similar to: p values for polychor

Hi, Is there any R library/package that calculates tetrachoric correlations from given marginals and Pearson correlations among ordinal variables? Inputs to polychor function in polycor package are either contingency tables or ordinal data themselves. I am looking for something that takes marginal distributions and Pearson correlation as inputs. For example, Y1=(1,2,3) with P(Y1=1)=0.3,

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 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 =

Hi, I'm running this for a simulation study, so many combinations of parameter produce many predictions that I need to correlate with data. The problem ---------------- I'm using rating data with 3 to 5 categories (e.g., too low, correct, too high). The underlying continuous scales should be normal, so I chose the polychoric correlation. I'm using library(polychor) in its

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must

Dear Professor Fox, Apologies for my oversight relating to the polychor command and thank you for your advice. I turned to the polychor command when trying to find an equivalent for the polychoric command found in the psych package (I am following a procedure outlined in Gadermann, Guhn & Zumbo, 2012 that uses this command rather than the polycor package) . The polychoric command is returning

I hope someone here knows the answer to this since it will save me from delving deep into documentation. Based on 22 pairs of vectors, I have noticed that tetrachoric correlation coefficients in stata are almost uniformly higher than those in R, sometimes dramatically so (TCC=.61 in stata, .51 in R; .51 in stata, .39 in R). Stata's estimate is higher than R's in 20 out of 22

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

About two years ago there was a thread about this which suggested that at that time nobody had these coefficients ready to go. (a) has anyone in the meanwhile programmed them? (b) I think I can see how to do the tetrachoric one with mvtnorm on similar lines to an example on the help page so will try that if nobody else already has (c) looking at the polychoric one makes me realise yet again

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?

Hello. Using the polychor function > polychor(data[c(s1,s2)] ) for polychoric correlations of two ordinal variables in R takes a long time for N=7000 (20 minutes+) and significantly slows down my computer. Now, I have a pretty old computer, but it takes about 20 seconds for MPLUS to print out the complete polychoric correlation matrix for all 16 variables, while I am running the R function

Good Morning, Is it possible to use the R program for a CFA with dichotomous data? Thank you, Kathleen Kathleen Kemp, M.A. Doctoral Clinical Psychology Student, Concentration: Forensic Psychology Drexel University Philadelphia, PA 19104 kk354@drexel.edu [[alternative HTML version deleted]]

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 :

Hello, I'd like to know if there is a way to fit a Structural equation model with dichotomous indicators (ex: problem with a phone solved/ or not) having effects on a ordinal variable. How I do that using R? Do you have an example with the code in R that you can send to me? Thanks a lot! Renata Estrella UFRJ, Brasil, Rio de Janeiro Renata Leite Estrella Assistente de

Hi all, I have two basic questions, hope you should help me: 1. How do I save a log file in R with the results? For example, in Stata it can be done by using "log using c:\...\test.txt" 2. How do I display the execution time of one function like the one below: CalculaCorrelacao <- function(construto, n) { library(polycor) for (i in 2:n){ for (j in i:n+1){ x1

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",

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.

I have data set with binary responses. I would like to conduct polychoric principal component analysis (pPCA). I know there are several packages used in PCA but I could not find one that directly estimate pPCA and graph the individuals and variables maps. I will appreciate any help that expand these reproducible scripts. #How to conduct polychoric principal component analysis pPCA using #either

Hi. Does anyone know whether the following error is a result of a bug or a feature? I can eliminate the error by making ML=F, but I would like to see the values of the cut-points and their variance. tmp.vec<-c(0, 0, 0 , 0 ,0 , 1, 0, 2, 0 , 0, 5 ,5 ,3 ,1, 0 , 1, 5, 10, 27, 20, 9, 0, 1, 1, 12, 29, 57, 34, 0, 0, 1, 2, 11, 31, 32) tmp.mat<-matrix(tmp.vec, nrow=7)

Hi there, I'm generally more a Stata user than a R user, but I need to computed something, and I am not able to do it with Stata 13. So, here I am! I have a database that has multiple imputations (imputations are already done) with a complex sample design (Strate and Weight). Is it possible, in R, to run polychoric correlation with multiple imputation, a Strate and a Weight ? For the