| plotCorrmatrix {growthPheno} | R Documentation | 
Calculates and plots correlation matrices for a set of responses
Description
Having calculated the correlations a heat map indicating the magnitude of the 
correlations is produced using ggplot. In this heat map, the darker the red in 
a cell then the closer the correlation is to -1, while the deeper the blue in the cell,
then the closer the correlation is to 1. A matrix plot of all pairwise 
combinations of the variables can be produced. The matrix plot contains a scatter diagram 
for each pair, as well as the value of the correlation coefficient. The argument 
pairs.sets can be used to restrict the pairs in the matrix plot to those 
combinations within each set. 
Usage
plotCorrmatrix(data, responses, which.plots = c("heatmap","matrixplot"), 
               title = NULL, labels = NULL, labelSize = 4, pairs.sets = NULL, 
               show.sig = FALSE, axis.text.size = 20, ggplotFuncs = NULL, 
               printPlot = TRUE, ...)
Arguments
| data | A  | 
| responses | A  | 
| which.plots | A  | 
| title | Title for the plots. | 
| labels | A  | 
| labelSize | A  | 
| pairs.sets | A  | 
| show.sig | A  | 
| axis.text.size | A  | 
| ggplotFuncs | A  | 
| printPlot | A  | 
| ... | allows passing of arguments to other functions; not used at present. | 
Details
The correlations and their p-values are producced using rcorr 
from the Hmisc package. The heatmap is produced using 
ggplot and the matrixplot is produced using GGally.
Value
The heatmap plot, if produced, as an object of class "ggplot", which 
can be plotted using print; otherwise NULL is returned.
Author(s)
Chris Brien
See Also
rcorr, GGally, ggplot.
Examples
data(exampleData)
longi.dat <- prepImageData(data=raw.dat, smarthouse.lev=1)
longi.dat <- within(longi.dat, 
                    {
                      Max.Height <- pmax(Max.Dist.Above.Horizon.Line.SV1,  
                                         Max.Dist.Above.Horizon.Line.SV2)
                      Density <- PSA/Max.Height
                      PSA.SV = (PSA.SV1 + PSA.SV2) / 2
                      Image.Biomass = PSA.SV * (PSA.TV^0.5)
                      Centre.Mass <- (Center.Of.Mass.Y.SV1 + Center.Of.Mass.Y.SV2) / 2
                      Compactness.SV = (Compactness.SV1 + Compactness.SV2) / 2
                    })
responses <- c("PSA","PSA.SV","PSA.TV", "Image.Biomass", "Max.Height","Centre.Mass",
               "Density", "Compactness.TV", "Compactness.SV")
plotCorrmatrix(longi.dat, responses, pairs.sets=list(c(1:4),c(5:7)))