similar to: rgl: ellipse3d with axes

Hi I'm trying to make a 3d plot showing a point cloud, the corresponding data ellipse and the principal axes of the ellipse as vectors. library(rgl) data(trees) cov <- cov(trees) mu <- mean(trees) plot3d(trees, type="s", size=0.5, col="blue", cex=2) In this step, an extra box is added. I've tried using box=FALSE, but it has no effect. # how to avoid the

I have a 3xN matrix of parameters obtained from N regressions where the 3 parameters are jointly statistically significant. I would like to reproduce a 3D confidence ellipsoid projecting 2D ellipses onto the XY plane as in Figure 5.2 in this

Dear all, I write to the community to know if other people expierenced the same problem. I want to save a rgl scene where I plotted a 3d cloud of spheres whose radius is set proportional to a given variable and coloured by another group variable and with 95% ellipsoids for distribution of any group. I had no problem at all for coding but I had two problem when saving the scene (in any format)

Dear list, I'm trying to visualise some ellipsoidal shapes in 3D. Their position, axes, and angular orientation can be arbitrary. I saw an ellipse3d function in rgl; however it is heavily oriented towards the statistical concept of ellipse of confidence, whilst I am just concerned with the geometrical object. Below is my current implementation. It is quite slow when creating many shapes, so

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

Dear List, I don't get scatter3d to color the sheres according to the '|' argument. library(car) scatter3d(prestige ~ income + education|type, data=Prestige) The spheres on my screen are all colored the same and they are not conditional on Prestige$type. On the other hand: Fit3d and Ellipse3d are colored according to the group argument. rgl_0.92.879 car_2.0-12 R version 2.15.0

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

Hello, Can you get eigenvalues in addition to eigevectors using prcomp? If so how? I am unable to use princomp due to small sample sizes. Thank you in advance for your help! Rebecca Young -- Rebecca Young Graduate Student Ecology & Evolutionary Biology, Badyaev Lab University of Arizona 1041 E Lowell Tucson, AZ 85721-0088 Office: 425BSW rlyoung at email.arizona.edu (520) 621-4005

I have a general style question about R coding. Suppose I'm writing a function (foo1) that calls other functions (foo2, foo3, ...) which have complicated argument lists (e.g. optim(), plot()), _and_ I may be calling several different functions in the body of foo1. Since foo2 and foo3 have different sets of arguments, I can't just use "..." ; I did write some code a while ago

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

I'm trying to make a screeplot including the Cumulative Proportion of the Variance, something that can easily be done in S-Plus with 'screeplot(pc.object,cumulative=T)'. How can I access the Proportion of Variance in an princomp object and how could I get the Cumulative Proportion of the Variance on the screeplot? Many thanks in advance, Jan:-) --

Hi. I have been looking at the PCAproj function in package pcaPP (R 2.4.1) for robust principal components, and I'm trying to interpret the results. I started with a data matrix of dimensions RxC (R is the number of rows / observations, C the number of columns / variables). PCAproj returns a list of class princomp, similar to the output of the function princomp. In a case where I can

Hello, I have a short question about the prcomp function. First I cite the associated help page (help(prcomp)): "Value: ... SDEV the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). ROTATION the matrix of variable loadings

Hello, I am using function princomp from the package psych. I have my principle component object mypc: mypc <- princomp(covmat=mycor) plot(mypc) # shows me a screeplot Question: how could I actually see the values displayed in the screeplot. I don't mean on the graph - I just want to know the actual value for each component (e.g., 10, 3.2, 1.8, etc.) I need to know how much variance,

Hi, Am wanting to save the summary of a PCA to file. Have tried: > write.table(summary(PCA), file="PCAvar.txt", sep="\t") but receive: Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "summary.princomp" into a data.frame What am I doing wrong? Thanks -- View this message in context:

Hi everyone, I am doing PCA with labdsv package. I was trying to create a biplot graphs in order to observe arrows related to my variables. However when I run the script for this graph, the console just keep saying: *Error in nrow(y) : element 1 is empty; the part of the args list of 'dim' being evaluated was: (x)* could please someone tell me what this means? what i am doing

I've been working on a revised version of prcomp and princomp. Below is my current draft of prcomp, which is marginally different from V&R. I've added center and scale as optional arguments. However, scale causes the following: > zi _ prcomp(iris[,,2]) Warning: ignored non function "scale" because scale is both a variable and a function. Is there any way to avoid this

Hi, my data frame consist of 8 Variables and 120 000 observations. With those datas I am running a PCA and after I want to calculate the Volume of the PCA-cloud of certain subsets of my data. Does anyone have an idea about a function that can do this? Thanks [[alternative HTML version deleted]]

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C