similar to: pooling categories in a contingency table

I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider

I have a large n-way contingency table, constructed as a table object, and want to pool (collapse) some categories, summing the frequencies in all collapsed cells. How can I do this? thx, -Michael -- Michael Friendly friendly at yorku.ca York University http://www.math.yorku.ca/SCS/friendly.html Psychology Department 4700 Keele Street Tel: (416) 736-5115

I'm trying to form contingincy tables among a set of character variables which were read from a .csv file and have missing represented as "". I want to exclude the missing levels from the table. > levels(CPIC) [1] "" "N" "Y" > levels(Manix) [1] "" "N" "Y" > xtabs(~CPIC + Manix, exclude=c("",NA))

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Hi, I am constructing a contingency table using xtabs. The function works great: mo yr Sep Oct Nov Dec 1950 -7.164486e-02 3.152674e-02 -1.283389e-02 1.570382e-01 1951 3.054293e-02 4.665234e-02 -2.445499e-04 8.720204e-02 1952 3.937034e-02 -4.790636e-02 5.022616e-02 1.180279e-01 but I wonder if there is an argument I can pass to xtabs

I have a sparse contingency table (most cells are 0): > xtabs(~.,data[,idx:(idx+4)]) , , x3 = 1, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 31 2 0 0 112 3 0 0 94 , , x3 = 2, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 3, x4 = 1, x5 = 1 x2 x1 1 2 3 1 0 0 0 2 0 0 0 3 0 0 0 , , x3 = 1, x4

In an Rweave tutorial written for possibly naive R users, I felt it necessary to explain why table() had to be used inside with(), whereas other tools like xtabs() had a data= argument. with() is quite nice for such cases, but it seems an unnecessary thing to learn right off. Before I turn this question into a request for R-devel, is there any inherent reason why it might be hard to add a

How does one ideally handle and display multidimenstional contingency tables in R v. 2.6.2? E.g.: > prob1<- data.frame(victim=c(rep('white',4),rep('black',4)), + perp=c(rep('white',2),rep('black',2),rep('white',2),rep('black',2)), + death=rep(c('yes','no'),4), count=c(19,132,11,52,0,9,6,97)) > prob1 victim perp

I am generating an ftable (by running ftable on the results of a xtabs command) and I am getting the following. ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Var1? Var2 date ? ? ? ? ? ? ? ? group? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 2007-01-01? ? ? ? ? q1 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?1? ? 9 ? ? ? ? ? ? ? ? ? ? ?q2 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Hi, I have large data frame with many columns. A short example is given below: > dataH host ms01 ms31 ms33 ms34 1 cattle 4 20 9 6 2 sheep 4 3 4 5 3 cattle 4 3 4 5 4 cattle 4 3 4 5 5 sheep 4 3 5 5 6 goat 4 3 4 5 7 sheep 4 3 5 5 8 goat 4 3 4 5 9 goat 4 3 4 5 10 cattle

Dear expeRts, The order in which the variables vary in expand.grid() is often unintuitive. I would like to suggest a 'method' slot for expand.grid() which requires only very little changes (100% backward compatible) and which allows one to control this order. Please find attached diffs against R-devel. Cheers, Marius ### ./src/library/base/R/expand.grid.R

Dear R-list, Hi everyone, Im trying to make an analysis of multidimensional contingency tables using R. I' working with the Agresti example where you have the data from 3 categories. The thing is how can I do the analisys using the G2 statistics. Somebody can send me an Idea? I attach the program where you can find the data. Best Regards, > prob1<-

Hi all, I am trying to analyze the following data. The first three columns are categorical variables (colors of three traits for a peripatus species) and the last column the count of individuals in each three-way classification. I wish to test if the three traits vary independently or if they are correlated across individuals, i.e. - this is a basic three way contingency table question.

For a variety of displays (mosaicplots, barplots, ...) one often wants to either abbreviate or wrap long labels, particularly when these are made up of several words. In general, it would be nice to have a function, abbreviate.or.wrap <- function(x, maxlength=10, maxlines=2, split=" ") { } that would take a character vector or a list of vectors, x, and try to abbreviate or wrap

I have been tinkering with mosaicplot() and friends as a way of learning R. As part of this, I've written a pair.table() method for mosaic matrices, and would like to extend mosaicplot to work with loglin and logln (MASS) objects. I'm using R 1.4.0 on Win 98. I've been trying to figure out the formula interface, and think there's a bug, but not sure how to find it, yet alone fix

Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified

Dear Friends. I am just starting to use R. And in this occasion I want to construct a high-dimensional contingency table, because I want to crate a mosaic plot with the vcd package. My table is in this format: año ac.rep cat.gru conteos 1 2005 R parejas 253 2 2005 N parejas 23 3 2006 R parejas 347 4 2006 N parejas 39 5 2007 R

switch(i, ...) extracts 'i'-th argument in '...'. It is like eval(as.name(paste0("..", i))) . Just mentioning other things: - For 'n', n <- nargs() can be used. - sys.call() can be used in place of match.call() . --------------------------- >>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Mon, 15 May 2017 16:28:42

In http://tolstoy.newcastle.edu.au/R/e2/help/06/10/3064.html a method was given for converting a frequency table to an expanded data frame representing each observation as a set of factors. A slightly modified version was later included in the NCStats package, only on http://rforge.net/ (and it has too many dependencies to be useful). I've tried to make it more general, allowing an input

I want to generate multiple multivariate normal samples with different mean vectors and common covariance matrix. I can do this with a loop, but can't quite figure out how to do it with apply and friends. In the example below, I want values to have 3 columns: group, x, y # number of groups, and group means x <- jitter(seq(2,10,by=2)) y <- x + rnorm(length(x), 0, .5) means <-