corPlot(mydata, pollutants = NULL, type = "default", cluster = TRUE, dendrogram = FALSE, 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 “all” use all available valid (numeric) data.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. “season”,
“year”, “weekday” and so on. For example, type
= "season" will produce four plots --- one for each season.It is also possible to choose type as another variable in
the data frame. If that variable is numeric, then the data will be
split into four quantiles (if possible) and labelled
accordingly. If type is an existing character or factor variable,
then those categories/levels will be used directly. This offers
great flexibility for understanding the variation of different
variables and how they depend on one another.
TRUE hierarchical clustering is applied to the
correlation matrices using hclust to group similar
variables together. With many variables clustering can greatly
assist interpretation.TRUE
a dendrogram is shown on the right of the plot. Note that this
will only work for type = "default".openColours for more details).r.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 “openair”. The object
includes three main components: 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).
If clustering is chosen it is also possible to add a dendrogram
using the option dendrogram = TRUE. Note that
dendrogramscan only be plotted for type = "default"
i.e. when there is only a single panel. The dendrogram can also be
recovered from the plot object itself and plotted more clearly;
see examples below.
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)
## Not run:
# ## 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)
# ## End(Not run)
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