by magnitude while the latter categorizes the positive and negative entries into two different colors. Default is "continuous"

plot.clust

Character, the ordering method of the correlation matrix. "original" for original order (default); "AOE" for the angular order of the eigenvectors; "FPC" for the first principal component order; "hclust" for the hierarchical clustering order, drawing 4 rectangles on the graph according to the hierarchical cluster; "alphabet" for alphabetical order.

plot.method

Character, the visualization method of correlation matrix to be used. Currently, it supports seven methods, named "circle" (default), "square", "ellipse", "number", "pie", "shade" and "color".

CorrOut

Logical, indicating if output is shown as correlation or covariance matrix. Default is FALSE and corresponds to a covariance matrix.

plot.display

Character, "full" (default), "upper" or "lower", display full matrix, lower triangular or upper triangular matrix.

Value

No return value.

Examples

#Example y input
n=20             # sample size
t=seq(0,1,length.out=100)       # length of data
x = matrix(runif(n),n)
theta1 = theta2 = array(0,n)
for(i in 1:n){
 theta1[i] = rnorm(1,x[i],x[i]^2)
 theta2[i] = rnorm(1,x[i]/2,(1-x[i])^2)
}
y = matrix(0,n,length(t))
phi1 = sqrt(3)*t
phi2 = sqrt(6/5)*(1-t/2)
y = theta1%*%t(phi1) + theta2 %*% t(phi2)
xout = matrix(c(0.25,0.5,0.75),3)
Cov_est=GloCovReg(x=x,y=y,xout=xout,optns=list(corrOut = FALSE, metric="power",alpha=3))
CreateCovRegPlot(Cov_est, optns = list(ind.xout = 2, plot.method = "shade"))

CreateCovRegPlot(Cov_est, optns = list(plot.method = "color"))