similar to: fa (psych) output oblique.scores=TRUE vs. FALSE

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

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

We have just put a package GPArotation on CRAN. The functions in this package perform an number of different orthogonal and oblique rotations for factor analysis, using the gradient projection algorithm described in Coen A. Bernaards and Robert I. Jennrich (2005), "Gradient Projection Algorithms and Software for Arbitrary Rotation Criteria in Factor Analysis, ", Educational and

Dear R help I have conducted a fa() analysis, and I want to use fa.diagram to assess the extent to which the 11 latent factors predict the 37 items in a psychological battery. However, the display on the screen has very large font size for the coefficients of the relationship between the 11 factors and the 37 items, so the numbers overlap and are therefore illegible. When I output this to a

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 seem to find only two types of rotation for the factanal function in R, the Varimax and Promax, but is it possible to run a orthogonal and oblique rotations in R? Thanks in advance Rosario

Dear R community, I have recently discovered the package oblique.tree and I must admit that it was a nice surprise for me, since I have actually made my own version of a kind of a classifier which uses the idea of oblique splits (splits by means of hyperplanes). So I am now interested in comparing these two classifiers. But what I do not seem to understand is why the function

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

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

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

I am sure there is a simple way to do the following, but i haven't been able to find it. I am hoping a merciful soul on R-help could point me in the right direction. I am doing a factor analysis on survey data with missing values. to do this, I run: FA1<-factanal(na.omit(DATA), factors = X, rotation = 'oblimin', scores = 'regression') Now that I have my factors and

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

Dear all, I just noticed that the 0.9 update for FrF2 did not work out for Mac OS due to an error in an example that ran without error on all other platforms. I do not find any reason for this. In the past, umlauts or tab characters have sometimes been an issue, but I didn't find any of these. The function definition is FrF2(nruns = NULL, nfactors = NULL, factor.names = if

my system.reg contains the following fonts [Software\\Microsoft\\Windows\\CurrentVersion\\Fonts] 1139543017 "Bitstream Vera Sans Bold (TrueType)"="VeraBd.ttf" "Bitstream Vera Sans Bold Oblique (TrueType)"="VeraBI.ttf" "Bitstream Vera Sans Mono Bold (TrueType)"="VeraMoBd.ttf" "Bitstream Vera Sans Mono Bold Oblique

my user.reg contains the following external fonts Software\\Wine\\Fonts\\External Fonts] 1139540729 "Bitstream Vera Sans Bold (TrueType)"="VeraBd.ttf" "Bitstream Vera Sans Bold Oblique (TrueType)"="VeraBI.ttf" "Bitstream Vera Sans Mono Bold (TrueType)"="VeraMoBd.ttf" "Bitstream Vera Sans Mono Bold Oblique

Hi, I am trying to calculate Principal Component Scores per id per year using the psych package. The following lines provide the scores per obeservation pca = data.frame(read.table(textConnection(" id year A B C D 1001 1972 64 56 14 23 1003 1972 60 55 62 111 1005 1972 57 51 10 47 1007 1972 59 49 7 10 1009 1972 65 50 9 32 1011 1972 52 58 3 5 1013

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I forgot to mention that this analysis was carried out using the Windows version . Perhaps this problem is windows specific. Peter Peter Ho wrote: > Prof. Ripley, > > I am sending this to you again, as the first one was undelivered. > > > Peter > > ripley at stats.ox.ac.uk wrote: > > >>> There is no intended limit, but there was a bug fix in 1.5.0. If

Dear Professor Revelle and R-helpers, This is a two-part question: the first part is general, and the second is specific to the psych package. First question: In the past I've constructed composite variables from questionnaire data by using rowMeans(), and then correlating items with the scale using cor() as an informal check for "bad" items. Over the weekend I decided to take a

Dear R-devel list members, Ben Fairbank draw it to my attention that factanal() (in the stats package) doesn't report factor correlations for oblique rotations. Looking at the source, I see that factanal also doesn't save the factor-transformation (rotation) matrix from which these correlations can be computed. I've modified the source, attached below, so that the transformation