Correlation matrices with conditioning

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.

Usage

corPlot(
  mydata,
  pollutants = NULL,
  type = "default",
  cluster = TRUE,
  method = "pearson",
  use = "pairwise.complete.obs",
  dendrogram = FALSE,
  lower = FALSE,
  cols = "default",
  r.thresh = 0.8,
  text.col = c("black", "black"),
  auto.text = TRUE,
  plot = TRUE,
  ...
)

Arguments

mydata

A data frame which should consist of some numeric columns.

pollutants

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

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.

cluster

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.

method

The correlation method to use. Can be “pearson”, “spearman” or “kendall”.

use

How to handle missing values in the cor function. The default is "pairwise.complete.obs". Care should be taken with the choice of how to handle missing data when considering pair-wise correlations.

dendrogram

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".

lower

Should only the lower triangle be plotted?

cols

Colours to be used for plotting. Options include “default”, “increment”, “heat”, “spectral”, “hue”, “greyscale” and user defined (see openColours for more details).

r.thresh

Values of greater than r.thresh will be shown in bold type. This helps to highlight high correlations.

text.col

The colour of the text used to show the correlation values. The first value controls the colour of negative correlations and the second positive.

auto.text

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.

plot

Should a plot be produced? FALSE can be useful when analysing data to extract corPlot components and plotting them in other ways.

...

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.

Value

an openair object

Details

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.

References

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

Author

David Carslaw — but mostly based on code contained in Sarkar (2007)

Examples

## basic corrgram plot
corPlot(mydata)

## plot by season ... and so on
corPlot(mydata, type = "season")

## recover dendrogram when cluster = TRUE and plot it
res <-corPlot(mydata)

plot(res$clust)

if (FALSE) { # \dontrun{
## 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)
} # }