similar to: NEW: extracat - a package for the visualization of categorical data

Dear R users, I have three questions measuring close relationships. The questions are same and the respondents put the answer in order. I'd like to examine the pattern of answers and visualize it. For example q1 (A,B,C,D,E) and q2 and q3 are the same. If the respondents selects A B C (so BCA or BAC or CBA or CAB), I'd like to construct frequency table for ABC and other combinations for

[Environment: Win XP, R 2.10.1] I'm trying to install the packages JGR and iWidgets required by the extracat package to make the interactive plots in the package work. I've tried various things, but nothing seems to work. Here is my most recent attempt, followed by my sessionInfo(). Does anyone have any suggestions how to make this work? > > library(extracat) Loading

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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",

It is unclear to me how aov() handles non-categorical variables. I mean it works and produces results that I would expect, but I was under impression that ANOVA is only defined for categorical variables. In addition, help(aov) says that it "call to 'lm' for each stratum", which I presume means that it calls to lm() for every group of the categorical variable, however I

Hello Tal, You asked *When is it helpful to use interactive plots? Either for data exploration (for ourselves) and data presentation (for a "client")?* My answer: It's helpful for checking data quality, for exploration with and without "clients", for checking results, and for data presenting. Notes: (1) It's difficult to explain interactive data visualization in

Hi, Suppose I have a categorical variable called STREET, and I have 30 levels for it (i.e. 30 different streets). I want to find all those streets with only 15 observations or below then collapse them into a level called OTHER. Is there a quick way, other than using a for() loop, to do it? Currently what I'm doing is something like: ### Collapse STREET (those < 15) st <- c()

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Dear Metafor users, I''d like to test a model with 2 continuous and 2 categorical moderators in a meta regression. One categorical parameter has 2 levels and the other has 4 levels. If I understand correctly, when I include all moderators in the model, Metafor returns main effects of the continuous parameters and contrasts of each level of categorical moderators with the intercept (which

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Hi! This is my first time posting. I've read the general rules and guidelines, but please bear with me if I make some fatal error in posting. Anyway, I have a continuous response and 29 predictors made up of continuous variables and nominal and ordinal categorical variables. I'd like to do lasso on these, but I get an error. The way I am using "lars" doesn't allow for the

It appears to be legitimate to include multi-level categorical and continuous variables in defining the model for earth (e.g. y ~ cat + cont1 + cont2) but is it also then possible use categoricals in the predict method using the earth result? I tried but it returns an error which is not very informative. Thanks Chris

Hi all, In a data-frame, I have two columns of data that are categorical. How do I form some sort of measure of correlation between these two columns? For numerical data, I just need to regress one to the other, or do some pairs plot. But for categorical data, how do I find and/or visualize correlation between the two columns of data? Thanks!

Hi, I have a simple question on interpreting output results from your msm package in R when using a categorical model with hcovariates i.e. covariates on the parameters of the emission model. The interpretation is straightforward for normal distributions, etc. but when using categorical distributions I get results like these: prob P(1)

Hello everyone, Can anyone suggest a decently documented (with good examples in the documentation) R package/function that performs segmentation (cluster, mixture modeling) of a population using both continuous and categorical input variables? Thank you, Dan [[alternative HTML version deleted]]

Dear all, I am using R version 2.15.0 and 'metafor' package version 1.6-0. Can this version of the package handle proportions from a categorical dependent variable for single studies?If so how do I set up my dataframe for the raw data from different studies? Also how do I give inputs, specially xi, mi (or ni) to the function escalc()? Thanks,Dushanthi [[alternative HTML

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Dear useRs, A new package 'drm', version 0.5-4, is available on CRAN. The drm package provides functions for marginal regression analysis of repeated (or otherwise clustered) binary, ordinal and nominal responses. This package can be considered as a likelihood-based alternative to GEE approach for marginal regression. In addition to regression modelling, several temporal and latent

Dear Dushanthi, Please keep your e-mails on the R-Help list, where Michael has already given you some excellent advice. As Michael already explained, metafor can handle proportions, but does not have any specific functionality for categorical variables with more than 2 levels (at the moment). So, if it is logical and possible to do so, you could collapse the levels of the categorical outcome to 2