similar to: problem with Matrix package

Here's another issue (that might well be operator error): > install.packages("lattice") ... ... ** save image Loading required package: grid Error in importIntoEnv(impenv, impnames, ns, impvars) : object(s) 'dev.list', 'cm.colors', 'gray', 'heat.colors' are not exported by 'namespace:graphics' Execution halted ERROR: execution of

I've used packages for some years now and seldom had any trouble using the tgz files. Now I've come across something I've never seen before. > version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 2 minor 1.0 year 2005 month 04 day 18 language R > library(lme4) Loading required package: methods

Hi all, I recently (today) updated the Matrix package and installed the latticeExtra package, but then when I tried to load the lme4 package, I got the following error message:- > library(lme4) Loading required package: Matrix Loading required package: latticeExtra Error in importIntoEnv(impenv, impnames, ns, impvars) : object(s) '.__C__lmeRep' are not exported by

Dear list, It seems that a package (pkgB) using another package (pkgA) with S4 generics formed by taking existing functions (for example 'plot') must not import the existing functions ('plot') in its namespace to avoid the warning "replacing previous import: plot". Suppose we use the simple 'import' directive in the name space of pkgB. pkgA and pkgB files would

Dear R users, Im trying to load *"xlsx"* package which depends on *"xlsxjars"* and * "rJava"* packages. All the 3 packages (zipped files) are installed successfully in windows. I have added "C:\Program Files\Java\jre1.6.0_03\bin\client" to the PATH variable to get rJava working. However, the following error message results after inputting

I don't understand this behavior. Why does the every data point get trashed by data.matrix when there is one non-numeric element in the array? Thanks. > temp GDP CPIYOY 19540 2098.1 garbage 19632 2085.4 0.9 19724 2052.5 0.8 19814 2042.4 1.1 > data.matrix(temp) GDP CPIYOY 19540 4 4 19632 3 2 19724 2 1 19814 1

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### <<insert bug report here>> There are two bugs associated with graphics devices.

Hi all, I have a problem with a package that imports two other packages which both export a method for the `[` primitive function. I set up a reproducible example here: https://github.com/kforner/namespaceImportFrom_problem.git Basically, the testPrimitiveImport package imports testPrimitiveExport1 and testPrimitiveExport2, which both export a S4 class and a `[` method for the class. Then: R

Hi, all. I'm trying to automate some regression operations in R but am confused about how to evaluate expressoins that are expressed as character strings. For example: y <- ifelse (rnorm(10)>0, 1, 0) sex <- rnorm(10) age <- rnorm(10) test <- as.data.frame (cbind (y, sex, age)) # this works fine: glm (y ~ sex + I(age^2), data=test, family=binomial(link="logit"),

Is there a quick way to fit student-t regressions (that is, a regression with t-distributed error, ideally with the degrees-of-freedom parameter estimated from the data)? I can do it easily enough in Bugs, or I can program the log-likelihood in R and optimize using optim(), but an R version (if it's already been written by somebody) would be convenient, especially for teaching purposes.

I want to write a function to standardize regression predictors, which will require me to do some character-string manipulation to parse the variables in a call to lm() or glm(). For example, consider the call lm (y ~ female + I(age^2) + female:black + (age + education)*female). I want to be able to parse this to pick out the input variables ("female", "age",

Thanks to some help by Doug Bates (and the updated version of the Matrix package), I've refined my question about fitting nested and non-nested factors in lmer(). I can get it to work in linear regression but it crashes in logistic regression. Here's my example: # set up the predictors n.age <- 4 n.edu <- 4 n.rep <- 100 n.state <- 50 n <- n.age*n.edu*n.rep age.id

Trying to compile the package Matrix_0.9975-11.tar.gz with newest R-2.5.0 alpha (2007-03-31 r40986) on FreeBSD 7.0-CURRENT (i386) I get the following error: ----- R CMD INSTALL Matrix_0.9975-11.tar.gz * Installing to library '/usr/local/lib/R/library' * Installing *source* package 'Matrix' ... ** libs ** arch - "Makefile", line 10: Missing dependency operator

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

I'm having another problem with lmer(), this time something simpler (I think) involving the coef() function for a model with varying coefficients. Here's the R code. It's a simple model with 2 observations per group and 10 groups: # set up the predictors n.groups <- 10 n.reps <- 2 n <- n.groups*n.reps group.id <- rep (1:n.groups, each=n.reps) # simulate the varying

I have written a function called mcsamp() that is a wrapper that runs mcmcsamp() and automatically monitors convergence and structures the inferences into vectors and arrays as appropriate. But I have run into a very little problem, which is that mcmcsamp() shortens the variable names. For example: > set.seed (1) > group <- rep (1:5,10) > a <- rnorm (5,-3,3) > y <-

I've been trying to keep track with lmer, and now I have a couple of questions with the latest version of Matrix (0.995-4). I fit 2 very similar models, and the results are severely rounded in one case and rounded not at all in the other. > y <- 1:10 > group <- rep (c(1,2), c(5,5)) > M1 <- lmer (y ~ 1 + (1 | group)) > coef(M1) $group (Intercept) 1 3.1 2

I'm trying to figure out how to use lmer to fit models with factors that have some nesting and some non-nested groupings. For example, in this paper: http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf we have a logistic regression of survey respondents' political preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, state (51 states within 5

Hi, all. In setting up my package for post-processing regression models, I am trying to use standard notation as much as possible: thus, I use coef() to access estimated coefficients. I wrote a function called se.coef() to grab standard errors, and se.fixef() and se.ranef() to grab se's from coefficients estimated from lmer(). I also need a function to access sigma-hat (the residual sd

Hi all. Are there any R functions around that do quick logistic regression with a Gaussian prior distribution on the coefficients? I just want posterior mode, not MCMC. (I'm using it as a step within an iterative imputation algorithm.) This isn't hard to do: each step of a glm iteration simply linearizes the derivative of the log-likelihood, and, at this point, essentially no