Re: [R] exploratory analysis of large categorical datasets
you can also look at correspondence analysis, which is implemented in multiple CRAN packages, for instance MASS, ade4 and others. See the multivariate analysis task view on CRAN. Kjetil On Thu, Nov 11, 2010 at 10:39 PM, Dennis Murphy djmu...@gmail.com wrote: Hi: A good place to start would be package vcd and its suite of demos and vignettes, as well as the vcdExtra package, which adds a few more goodies and a very nice introductory vignette by Michael Friendly. You can't fault the package for a lack of documentation :) You might also find the following link useful: http://www.datavis.ca/R/ Scroll down to 'vcd and vcdExtra', and further down to 'tableplot', which was recently released on CRAN. HTH, Dennis On Thu, Nov 11, 2010 at 2:09 PM, Lara Poplarski larapoplar...@gmail.comwrote: Dear List, I am looking to perform exploratory analyses of two (relatively) large datasets of categorical data. The first one is a binary 80x100 matrix, in the form: matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) and the second one is a multistate 750x1500 matrix, with up to 15 *unordered* states per variable, in the form: matrix(sample(c(1:15),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) Specifically, I am looking to see which pairs of variables are correlated. For continuos data, I would use cor() and cov() to generate the correlation matrix and the variance-covariance matrix, which I would then visualize with symnum() or image(). However, it is not clear to me whether this approach is suitable for categorical data of this kind. Since I am new to R, I would greatly appreciate any input on how to approach this task and on efficient visualization of the results. Many thanks in advance, Lara [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] exploratory analysis of large categorical datasets
Dear List, I am looking to perform exploratory analyses of two (relatively) large datasets of categorical data. The first one is a binary 80x100 matrix, in the form: matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) and the second one is a multistate 750x1500 matrix, with up to 15 *unordered* states per variable, in the form: matrix(sample(c(1:15),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) Specifically, I am looking to see which pairs of variables are correlated. For continuos data, I would use cor() and cov() to generate the correlation matrix and the variance-covariance matrix, which I would then visualize with symnum() or image(). However, it is not clear to me whether this approach is suitable for categorical data of this kind. Since I am new to R, I would greatly appreciate any input on how to approach this task and on efficient visualization of the results. Many thanks in advance, Lara [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] exploratory analysis of large categorical datasets
Hi: A good place to start would be package vcd and its suite of demos and vignettes, as well as the vcdExtra package, which adds a few more goodies and a very nice introductory vignette by Michael Friendly. You can't fault the package for a lack of documentation :) You might also find the following link useful: http://www.datavis.ca/R/ Scroll down to 'vcd and vcdExtra', and further down to 'tableplot', which was recently released on CRAN. HTH, Dennis On Thu, Nov 11, 2010 at 2:09 PM, Lara Poplarski larapoplar...@gmail.comwrote: Dear List, I am looking to perform exploratory analyses of two (relatively) large datasets of categorical data. The first one is a binary 80x100 matrix, in the form: matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) and the second one is a multistate 750x1500 matrix, with up to 15 *unordered* states per variable, in the form: matrix(sample(c(1:15),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( group1, group2,group3, group4,group5), c(V.1, V.2, V.3, V.4, V.5))) Specifically, I am looking to see which pairs of variables are correlated. For continuos data, I would use cor() and cov() to generate the correlation matrix and the variance-covariance matrix, which I would then visualize with symnum() or image(). However, it is not clear to me whether this approach is suitable for categorical data of this kind. Since I am new to R, I would greatly appreciate any input on how to approach this task and on efficient visualization of the results. Many thanks in advance, Lara [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
