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)))