clues (version 0.6.2.2)

plotCurves: Plot Trajectories for Each Cluster

Description

Plot trajectories for each cluster.

Usage

plotCurves(y, mem, xlab = NULL, ylab = NULL,
      xlim = NULL, ylim = NULL, 
      las = NULL, lwd = NULL, ...)

Arguments

y

data matrix which is an R matrix object (for dimension > 1) or vector object (for dimension=1) with rows being observations and columns being variables.

mem

vector of the cluster membership for data points. The cluster membership takes values: \(1\), \(2\), \(\ldots\), \(g\), where \(g\) is the estimated number of clusters.

xlab

a title for the x axis. If xlab = NULL, then xlab will be set to be “variable”.

ylab

a title for the y-axis. If ylab = NULL, then ylab will be set to be “average observation”.

xlim

range of x-axis. If xlim = NULL, then xlim will be set to be (0, nc + 1), where nc is the number of columns of the matrix y.

ylim

range of y-axis. If ylim = NULL, then ylim will be set to be the range of the matrix y.

las

numeric in \(0,1,2,3\); the style of axis labels (see par. 0 means that the labels always parallel to the axis [default]; 1 means that the labels always horizontal; 2 means that the labels always perpendicular to the axis, 3 means that the lables always vertical. If las = NULL, then las will be set to be 2.

lwd

the line width, a positive number, defaulting to 1 (see par. If lwd = NULL, the lwd will be set to be 3.

...

graphical parameters (see par).

References

Wang, S., Qiu, W., and Zamar, R. H. (2007). CLUES: A non-parametric clustering method based on local shrinking. Computational Statistics & Data Analysis, Vol. 52, issue 1, pages 286-298.

Examples

# NOT RUN {
    data(Curve)
    
    # data matrix
    curve <- Curve$curve
 
    # 'true' cluster membership
    curve.mem <- Curve$curve.mem
    # 'true' number of clusters
    nClust <- length(unique(curve.mem))
 
    # plot average trajectories
    plotCurves(curve, curve.mem)
    
# }