similar to: Comparison of % variance explained by each PC before AND after rotation

Hi everyone- I have used several methods to calculate principal components rotated using the varimax procedure. This is simple enough. But I would like to calculate the % of variance explained associated with each PC before and after rotation. factanal returns the % of variance explained associated with each PC but I cannot seem to get it to change after rotation. Many thanks for your

On p. 48 of "Statistics Complements" to the 3rd MASS edition, http://www.stats.ox.ac.uk/pub/MASS3/VR3stat.pdf I read that the orthogonal rotations of Z Lambda^-1 remain uncorrelated, where Z is the PC and Lambda is the diag matrix of singular values. However, the example below that text is > A <- loadings(ir.pca) %*% diag(ir.pca$sdev) If ir.pca$sdev are the singular values,

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

Dear R Helper, I am writing because I seek to perform a varimax rotation on my Principal Components Analysis (PCA) solution. (I have been performing PCA's using the eigen command in R.) If you can tell me how to perform this rotation when I use the eigen command (or the princomp command) I would be thrilled. Thanks so much! Wendy Treynor Ann Arbor, MI USA

A bit of a newbee to R and factor rotation I am trying to understand factor rotations and their implementation in R, particularly the GPArotation library. I have tried to reproduce some of the examples that I have found, e.g., I have taken the values from Jacksons example in "Oblimin Rotation", Encyclopedia of Biostatistics

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

Dear Folks I need to rotate PCA loadings and scores using R. I have run a pca using princomp and I have rotated PCA results with varimax. Using varimax R gives me back just rotated PC loadings without rotated PC scores. Does anybody know how I can obtain/calculate rotated PC scores with R? Your kindly help is appreciated in advance Francesca [[alternative HTML version deleted]]

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

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

Haj i try to perform a principal component analysis by using a tetrachoric correlation matrix as data input tetra <- tetrachoric (image_na, correct=TRUE) t_matrix <- tetra$rho pca.tetra <- principal(t_matrix, nfactors = 10, n.obs = nrow(image_na), rotate="varimax", scores=TRUE) the problem i have is to compute the individual factor scores from the pca. the code runs perfect

I am running a PCA, but would like to rotate my data and limit the number of factors that are analyzed. I can do this using the "principal" command from the psych package [principal(my.data, nfactors=3,rotate="varimax")], but the issue is that this does not report scores for the Principal Components the way "princomp" does. My question is: Can you get an

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:

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

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

Dear R-Helpers, I've looked high and low for a function that provides frequencies, proportions and cumulative proportions side-by-side. Below is the table I need. Is there a function that already does it? Thanks, Bob > # Generate some test scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > >

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

Hi, I am puzzeled with a differing result of princomp in R and FACTOR in SPSS. Regarding the amount of explained Variance, the two results are the same. However, the loadings differ substantially, in the unrotated as well as in the rotated form. In both cases correlation matrices are analyzed. The sums of the squared components is one in both programs. Maybe there is an obvious reason, but I

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

Hola! No he podido consultar la doc. del paquete ade4, algo debe estar caído en CRAN ahora mismo. Dos cosas sobre la metodología -aun desconociendo los detalles de cómo lo hace ade4: El output de un PCA, los "pesos" de cada variable en las dimensiones de los componentes se interpretan como correlaciones, a mayor valor absoluto mayor asociación variable-componente. Ahora, como tales