similar to: how to get components / factors in factanal / princomp not loadings

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

Hi is there a way to plot only the loadings in a biplot (with the nice arrows), and to skip the scores? thanks christoph

Hi! I am trying to analyse with R a database that I have previously analysed with SPSS. Steps with SPSS: Factorial analysis Extraction options : I select = Principal component analysis Rotation: varimax Steps with R: I have tried it with varimax function with factanal or with princomp...and the results are different of what I have with SPSS. I think that varimax function is incorporated in

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

I'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

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May be a simple question, but not understanding why in princomp I get different results for loadings and summary for my eigenvectors and eigenvalues. When I use pc.cr$loadings using the USArrests dataset the proportion of variance is equal for each of the components, but when summary(pc.cr) is used the proportion of variance is showing different proportions. My question is why do they differ? I

I am performing a large (105 variable) factor analysis with factanal, specifying promax rotation. I kow that some loadings over 1.0 are not unsual with that rotation, but I have some as large as 1.2, which seems extreme. I am skirting the assumptions of the model by using responses on a 7-point rating scale as data; I may have to go back and compute polychoric correlations instead of product

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Dear list members, I apologize for putting this (probably) very basic question on the mailing list. I have scanned through the R website (using search) but did not found an answer. (code included below) A factor matrix is simply extracted (which can then subsequently be exported using write.table) by FACT$loadings[1:6,]. I would also like to specifically extract and export

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

Dear R users: I'm wondering if it's possible to get the residual correlation matrix when using factanal. Since factanal assumes that the errors are normally distributed and independent (provided the factor model fits the data) this would be useful. Of course you would need to submit the data to the function to get the residuals (not just their correlation matrix), but it should be possible

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

Dear R developers, Would it be possible to make `factanal` to also accept functions and not just function names for its `rotation` parameter? If I understand correctly, `do.call` also supports this. Best greetings, Stefan -------------- next part -------------- A non-text attachment was scrubbed... Name: patch.diff Type: text/x-patch Size: 568 bytes Desc: patch.diff URL:

Dear R users, When I tried to use princomp() from stats packages to do Principal Components Analysis, I am not very clear what is the "scale". And the scores are different from "PROC PRINCOMP" procedure from SAS. Using the example data from this package: restpc <- princomp(USArrests, cor = TRUE) > restpc$scale Murder Assault UrbanPop Rape 4.311735 82.500075

In R factanal output, I can't find a function to give me residuals e. I mannually got it by using x -lamda1*f1 -lamda2*f2 - ... -lamdan*fn, but the e I got are not uncorrelated with all the f's. What did I do wrong? Please help. Yijun ____________________________________________________________________________________ Be a better friend, newshound, and

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df

Dear list members, Could any expert on factor analysis be so kind to explain how to calculate AIC on the output of factanal. Do I calculate AIC wrong or is factanal$criteria["objective"] not a negative log-likelihood? Best regards Jens Oehlschl?gel The AIC calculated using summary.factanal below don't appear correct to me: n items factors total.df rest.df model.df