similar to: contingency table analysis; generalized linear model

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Dear R-help, Has anyone implemented sparklines in the strips of a lattice plot? What I have in mind is, say, highlighting that part of a time series that one is examining in more detail in a set of lattice plots. Regads,. Mark Difford. PS: (Andreas Loffler has implemented a simple but functional version for TeX/LaTeX: http://www.tug.org/tex-archive/help/Catalogue/entries/sparklines.html)

Dear R-core/development-team, The problem noted in the subject-line has been a problem in the last three development versions of R for Windows that I have downloaded and tested, the most recent of them being a version I downloaded this morning. Update.packages() using the graphical option, i.e. called as update.packages(ask='graphics', checkBuilt=TRUE) does not work as it should, but

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Dear R-help, I posted this on 4 Oct but got no response (I wasn't even told to go away and do some more background reading ;) ). I am reposting it in the, perhaps, vain hope that someone with knowledge of the subject will reply, if only to point me in a different direction to which I am now facing. Earlier Posting:--- I am sorry if this is more of a stats question than an R-question, but I

An amusing afterthought : What is a rival software (ahem!) was planting this, hoping for a divide between S and R communities.or at the very minimum hoping for some amusement. an assumption or even a pretense of stealing credit is one of the easiest ways of sparking intellectual discord Most users of softwares don't really care about who gets credit ( Who wrote Windows Vista ,or Mac OS or

Dear R-users, I am a newbie to this site and a relative new-comer to S/R, so please tread lightly, for you tread... There have been several posting relating to problems with augPred() from the nlme library. Here is a "fix" for one of these problems which may lie at the root of others. In my case the problem with augPred() lay in gsummary(), which augPred() uses, causing it to fail.

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

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

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

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

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 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

John Fox helped show me how to collapse the categories, pooling frequencies, in an n-way table, using levels()<- on an equivalent data frame: # create a table sex <- c("Male", "Female") age <- letters[1:6] education <- c("low", 'med', 'high') data <- expand.grid(sex=sex, age=age, education=education) counts

Dear R-help community, I am working with multidimensional contingency tables and I am having trouble getting loglm to run on all dimensions without typing out each dimension. I have generated random data and provided output for the results I want below: d1.c1 <- rnorm(20, .10, .02) d1.c2 <- rnorm(20, .10, .02) d2.c1 <- rnorm(20, .09, .02) d2.c2 <- rnorm(20, .09, .02) d3.c1 <-

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<-

Dear R-help, I am sorry if this is more of a stats question than an R-question, but I have found it difficult to get a clear answer by other means. Q. Would it be "wrong" to specify a nested model and retain a common intercept, e.g. lm(NH4 ~ Site/TideCode + 1) I am aware (?) that my Site-coefficients are now calculated relative to my reference Site (treatment.contrasts), *but* that

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

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

Forgive my resending this post. To data I have received only one response (thank you Bert Gunter), and I still do not have an answer to my question. Respectfully, John Windows XP R 2.12.1 contrast package. I am trying to understand how to create contrasts for a model that contatains an interaction. I can get contrasts to work for a model without interaction, but not after adding the