corPlot(mydata, pollutants = NULL, type = "default", cluster = TRUE,
cols = "default", r.thresh = 0.8, text.col = c("black", "black"),
auto.text = TRUE, ...)mydata to be plotted by corPlot. The
default option NULL and the alternative
type determines how the data are split
i.e. conditioned, and then plotted. The default is will
produce a single plot using the entire data. Type can be
one of the built-in types as detailed in cutData
e.g. TRUE hierarchical clustering
is applied to the correlation matrices using
hclust to group similar variables together. With
many variables clustering can greatlyopenColoursr.thresh
will be shown in bold type. This helps to highlight high
correlations.TRUE (default) or
FALSE. If TRUE titles and axis labels will
automatically try and format pollutant names and units
properly e.g. by subscripting the `2' in NO2.lattice:levelplot, with common axis and title
labelling options (such as xlab, ylab,
main) being passed via quickText to handle
routine formattcorPlot also
returns an object of class call, the command
used to generate the plot; data, the data frame of
summarised information used to make the plot; and
plot, the plot itself. If retained, e.g. using
output <- corPlot(mydata), this output can be used
to recover the data, reproduce or rework the original plot
or undertake further analysis. Note the denogram when
cluster = TRUE can aslo be returned and plotted. See
examples.An openair output can be manipulated using a number of
generic operations, including print, plot and
summary. See openair.generics for
further details.
corPlot function plots correlation matrices. The
implementation relies heavily on that shown in Sarkar
(2007), with a few extensions.Correlation matrices are a very effective way of
understating relationships between many variables. The
corPlot shows the correlation coded in three ways:
by shape (ellipses), colour and the numeric value. The
ellipses can be thought of as visual representations of
scatter plot. With a perfect positive correlation a line at
45 degrees positive slope is drawn. For zero correlation
the shape becomes a circle. See examples below.
With many different variables it can be difficult to see
relationships between variables i.e. which variables tend
to behave most like one another. For this reason
hierarchical clustering is applied to the correlation
matrices to group variables that are most similar to one
another (if cluster = TRUE.)
It is also possible to use the openair type option
to condition the data in many flexible ways, although this
may become difficult to visualise with too many panels.
Friendly, M. (2002). Corrgrams : Exploratory displays for correlation matrices. American Statistician, 2002(4), 1-16. doi:10.1198/000313002533
taylor.diagram from the plotrix package from
which some of the annotation code was used.# load openair data if not loaded already
data(mydata)
## basic corrgram plot
corPlot(mydata)
## plot by season ... and so on
corPlot(mydata, type = "season")
## recover dendogram when cluster = TRUE and plot it
res <-corPlot(mydata)
plot(res$clust)
## a more interesting are hydrocarbon measurements
hc <- importAURN(site = "my1", year = 2005, hc = TRUE)
## now it is possible to see the hydrocarbons that behave most
## similarly to one another
corPlot(hc)Run the code above in your browser using DataLab