Rohit, > but this package is huge ... and m new to R so if i can get some straight commands it > will be really helpful ... I never used "ord", but using the traditional "prcomp" and scatterplot3d, you can do it like this: library(scatterplot3d) data <- read.table("your_data_file"......) pca <- prcomp(data) # if you are loading a microarray, matrix you should use prcomp(t(data)) scatterplot3d(pca$x[,1], pca$x[,2], pca$x[,3], main = "PCA 3D plot") Good luck, Frederico Arnoldi _________________________________________________________________ NINGU?M PRECISA SABER O QUE VOC? EST? COMPRANDO. LEIA MAIS SOBRE ESSE ASSUNTO AQUI. rivately.aspx?tabid=1&catid=1&WT.mc_id=1590

I prefer the plots you can construct using the rgl package (which provides an R interface to the OpenGL graphics standard). However, this requires some manual manuipulations to get just the plot you want. Here is a complete example: ############# start here ################ library(rgl) # open the GL graphics windows rgl.open() # resize the graphics window programatically. # Note: there seems to be no way to pass this information # when you first create the window. offset <- 50 par3d(windowRect=c(offset, offset, 640+offset, 640+offset)) rm(offset) # clear all GL graphics elements. # Not really needed here, but useful to include in case you # need to rerun the code after fine tuning the display rgl.clear() # set the camera viewpoint. mainly useful for fine tuning; # you can often omit this and live with the defaults. rgl.viewpoint(theta=45, phi=30, fov=60, zoom=1) # generate the basic figure # assumes you have put the principal components into a structure # called "data3" with names "PC1", "PC2", and "PC3". Modify for # your situation. Also assumes that you have a vector of colors # that you want to use for each point, perhaps reflecting a # separate classification. Can omit the "colors" option if you # want. spheres3d(data3$PC1, data3$PC2, data3$PC3, radius=0.3, color=myColors, alpha=1, shininess=20) # Note that there are lots of other 'primitive' functions # in the package for other 3D shapes # set the 3D aspect ratio aspect3d(1, 1, 1) # add axes axes3d(col='black') # add labels, which are related to title and subtitle title3d("", "", "PC1", "PC2", "PC3", col='black') # set the background color bg3d("white") # can optionally set a background to "gray" and give # it reflective properties. # now work with the figure interactively to align it at the # angles that show the best separation between the groups # improve the lighting # start by working in the dark rgl.clear(type='lights') # add specular (point-source) light rgl.light(-45, 20, ambient='black', diffuse='#dddddd', specular='white') # add ambient light rgl.light(60, 30, ambient='#dddddd', diffuse='#dddddd', specular='black') # save the result as a PNG file rgl.snapshot("threePC.png") ############################### END HERE #################### Frederico Arnoldi wrote: > Rohit, > > >> but this package is huge ... and m new to R so if i can get some straight commands it >> will be really helpful ... >> > > I never used "ord", but using the traditional "prcomp" and scatterplot3d, you can do it like this: > > library(scatterplot3d) > data <- read.table("your_data_file"......) > pca <- prcomp(data) # if you are loading a microarray, matrix you should use prcomp(t(data)) > scatterplot3d(pca$x[,1], pca$x[,2], pca$x[,3], main = "PCA 3D plot") > > Good luck, > Frederico Arnoldi > > _________________________________________________________________ > NINGU?M PRECISA SABER O QUE VOC? EST? COMPRANDO. LEIA MAIS SOBRE ESSE ASSUNTO AQUI. > > rivately.aspx?tabid=1&catid=1&WT.mc_id=1590 > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >