similar to: factanal doesn't work with singular covariance matrix?

Hi, I have a set of covariance matrices but not the original data. I want to carry out some exploratory factor analysis. So, I am trying to construct a covariance matrix list as the input for factanal. I can construct a list which includes the cov, the centers, and the n.obs. But it doesn't work. I get an error that says "Error in sqrt(diag(cv)) : Non-numeric argument to mathematical

# 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 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

Are there any functions available to do a factor analysis with fewer observations than variables? As long as you have more than 3 observations, my computations suggest you have enough data to estimate a factor analysis covariance matrix, even though the sample covariance matrix is singular. I tried the naive thing and got an error: > set.seed(1) > X <- array(rnorm(50), dim=c(5,

Dear R-help, Using R 1.2.1 on Windows98 to run a factor analysis on a 64x150 matrix of data generated from a simulation model, factanal() reported that it failed to find a solution. Looking at the factanal code, I see the immediate condition that triggered the result: if (best == Inf) stop("Unable to optimize from these starting value(s)") So I am sure factanal() is giving

Hi everyone, I'm running a factor analysis on a correlation matrix with 32 rows and columns. I get the following error when I issue the command sequence mich.fac1 <- factanal(michcor, factor=1) Error in solve.default(cv) : system is computationally singular: reciprocal condition number = 3.24729e-18 I'd really appreciate an explanation for this error and a solution to the problem

--=-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 =

Hi list, I'm performing a series of confirmatory factor analysis on different groupings of items from data collected with questionnaires. There are some missing values. For those sets with no missing values I call factanal(datamatrix,factors=n) where datamatrix is a table of all observations for the items under investigation. This call fails when there are missing values. help(factanal)

Hi. I tried running the following code. I don't understand the error. mydata <- read.table("C:/dataForR/radiology/wordFrequencies.csv", header=TRUE, sep=",") > dim(mydata) [1] 982 924 mydataN<-mydata[,-923] > dim(mydataN) [1] 982 923 cor(mydataN) factanal(mydataN, factors=3) Error in solve.default(cv) : Lapack routine dgesv: system is exactly singular

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

Hello, I have been trying for weeks to compile a 64-bit Rblas. I started with ATLAS where I have had success in the past, but 64 bit was not behaving, and as each compilation takes between 9 and 12 hours, "test, check, and revise" was not going to be really viable. I therefore switched to OpenBLas (OPBL). I was successful in compiling R-3.0.1 and an OPBL-based BLAS for Windows 64bit

Hi, I have obtained some results with factanal that seem to support a hypothesis I already had, and I'd like to verify that I can indeed conclude this from this new analysis. We had subjects reproduce perceived trajectories with a device that allowed us to measure spatial position (the path) and the device's orientation at any of those positions. From this, we calculated the rotation of

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]]

hi, Does factanal() force the user to use the formula interface if they wish to specify an na.action? v1 <- c(1,1,1,1,1,1,1,1,NA,1,3,3,3,3,3,4,5,6) v2 <- c(1,2,1,1,1,1,2,1,2,1,3,NA,3,3,3,4,6,5) v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) v4 <- c(3,3,4,NA,3,1,1,2,1,1,1,1,2,NA,1,5,6,4) v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) m1

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

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

Dear friends, I'm using R to produce the following Factor Analysis: > matriz.cor<-hetcor(matrix(as.factor(data), ncol=variables, byrow=T))$correlations > factanal(x=data, factors=2, covmat=matriz.cor, scores='regression') Then the screen output shows the following message: Error en factanal(x = data, factors = 2, covmat = matrix, : requested scores without

> Does factanal() force the user to use the formula interface if they > wish to specify an na.action? Short answer: yes. Long answer: The handling of na.action is a built in part of the formula processing functions, so it's automatic when dealing with a formula. There are also downstream effects on predict() and resid() that are worked out for the formula case, but aren't clear

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 <-