Function to to draw and visualise correlation matrices using lattice. The primary purpose is as a tool for exploratory data analysis. Hierarchical clustering is used to group similar variables.
corPlot(
mydata,
pollutants = NULL,
type = "default",
cluster = TRUE,
method = "pearson",
dendrogram = FALSE,
lower = FALSE,
cols = "default",
r.thresh = 0.8,
text.col = c("black", "black"),
auto.text = TRUE,
...
)As well as generating the plot itself, 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.
A data frame which should consist of some numeric columns.
the names of data-series in 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.
Should the data be ordered according to cluster analysis. If
TRUE hierarchical clustering is applied to the correlation matrices
using hclust to group similar variables together. With many
variables clustering can greatly assist interpretation.
The correlation method to use. Can be “pearson”, “spearman” or “kendall”.
Should a dendrogram be plotted? When TRUE a
dendrogram is shown on the right of the plot. Note that this will only
work for type = "default".
Should only the lower triangle be plotted?
Colours to be used for plotting. Options include
“default”, “increment”, “heat”, “spectral”,
“hue”, “greyscale” and user defined (see openColours
for more details).
Values of greater than r.thresh will be shown in bold
type. This helps to highlight high correlations.
The colour of the text used to show the correlation values. The first value controls the colour of negative correlations and the second positive.
Either 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.
Other graphical parameters passed onto lattice:levelplot,
with common axis and title labelling options (such as xlab,
ylab, main) being passed via quickText to handle
routine formatting.
David Carslaw --- but mostly based on code contained in Sarkar (2007)
The 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.
Sarkar, D. (2007). Lattice Multivariate Data Visualization with R. New York: Springer.
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)
if (FALSE) {
## 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