similar to: two questions for R beginners

Hi, I wonder why factor scores produced by factanal are correlated, and I'd appreciate any hints from people that may help me to get a deeper understanding why that's the case. By the way: I'm a psychologist used to SPSS, so that question my sound a little silly to your ears. Here's my minimal example: *********************************************** v1 <-

Dear expeRts, sorry for such a newbie question - in PCA/factor analysis e.g. in SPSS it is possible to save scores from the factors. Is it analogously possible to "save" the implied scores from the latent variables in a measurement model or structural model e.g. using the sem or lavaan packages, to use in further analyses? Best wishes Steve Powell www.promente.org | skype

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

I try to code the ULS factor analysis descrbied in ftp://ftp.spss.com/pub/spss/statistics/spss/algorithms/ factor.pdf # see PP5-6 factanal.fit.uls <- function(cmat, factors, start=NULL, lower = 0.005, control = NULL, ...) { FAfn <- function(Psi, S, q) { Sstar <- S - diag(Psi) E <- eigen(Sstar, symmetric = TRUE, only.values = TRUE) e <- E$values[-(1:q)] e <-

Hi All, There were lot of diffrences in the R and SPSS results for Exploratory Factor Analysis.why is it so ?I used standard factor analysis functions like:-- factanal(m, factors=3, rotation="varimax") princomp(m, cor = FALSE, scores = TRUE, subset = rep(TRUE, nrow(as.matrix(m)))) print(summary(princomp(m, cor=TRUE),loadings = TRUE, cutoff = 0.2), digits = 2) prcomp(m, scale = TRUE)

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

Dear R users Happy New year to all for a start. Below is some data that I ran a factor analysis on. Using $score prints the scores for each of the three factors. However, I would like to access those factors as variable for new computations. How do I do that? In SPSS we just call fact1_1, fact2_1 etc.. Thanks for your suggestions. V ============ v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) v2

For those new to this list, I point out that Yuelin Li and I have drafted an introduction to R for psychology, trying to emphasize the things the psychologists usually do. It is at http://www.psych.upenn.edu/~baron/rpsych.pdf and http://www.psych.upenn.edu/~baron/rpsych.htm Because it is a draft, with frequent changes, we have not yet asked for it to be placed in the CRAN page. We welcome

Dear R fans ( and wiki fans), I am just writing a draft to introduce confidence intervals of various "effect sizes" to my students. Surely, I'll recommend the package MBESS in R. Currently, it means I have to recommend R's interface at first. As a statistics teacher in a dept of psychology, I often have to reply why not to teach SPSS. Psychologists and their students hate to

Dear R people: I wish to switch from SPSS to R, but there is one particular type of ANOVA design that cannot be done in R. Or more likely, it can be done, but it is nowhere documented. The problem is typical for psychologists: You have a repeated measures design with different groups of subjects. Now, this can be done with the aov command, but the number of subjects in both groups must be

Howdy! I'm a clinical psychologist desperately trying to get rid of SPSS. I just discovered R and like it quite a lot. The main reason why we're still using SPSS is the hierarchical regression where you enter bundles of variables into a linear model and get an R-sqare increase tested with an F-test. I already found add1 and drop1 but would rather need addn and dropn. Is there

Dear expe-R-ts, I try some test data for a factorAnalysis (resp. pca) in the sense of Prof. Ripley's MASS ? 11.1, p. 330 ff., just to prepare myself for an analysis of my own empirical data using R (instead of SPSS). 1. the data. ## The test data is (from the book of Backhaus et al.: Multivariate ## Analysemethoden. Springer 2000 [9th ed.], p. 300 ff):

Hi, is there any other routine for factor analysis in R then factanal? Basically I'am interested in another extraction method then the maximum likelihood method and looking for unweighted least squares. Thanks in advance Sigbert Klinke

Is there any R function to perform factor analysis using Principal Component Method? why factanal() method is " always "mle" " ?

Hi list members Any easy way to get standardized cronbach's alpha for a scale, as in SPSS? Thanks Steve Powell proMENTE social research research | evaluation | training & consulting Kranj?evi?eva 35, 71000 Sarajevo mobile: +387 61 215 997 | office: +387 33 556 865 | fax: +387 33 556 866 Checked by AVG Free Edition. 17:44

Me and some colleagues are planning to write a textbook together ("Statistics using R") where the target audience for the book is psychologists and students of psychology. We thought that it might be a good idea to use a Wiki when writing the text. Is that a good idea? Does anybody have any experience in that direction? What alternatives are there? The tool (Wiki) would have to be

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

Hi. It seems that nearly every time I try to use factanal I get the following response: >faa2db1<-factanal(mretdb1,factors=2,method="mle",control=list(nstart=25)) Error in factanal(mretdb1, factors = 2, method = "mle", control = list(nstart = 25)) : unable to optimize from these starting value(s) > In the case cited above, mretdb1 is synthetic data created

Hi Can anybody please suggest me about the documentation of "factanal.fit.mle()" (Not factanal()------ searching factanal.fit.mle() in R always leads to factanal()). Is there any function for doing principal component factor analysis in R. Regards Souvik Bandyopadhyay JRF, Dept Of Statistics Calcutta University [[alternative HTML version deleted]]

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