similar to: Color coding plotted numbers

Hello I've created a 3d scatterplot, and had no problems labeling the points. However, I've been really struggling to change the color of the points based on a factor (see 'group' below). Is such a thing possible? My data look like this: food group x y z apple fruit 0.216 -0.112 -0.893 orange fruit 0.814 0.097 0.460 broccoli veg -0.239 0.240 -0.425 banana

I'm looking for a way to online resize a Windows disk -- i.e., be able to resize the disk without shutting down, rebooting, or detaching the disk. Is this at all possible? Or am I just barking up the wrong tree? I'm not finding a way to do this and even Amazon has a weird workaround, in which the user must write data to the newly resized-drive in order to recognize the new size

Hello together, this is ma first post, so please aplogize me if post this in the wrong section. I have problem concerning ma two matrizes. After a regressione and so on, I got two matrizes Matrixres contains the results of ma calculation. Matrixr contains my detiene, which where Aldo used for the regression. Please ser the following code: #Datei einlesen residual =

I'm using R 1.6.2 on a Windows 2000 machine. I've plotted the results of an MDS run labeled by a numerical ID, and color coded by a group code: plot(cv.mds.spr$points, type="n", main="Non-Metric Multidimensional Scaling of SprRun CV Watersheds") text(cv.mds.spr$points, labels = as.character(cv.wshed.id.spr), col = codes(cv.wshed.grp), cex=.75) Question is, how do I

Cordial saludo adjunto la base de datos y el script: eu60<- read.csv(file.choose(), header=T, sep=";", dec=".", row.names=1) eu60.pca <- PCA(eu60, quali.sup=19) eu60.data <- cbind.data.frame(eu60[,19], eu60.pca$ind$coord) eu60.ellipse <- coord.ellipse(eu60.data, bary=TRUE) plot.PCA(ellipse=eu60.ellipse, cex=0.8) El 4 de diciembre de 2013 17:35, Camilo Calle

Hi all, I am doing bootstrap on a distance matrix, in which samples have been drawn with replacement. After that I do PCA on a resulted matrix, and these 2 steps are repeated 1000 times. pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 to 1000 SampleD is a new matrix after resampling; I am getting the following error message, which I don't understand: ....

Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

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

Dears, I've been learning biplot (Gabriel, 1971) and I found the function 'biplot', inside of the package 'stats', useful but, a bit limited. So, I'm thinking to start a colaborative package to enhance this methods to other multivariate methods. In this way, I would like to start it, making public a new function (biplot.pca, still in development, but running) that make

Dear R users, i would like to apply a PCA on image data for data reduction. The image data is available as three matrices for the RGB values. At the moment i use x <- data.frame(R,G,B)#convert image data to data frame pca<-princomp(x,retx = TRUE) This is working so far. >From this results then i want to create a new matrix from the first (second..) principal component. Here i stuck.

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context:

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

Hi, Suppose I have the following data frame. __BEGIN__ > library(MASS) > data(crabs) > crab.pca <- prcomp(crabs[,4:8],retx=TRUE) > crab.pca$rotation PC1 PC2 PC3 PC4 PC5 FL 0.2889810 0.3232500 -0.5071698 0.7342907 0.1248816 RW 0.1972824 0.8647159 0.4141356 -0.1483092 -0.1408623 CL 0.5993986 -0.1982263 -0.1753299 -0.1435941 -0.7416656 CW

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

Hi all, I' m trying to figure out if it is appropriate to do a PCA having only categorical data (not ordinal). I have only find the following quote: One method to find such relationships is to select appropriate variables and to view the data using a method like Principle Components Analysis (PCA) [4]. This approach gives us a clear picture of the data using KL-plot of the PCA. However, the

Hi, I have to construct an ellipse interval region on a PCAbiplot, I have my parameters "a" and "b" and I would apply the formula: draw.ellipse(x, y, a = , b = ) I have done a PCA on my data so I have my scores and loading for the first and second component, but my answer is what I have to choose as X and Y into the formula? if "a" and "b" are scalars or

Hi There, I'm attempting to plot 10 values on a three-dimensional PCA with text labels next to each point. While i have no trouble doing this on 2D plots using the 'text' or 'textxy' function, I cannot find a function to do this on a 3D plot. I am using princomp for my PCA: >PCA<-princomp(eucdata, cor=TRUE) >PCA$scores [,1:3] # the three principal components i

Hello everybody, I Would like to get some help to plot together, Principal Components Analysis (PCA) and clusters. I am handling environmental data from 25 locations spread across 5 different ecosystems.When grouped into 5 clusters, locations from different ecosystems are arranged in the same group. So, I want to plot together PCA and Clusters, in a such way that locations belonging to the same

I start a clean session of R 1.9.1 on Windows and I run the following code: > library(MASS) > data(painters) > pca.painters <- princomp(painters[ ,1:4]) > loadings(pca.painters) Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Composition 0.484 -0.376 0.784 -0.101 Drawing 0.424 0.187 -0.280 -0.841 Colour -0.381 -0.845 -0.211 -0.310 Expression 0.664 -0.330 -0.513

Dear all, I'm using R 2.8.1 under Vista. I programmed a Simulation with the code enclosed at the end of the eMail. After the simulation I want to analyse the columns of the single simulation-runs, i.e. e.g. Simulation[[1]][,1] sth. like that but I cannot address these columns... Can anybody please help? Best, Thomas ############################ CODE ############################