similar to: correlation matrix for mixed contin/nominal/ordinal variables

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

This is a bit late, but: > 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? http://www.qimr.edu.au/davidD/R/polyr.R > (d) I appreciate this last item is not strictly an R question, but my > intention is to use these as input into the sem package for

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 merlin, Check out the hetcor package. Jim On Sat, Sep 2, 2017 at 6:25 AM, <merlinverdecia at infomed.sld.cu> wrote: > I would be very grateful if you would tell me how I can find the degree of > correlation between a nominal dependent variable and an independent ordinal > variable. The nominal variable has only two levels: YES and NO. > thank you very much in advance >

I have two series of events over time and I want to construct a graph of the relative frequency/density of these events that allows their distributions to be sensibly compared. The events are the milestones items in my project on milestones in the history of data visualization [1], and I want to compare trends in Europe vs. North America. I decided to use a graph of two overlaid density

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

Hi, are there readily available R packages that are able to perform FA on ordinal and/or nominal data? If not, what other approaches and helpful packages would you suggest? BR, Jay

I'm working with an historical image that may be (one of?) the first uses of gray-scale shading to show the pattern of values in a matrix/table, later used by Bertin in his 'reorderable matrix' and sometimes called a scalogram. The image is at http://euclid.psych.yorku.ca/SCS/Gallery/images/Private/scalogram.jpg The rows refer to the arrondisements of Paris, the cols to various

Dear all, I am trying to conduct an enhanced version of factor analysis with a SPSS interface that allows to use R. This approach has been suggested in the recent article: Basto, M. and J.M. Pereira An SPSS R-Menu for Ordinal Factor Analysis. Journal of Statistical Software 46, pp. 1-29. My variables are ordinal-type and the tool of Basto allows to run polychoric correlations in the SPSS

I would be very grateful if you would tell me how I can find the degree of correlation between a nominal dependent variable and an independent ordinal variable. The nominal variable has only two levels: YES and NO. thank you very much in advance regards, merlin ---------------------------------------------------------------- -- Este mensaje le ha llegado mediante el servicio de correo

The question is how to use a URL in place of a file= argument for read.*.functions that do not support this internally. e.g., utils::read.table() and her family all support a file= argument that can take a URL equally well as a local file. So, if I have a file on the web, I can equally well do > langren <- read.csv("langrens.csv", header=TRUE) > langren <-

In a recent talk ('Visions of the Past, Present & Future of Statistical Graphics'), I talked about, among other things, the lessons Minard's March on Moscow graphic had for modern statistical graphics, and illustrated aspects of power and simplicity in several programming languages where this graphic had been recreated. I referred to 'elegance factors' of various

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

I'm happy to announce the candisc package, v 0.5-9, now on CRAN. Generalized Canonical Discriminant Analysis Description This package includes functions for computing and visualizing generalized canonical discriminant analyses for a multivariate linear model. They are designed to provide low-rank visualizations of terms in a mlm via the plot method and the heplots package. The methods

System: [Windows XP Pro, R 2.8.0 and R 2.81.] When I do R graphics on Windows and want .eps output for LaTeX, with *tight bounding boxes* I usually do as follows from Rgui: - Save as Postscript ... myfig.ps - Open myfig.ps in Gsview, select PS to EPS, Save as ... myfig.eps This always worked before, and gave me .eps files that did not include much more white space than I like, didn't

How can I read a space-delimited file, where the data values for each case are folded before column 80, and so appear on two lines for each case? The first few cases look like this loc type bio H2S sal Eh7 pH buf P K Ca Mg Na Mn Zn Cu NH4 OI DVEG 676 -610 33 -290 5.00 2.34 20.238 1441.67 2150.00 5169.05 35184.5 14.2857 16.4524 5.02381 59.524 OI DVEG 516 -570 35 -268 4.75 2.66 15.591 1299.19

For an historical paper I'm working on, I have some Spanish plaintext, presently in the form of a Word .doc file, http://euclid.psych.yorku.ca/SCS/Gallery/images/Private/Langren/Verdadera-spanish-stripped.doc and also some ciphered text from the same original source. The ultimate goal is to use some frequency analysis of letters and word lengths in the plaintext to help decode the

I'd like to do some plots of historical event data on a reverse log scale, started, say at the year 2000 and going backwards in time, with tick marks spaced according to log(2000-year). For example, see: http://euclid.psych.yorku.ca/SCS/Gallery/images/log-timeline.gif As an example, I'd like to create a density plot of such data with the horizontal axis reverse-logged, a

For the following model, library(vcd) arth.polr <- polr(Improved ~ Sex + Treatment + Age, data=Arthritis) summary(arth.polr) where Improved is an ordered, 3-level response I'm looking for a *simple* way to test the validity of the proportional odds assumption, typically done via a score test for equal slopes/effects over the predictors. I do find a po.test= option in the repolr package

I'm trying to make a set of contour plots of bivariate kernel density estimates, showing three such plots overlaid, similar to this plot http://euclid.psych.yorku.ca/SCS/Private/Test/ridge-boot2.pdf except that I would like to have the contours *filled* (using transparent colors). To make this reproducible, I've saved the results of KernSmooth::bkde2D() in the following file: