Creates a scatter plot for each pair of variables in given data. Observations in different classes are represented by different colors and symbols.
clPairs(data, classification, symbols, colors, labels = dimnames(data)[[2]],
CEX = 1, gap = 0.2, …)clPairsLegend(x, y, class, col, pch, …)
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
A numeric or character vector representing a classification of observations
(rows) of data
.
Either an integer or character vector assigning a plotting symbol to each
unique class in classification
. Elements in symbols
correspond to classes in order of appearance in the sequence of
observations (the order used by the function unique
).
The default is given by mclust.options("classPlotSymbols")
.
Either an integer or character vector assigning a color to each
unique class in classification
. Elements in colors
correspond to classes in order of appearance in the sequence of
observations (the order used by the function unique
).
The default is given by mclust.options("classPlotColors")
.
A vector of character strings for labeling the variables. The default
is to use the column dimension names of data
.
An argument specifying the size of the plotting symbols. The default value is 1.
An argument specifying the distance between subplots (see pairs
).
The x and y co-ordinates with respect to a graphic device having
plotting region coordinates par("usr" = c(0,1,0,1))
.
The class labels.
The colors and plotting symbols appearing in the legend.
The function clPairs
invisibly returns a list with the following
components:
A character vector of class labels.
A vector of colors used for each class.
A vector of plotting symbols used for each class.
The function clPairs
draws scatter plots on the current graphics
device for each combination of variables in data
. Observations of
different classifications are labeled with different symbols.
The function clPairsLegend
can be used to add a legend. See examples
below.
C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.
# NOT RUN {
clPairs(iris[,1:4], cl = iris$Species)
clp <- clPairs(iris[,1:4], cl = iris$Species, lower.panel = NULL)
clPairsLegend(0.1, 0.4, class = clp$class,
col = clp$col, pch = clp$pch,
title = "Iris data")
# }
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