similar to: biserial correlation with pkg polycor

Hello, I found in the forum two threads about point biserial correlation. One of them (1) mentioned "a point-biserial correlation is just a Pearson correlation where one of the variables is dichotomous. Thus, the command is just the normal cor function". The other (2) mentioned "Professor Fox's package polycor" as a way to calculate point biserial correlation?

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 have been using the hetcor function in the polycor package. When I don't specify the use option everything runs smoothly. However, when I specify use either as "pairwise.complete.obs" or "complete.obs" I get this error Error in optim(rho, f, control = control, hessian = TRUE, method = "BFGS") : non-finite value supplied by optim Is this an error in

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 list, has anyone written a package/function in R for computing a point- biserial resp. biserial correlation? Thanks in advance Bernd

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

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

Dear community I'm pretty new to R and I'm trying to do a Point-baserial correlation for a nominal dichotomous variable with a interval scaled variable. It works fine, but the output just shows me the correlation and nothing else (p-Value would be important). I tried it with the following codes: - biseral.cor() - cor.biseral() - I also tried a polyserial() I've found on this

Dear developers of R-project, I have such data height of male persons 181, 178, 182, 160, 187, 193, 184, 184, 175, 178, 184, 184, 174, 185, 175 height of female persons 164, 165, 160, 174, 167, 161, 164, 165, 169, 175, 165, 155, 172, 164, 172, 166, 160, 159, 158, 173 I want to determine if height is dependent on gender, and I need to find, as I understand, point biserial correlation. I had tried

Dear R-listers, Can someone suggest a method for generating a finite Bernoulli sequence that is likely to have a given point-biserial correlation with an existing Bernoulli sequence? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall

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 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 have used a polycor package for categorical correlation coefficients. I run the following script. But there were no results. Could you tell me how to correct the script? Thanks in advance, vars <- names(sdi) for (i in 1:length(vars)) { for (j in 1:length(vars)) { paste(vars[i]," and ", vars[j]) polychor(vars[i], vars[j]) # corr } } -- Kum-Hoe Hwang, Ph.D.Phone :

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 R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

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

What test do I use to determine if there is a correlation between a discrete variable and a continuous variable? For example - I have water quality ratings for streams (excellent, good, fair, poor) and a corresponding nitrogen concentration for each rating. I want to know if the the ratings correlate with the concentration of nitrogen in the stream. Help?