matplot(x, y, type="p", lty=1:5, pch=<<see below>>, col=1:4) matpoints(x, y, type="p", lty=1:5, pch=<<see below>>, col=1:4) matlines(x, y, type="l", lty=1:5, pch=<<see below>>, col=1:4)
"p"
, points;
"l"
, lines;
"b"
, both;
"n"
, none; or
"h"
, high-density)
should be done
for each plot (column). The first character of
type
defines the
first plot, the second character the second, etc.
Characters in
type
are cycled through; e.g.,
"pl"
alternately plots
points and lines.
pch="X 02"
or figure out
the numeric equivalent of the ASCII character,
pch=c(88,2)
.
matplot
generates a new plot;
matpoints
and
matlines
add to the
current plot.
Points involving missing values are not plotted.
The first column of
x
is plotted against the first column of
y
, the
second column of
x
against the second column of
y
, etc.
If one matrix has fewer columns, plotting will cycle back
through the columns again. (In particular, either
x
or
y
may be a vector, against which all columns of the other
argument will be plotted.)
Because plotting symbols are drawn with lines and because these functions
may be changing the line style, you should probably specify
lty=1
when using plotting symbols.
#set up axes with vectors of extremes of data matplot(c(1,8), c(0,4.5), type="n", xlab="Length", ylab="Width", main= "Petal and Sepal Dimensions in Iris Blossoms") #add points for each species of iris separately matpoints(iris[,c(1,3),1], iris[,c(2,4),1], pch="sS") matpoints(iris[,c(1,3),2], iris[,c(2,4),2], pch="vV") legend(1,4, c("Setosa Petals", "Setosa Sepals", "Versicolor Petals", "Versicolor Sepals"), pch="sSvV") matplot(1:53, log(chernoff2[,c(1,10)]) ) # add line connecting points of column 1 matlines(1:53, log(chernoff2[,1]) ) matplot(iris[,3,], iris[,4,], xlab="Petal Length", ylab="Petal Width", pch="SCV", sub="S=Setosa, C=Versicolor, V=Virginica", main="Fisher's Iris Data", col=1)