similar to: declaring package dependencies

In checking my vcdExtra package, the following NOTE newly appeared (R-Forge, using R version 3.0.1 Patched (2013-08-20 r63635)) Package in Depends field not imported from: ?gnm? These packages needs to imported from for the case when this namespace is loaded but not attached. In the DESCRIPTION file, I have Depends: R (>= 2.10), vcd, gnm (>= 1.0.3) In NAMESPACE: # we are a vcd

I posted on this topic to r-help, but never got a sufficient answer, so I'm reposting here. [Env: R 2.11.1, Win Xp, using Eclipse/StatET] In a package I'm working on, I want to create as.matrix() and as.array() methods for a particular kind of object (log odds ratios). These are returned in a loddsratio object as the $coefficients component, a vector, but really reflect an underlying

I'm pleased to announce the release of the vcdExtra package, v. 0.5-0 from R-Forge to CRAN, on its way to a CRAN server near you. vcdExtra was originally designed to serve as a sandbox for introducing extensions of mosaic plots and other visualizations for categorical data, particularly those that apply to (poisson surrogate) loglinear models fitted using glm() and related, generalized

[Env: R 2.12.2, WinXp] In a vignette for the vcdExtra package, I had a text file, tv.dat under data/, that I used in the vignette as <<tv1,results=verbatim>>= tv.data<-read.table(system.file("data","tv.dat",package="vcdExtra")) head(tv.data,5) @ I was told that this now generates a warning for non-Rdata files in R CMD check. But I'm now

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

I don't know of any R package that can match all the functionality of LEM eg fitting equality constraints to model parameters a la LISREL. WRT dumping tables, I would have thought that as.data.frame.table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") }

Dear all, I have a problem to make figures with two columns in package vcd. Here an example code I take from "\library\vcd\html\plot.loglm.html" What I need, I want to make two figures in one plot. How could I do that. I have tried with layout(rbind(c(1, 1, 2, 2))) but the same result, two plot. Best wishes, Muhammad Subianto library(vcd) oldpar <- par(mfrow=c(1, 2)) ## mosaic

aperm() was designed for multidimensional arrays, but is also useful for table objects, particularly with the lattice, vcd and vcdExtra packages. But aperm() was designed and implemented before other related object classes were conceived, and I propose a small tune-up to make it more generally useful. The problem is that aperm() always returns an object of class 'array', which

I can use data() to find the available datasets in a package, but I'd like to extract and display some additional information for each dataset than what is provided by data(), e.g., class() and dim() for datasets for which these are available. I'm stuck on using using lapply properly with objects, rather than the names of objects that I get from data() Example: > DS <-

Hi all: For stratified count data,how to perform regression analysis? My data: age case oc count 1 1 1 21 1 1 2 26 1 2 1 17 1 2 2 59 2 1 1 18 2 1 2 88 2 2 1 7 2 2 2 95 age: 1:<40y 2:>40y case: 1:patient 2:health oc: 1:use drug 2:not use drug My purpose: Anaysis whether case and

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",

Hello: What is the most efficient way to change the plotted variable names in mosaic plots in the vcd package? Should one do a separate contingency table first, change the dimension names there and then pass that to mosaic? Or is there a way to do it simply within mosaic. I was thinking something like: mosaic(~var1+var2, labelling_args=list(varnames=c('newvar1', 'newvar2')) Simon

I have a print method for a set of statistical tests, vcdExtra::CMHtest, for which I'd like to have more sensible printing of pvalues, as in print.anova(). [Testing this requires the latest version of vcdExtra, from R-Forge **|install.packages("vcdExtra", repos="http://R-Forge.R-project.org")|** ] With my current print method, I get results like this, but all Prob values

helpeRs, I have a nice looking mosaic plot in an article to be published soon. Sadly, the published version will be in black and white and so ruin the advantage of the default shading scheme of tiles. What would readers suggest as an alternative shading scheme? If I have a black-and-white shading scheme graduated according to suitable cutoffs I won't be able to tell positive from

Hi all, anyone has an idea how I could fix this? \donttest{ ## Set colors from colorspace package with a fallback col <- try(colorspace::rainbow_hcl(5), silent = TRUE) %||% rainbow(5) } The problem is that this makes R CMD check freak out (http://www.r-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/falsy-00check.html) if the colorspace package is not declared as a dependency.

In the vcdExtra package on R-Forge, I have functions and generic methods for calculating log odds ratios for R x C x strata tables. I'd like to define methods for fitting weighted lm()s to the resulting loddsratio objects, but I'm having problems figuring out how to do this generally. # install.packages("vcdExtra", repos="http://R-Forge.R-Project.org")

Dear Rgurus, To my knowledge the best way to visualize the distribution of a discrete variable X is plot(table(X)) The problem which I have is the following. I have to discrete variables X and Y which distribution I would like to compare. To overlay the distribution of Y with lines(table(Y)) gives not satisfying results. This is the same in case of using density or histogram. Hence, I am

Feliz año a todos! Y al que sepa y tenga tiempo, una duda: Quiero hacer un análisis de distribución de léxico (supongo que da igual eso) mediante mosaicplot(), en particular me interesa que me muestre el peso de la distribución según una proporción que ya conseguí sacar; la de el uso de x termino en un espacio determinado. Ejemplo: Distribución de los términos, totales (tabla1) sitio1

if i want to plot the chi-square distribution with a different degree of freedom how can i plot it in the graph?Sometimes i plot the histogram and cut it in a lot of piece.It's distribution like a chi-square.So i want to plot the chi-square with a different degree of freedom to compare it . -- View this message in context:

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]