On 4/17/2009 10:43 AM, Alejandro González wrote:
Dear all,
I'm trying to perform a principal components analysis of a sample of
individuals, and to plot it in 3D, assigning different colors according
to the population each individual belongs to. Given that the matrix I
have to use for the PCA cannot contain cualitative variables (here, the
population of origin), I have no idea how can I do this in R. ANy help?
Thanks in advance
As long as the order of rows in the matrix is unchanged when you
calculate the pc scores, you should be able to use a separate dataframe
or vector to give information about each row, and to work out a colour
for plotting it.
For example:
X <- matrix(rnorm(1000), 100, 10)
# Make up a fake population based on the first column
popn <- round(X[,1])
colour <- popn - min(popn) + 1
pc <- princomp(X)
library(rgl)
plot3d(pc$scores[,1:3], col=colour)
Duncan Murdoch
Alejandro González
Departamento de Biodiversidad y Conservación
Real Jardín Botánico
Consejo Superior de Investigaciones Científicas
Claudio Moyano, 1
28014 Madrid, Spain
Tel +0034 914203017
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and provide commented, minimal, self-contained, reproducible code.
