similar to: outer problem

I have been trying to use lavaan (version 0.4-7) for a simple path model, but the program seems to be computing far less degrees of freedom for my model then it should have. I have 7 variables, which should give (7)(8)/2 = 28 covariances, and hence 28 DF. The model seems to only think I have 13 DF. The code to reproduce the problem is below. Have I done something wrong, or is this something I

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

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Dear List, First off, my deepest gratitude to the Sweave developers: this tool has improved my quality greatly. A question in my work I use \Sexpr{} statements scalar values and the xtable package for all manner of tables. What I'd like to do is to use a vector inline, rather than a whole separate table. Something like: %%%%%%%%%%%%%%%% begin code % Latex junk % Sweave block:

Dear all, I've sent this question 2 days ago and got response from Sarah. Thanks for that. But unfortunately, it did not really solve our problem. The main issue is that we want to use our own (manipulated) covariance matrix in the calculation of the mahalanobis distance. Does anyone know how to vectorize the below code instead of using a loop (which slows it down)? I'd really appreciate

R version: 1.7.1 OS: Red Hat Linux 7.2 When "covmat" is supplied in princomp(), the output value "center" is all NA's, even though the input matrix was indeed centered. I haven't read anything about this in the help file for princomp(). See code below for an example: pc2$center is all NA's. Jerome Asselin x <- rnorm(6) y <- rnorm(6) X <- cbind(x,y)

--=-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 =

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

All, I am working with NIR spectral data and it was great to find that the example in ?plsr also used spectral data. Unfortunately, I am having difficulty figuring out how the "yarn" dataset is structured to allow for the plsr model to read: library(pls) data(yard) yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls")

Hi, I had a look at the MASS4 scripts in the MASS package, in Ch 11.3 Factor Analysis, there is a section of codes like: data(ability.cov) ability.FA <- factanal(covmat = ability.cov, factors = 1) ability.FA (ability.FA <- update(ability.FA, factors = 2)) #summary(ability.FA) round(loadings(ability.FA) %*% t(loadings(ability.FA)) + diag(ability.FA$uniq), 3)

I''m struggling with activerecord and mysql blobs (uploading files)... blobs over 400K-ish cause the below exception... ActiveRecord::StatementInvalid in File#upload Mysql::Error: Lost connection to MySQL server during query: I''ve tried blob, mediumblob, and longblob... I''m using windows xp for development - and have not installed the mysql gem... simlar google

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, I have the following piece of code that is part of a larger function. This piece is the most time consuming part of the function, and I would like to make this a bit more efficient. Could anyone suggest a way to do this faster? In particular, I would like to replace the nested "for" loop with a faster construct. I tried things like "kronecker" and "outer"

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

Greetings, all: I am using factanal in R. When I enter a matrix or a formula, the print method winds up with something like this: Test of the hypothesis that 6 factors are sufficient. The chi square statistic is 28.1 on 22 degrees of freedom. The p-value is 0.172 But when I enter a covmat, the print method winds up with something like this: The degrees of freedom for the model is 22 and the

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

I am doing Principal Component Analysis (PCA) on assets data for household income prediction. The problem is that the assets data are rank ordered (usually binary ... possess car/don't possess car), so the normal correlation is inappropriate for the calculation of the PCA. Instead one has to use the polychoric correlation coefficient. It uses the "random.polychor.pa" package.

We have been contemplating a complete rewrite of our web software, Noosphere, that runs PlanetMath (http://www.planetmath.org) and PlanetPhysics (http://www.planetphysics.org). The current development framework is simlar to slashcode (Apache-mod_perl-mysql). One big limitation is not being able to run different Noosphere modules installed for different sites on the same Apache server.