R: corrgram plot with conditioning

corPlot {openair} | R Documentation

## corrgram plot with conditioning

### Description

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",
      dendrogram = FALSE,
      lower = FALSE,
      cols = "default",
      r.thresh = 0.8,
      text.col = c("black", "black"),
      auto.text = 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”.
`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.
`...` | 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.

### 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.

### Value

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`.

### Author(s)

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

### 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

### See Also

`taylor.diagram` from the `plotrix` package from which some of the annotation code was used.

### Examples

    # 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)

[Package openair version 2.8-5 Index]