This function plots a numerical matrix, and is often used to plot the following matrices: correlation, covariance, distance, and precision.
plotMatrix(x, col=colorRampPalette(c("red","black","green"))(100),
cex=1, circle=TRUE, order=FALSE, zlim=NULL, title="", PDF=FALSE, …)This required argument is a numerical matrix, or an
object of class bayesfactor, demonoid, iterquad,
laplace, pmc, posteriorchecks, or vb.
See more information below regarding these classes. One component of
a blocked proposal covariance matrix must be pointed to explicitly,
rather than to the object of class demonoid.
This argument specifies the colors of the circles. By
default, the colorRampPalette function colors strong positive
correlation as green, zero correlation as black, and
strong negative correlation as red, and provides 100 color
gradations.
When circle=TRUE, this argument specifies the size
of the marginal text, the names of the parameters or variables, and
defaults to 1.
Logical. When TRUE, each element in the numeric
matrix is represented with a circle, and a larger circle is assigned
to elements that are farther from zero. Also, when TRUE, the
gradation scale does not appear to the right of the plot.
Logical. This argument defaults to FALSE, and
presents the parameters or variables in the same order as in the
numeric matrix. When TRUE, the parameters or variables
are ordered using principal components analysis.
When circle=FALSE, the gradation scale may be
constrained to an interval by zlim, such as
zlim=c(-1,1), and only values within the interval are
plotted.
This argument specifies the title of the plot, and the
default does not include a title. When x is of class
posteriorchecks, the title is changed to Posterior
Correlation.
Logical. When TRUE, the plot is saved as a .pdf
file.
Additional arguments are unused.
The plotMatrix function produces one of two styles of plots,
depending on the circle argument. A circle=TRUE) according to its value.
Although plotMatrix does not provide the same detail as a
numeric matrix, it is easier to discover elements of interest
according to color (and size when circle=TRUE).
The plotMatrix function is not inherently Bayesian, and does
not include uncertainty in matrices. Nonetheless, it is included
because it is a useful graphical presentation of a numeric matrices,
and is recommended to be used with the posterior correlation matrix in
an object of class posteriorchecks.
When x is an object of class bayesfactor, matrix
B is plotted. When x is an object of class
demonoid (if it is a matrix), iterquad, laplace,
pmc, or vb, the covariance matrix Covar is
plotted. When x is an object of class posteriorchecks,
the posterior correlation matrix is plotted.
This is a modified version of the circle.corr function
of Taiyun Wei.
# NOT RUN {
library(LaplacesDemon)
### Although it is most commonly used with an object of class
### posteriorchecks, it is applied here to a different correlation matrix.
data(mtcars)
plotMatrix(cor(mtcars), col=colorRampPalette(c("green","gray10","red"))(100),
cex=1, circle=FALSE, order=TRUE)
plotMatrix(cor(mtcars), col=colorRampPalette(c("green","gray10","red"))(100),
cex=1, circle=TRUE, order=TRUE)
# }
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