similar to: Creating SubScale Scores

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

Hello! I'd like to create an assets-based economic indicator using data from a national household survey. The economic indicator is to be the first principal component from a principal components analysis, which (given the source of the data) I believe should take in consideration the sampling weights of the observations. After running the PCA with svyprcomp(), from the survey package, I

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

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

Hello everyone. In my last post I did not explained my problem quite well. I made a principal component analysis and took the 2 first principal components. I made ​​a chart of my points based on the score of the 2 PC. I would like to add on this graph a 95% confidence region. To do this I used the ellipse function as follows: pcsref=PC$score[data[,1]==ref,1:2] #matrix containing the scores

Hi all, Does anyone know how to get the independent components and loadings from an Independent Component Analysis (ICA), as well as principal components and loadings from a Pricipal Component analysis (PCA) using the fastICA package? Or perhaps if there's another way to do ICAs in R? Below is an example from the fastICA manual (http://cran.r-project.org/web/packages/fastICA/fastICA.pdf)

Dear All, It might be a simple question. But I could not find the answer from function “prcomp” or “princomp”. Does anyone know what are the codes to get coefficient and scores of Principal component analysis in R? Your reply will be appreciated! Best Zunqiu [[alternative HTML version deleted]]

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?

Hi all, I was hoping that someone could verify this for me- when I run princomp() on a matrix, it is my understanding that the scores slot of the output is a measure of how well each row correlates (for lack of a better word) with each principal component. i.e. say I have a 300x6 log2 scaled matrix, and I run princomp(). I would get back a $scores slot that is also 300x6, where each value

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

Hello all, I've run a principal component regression using the PLS package. I then applied varimax rotation (i.e., using http://stat.ethz.ch/R-manual/R-patched/library/stats/html/varimax.html). I cannot figure out how to extract the factor loadings post-varimax. Is there a command to do this? scores(x) does not do it. Thanks and happy holidays -- View this message in context:

My previous post about prcomp and princomp was done in some haste as I had long ago indicated to Kurt that I would try to have this ready for the June release, and it appeared that I would miss yet another release. I also need to get it out before it becomes hopelessly buried by other work. Brian Ripley kindly pointed out some errors, and also pointed out that I was suggesting replacing some

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

I am really having trouble with getting the right syntax for my model. Here is a truncated version of my data: > data Ind Treatment Order Date PC1 1 PER14 SC 3rd 4-May-09 0.5704611 2 PER14 SH 1st 26-Apr-09 0.5329025 3 PER14 AC 2nd 29-Apr-09 2.1392279 4 PER25 SC 2nd 29-Apr-09 -0.2083382 5 PER25 SH 3rd 3-May-09 3.7818356 6

Version 2.0-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Version 2.0-0 of the pls package is now available on CRAN. The pls package implements partial least squares regression (PLSR) and principal component regression (PCR). Features of the package include - Several plsr algorithms: orthogonal scores, kernel pls and simpls - Flexible cross-validation - A formula interface, with traditional methods like predict, coef, plot and summary - Functions

Hi, The help file on princomp says that 'The signs of the columns of the loadings and scores are arbitrary, and so may differ between differnt programs for PCA, and even between different builds of R'. Does anyone know 1. Whether this applys to all functions like svd, eigen ... etc? 2. During a leave-one-out cross step, I notice the loadings also change sige when a different

I'm working with a data collected through complex survey design. My goal is to conduct a factor analysis to extract two a priori, known factors, and to get factor scores for these factors. Unfortunately, the "svyfactanal" procedure from the Survey package does not allow for the calculation of either Thompson regression scores or Bartlett scores. So, I found several sources that say

dear R wizards: the good news is that I know how to omit missing observations and run a principal components analysis. p= princomp( na.omit( dataset ) ) p$scores[ ,1] # the first factor (where dataset contains missing values; incidentally, princomp(retailsmall, na.action=na.omit) does not work for me, so I must be doing something wrong, here.) the bad news is that I would like NA

Hi I have used fa() to perform a factor analysis of a psychological battery which is thought to have 11 factors. I can identify which factors the loadings relate to easily enough because I can see which items are loading onto each of the columns in the $loading output. However, how can I identify which items or loadings are being used to create each of the columns in the $scores output? I have