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 greatly assist openColours for mor.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 formatting.corPlot
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.
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