similar to: error in hetcor function (polycor package)?

Hello R Users, While I have attempted to dig into the R help files and I have not identified the answer to these questions, I apologize in advance if my questions were answered in the past. I also recognize that one of my questions unfortunately verges on statistical rather than code territory. I have two rather unrelated questions about using the sem and polycor packages for a relatively

Een ingesloten tekst met niet-gespecificeerde tekenset is van het bericht gescrubt ... Naam: niet beschikbaar Url: https://stat.ethz.ch/pipermail/r-help/attachments/20071029/b29e9bd5/attachment.pl

Sorry if this post should be long but I tried to give you a piece of my data to reproduce my error message using hetcor: Fehler in result$rho : $ operator is invalid for atomic vectors Zus?tzlich: Warning messages: 1: In polychor(x, y, ML = ML, std.err = std.err) : 1 row with zero marginal removed 2: In polychor(x, y, ML = ML, std.err = std.err) : the table has fewer than 2 rows Error in

I am currently working with R's polycor package and I have encountered a problem. I tried to follow the steps as outlined in the sem.pdf file where a CFA model is run using polychoric correlations. Every time I run the command sem(model, data, N=.), I get the following warning message: Warning message: In if (orthogonal) { : the condition has length > 1 and only the first element will be

Hi all, For computational reasons, I need to estimate an 18x18 polychoric correlation matrix two variables at a time (rather than trying to estimate them all simultaneously using ML). The resulting polychoric correlation matrix I am getting is non-positive definite, which is problematic because I'm using this matrix later on as if it were a legitimately estimated correlation matrix (in order

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

Hi, I would like to use the sem package to perform a path analysis (no latent variables) with a mixture of 2 nominal exogenous, 1 continuous exogenous, and 4 continuous endogenous variables. I seek advice as to how to calculate the appropriate covariance matrix for use with the sem package. I have read through the polycor package, and am confused as to the use of "numeric" for

Hi I am looking for a structural equation modeling package in R which can be used for categorical data. Is anyone aware of the existence of such a package? Would appreciate any help on this. Thank you Upasna -- --------------------------------------------------------------------- Upasna Sharma Research Scholar Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay

Hi - I am trying to fit sem model with intercepts. Here is what I have in my model. Exogeneous vars: x1 (continous), x2 (ordinal), x3 (ordinal), x4(continuous) Endogeneous vars: y1 (continuous), y2 (ordinal), y3 (ordinal) SEM model: x1 -> eta1; x2 -> eta1; x3 -> eta2; x4 -> eta2; eta1 -> y1, eta1 -> y2, eta2 -> y2, eta2 -> y3 However, in these arrow models, I

A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

Dear R-users Could anyone tell me which library contains a function to compute polycoric correlations? I wonder the same question was asked a while ago, but I could not locate the mail in the R-help archives. Sorry for bothering you. Sincerely ------------------------ Hiroto Miyoshi ???? h_m_ at po.harenet.ne.jp

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.

Hi, I'm using the SEM package to estimate a model with a binary variabele as dependent variable. In the literature I have to use then the correlation matrix, made by function hetcor(). Literature also says that the standard errors are not correct then. My question is if somebody knows what the impact is on the estimated coefficients. If I want to calculate the estimated probability I see a

Dear list members, I've uploaded a new package, called polycor (version 0.5-0), to CRAN. The package has functions for computing polychoric and polyserial correlations, either by maximum-likelihood (in which case, standard errors are available) or by faster approximations. There's also a function to compute "heterogeneous" correlation matrices composed of product-moment,

Dear list members, I've uploaded a new package, called polycor (version 0.5-0), to CRAN. The package has functions for computing polychoric and polyserial correlations, either by maximum-likelihood (in which case, standard errors are available) or by faster approximations. There's also a function to compute "heterogeneous" correlation matrices composed of product-moment,

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

Hi there, Am I correct to believe that tetrachoric correlation is a special case of polychoric correlation when there are only two levels to the ordered factor? Thus it should be okay to use hetcor from the polycor package to build a matrix of correlations for binary variables? If this is true, how can one estimate 95% confidence intervals for the correlations? My guess would be mat =

I am trying to run a confirmatory factor analysis using the SEM package. My data are ordinal. I have read http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf. When I apply the hetcor function, I receive the following error: Error in checkmvArgs(lower = lower, upper = upper, mean = mean, corr = corr, : at least one element of 'lower' is larger than 'upper' Example:

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->