similar to: importing a list

Dear all, I'm trying to install in R the package blme that has been removed from R cran but it is still in the Archive: http://cran.r-project.org/src/contrib/Archive/blme/ I have a Mac OSX and I have been trying with the following command from the Shell: R CMD INSTALL --build http://cran.r-project.org/src/contrib/Archive/blme/blme_0.01-4.tar.gz getting the following answer: invalid

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 While I'm not an R expert, I have used R on Windows XP. Now I've moved to Ubuntu (Intrepid), and I'm trying to configure R to work with the Gelman and Hill _Data Analysis Using Regression and Multilevel/Hierarchical Models_. So far, it's not working. I start by following the instructions for installing arm and BRugs at

Hi all, I have a dataset of individuals where the variable ID corresponds to the identification of the household where the individual lives. rel.head stands for the relationship with the household head. so rel.head=1 is the household head, rel.head=2 is the spouse, rel.head=3 is the children. Here is an example to see how it looks like: df<-data.frame(ID=c("17100",

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 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

Note that the problem arises only by scanning subdirectories with a large number of files ( 3.000 files and more ). -----Messaggio originale----- Da: Usai, Maria Grazia Inviato: gioved? 25 luglio 2002 11.11 A: samba@lists.samba.org Oggetto: [Samba] High CPU utilization with samba2.2.5 on HPUX 11.00 Hello, I need some help for a strange problem. I have an HP L2000 server ( 2 CPU and 2 GB RAM

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"),

I decided to update my packages and then had a problem with loading the Matrix package http://cran.at.r-project.org/bin/windows/contrib/2.4/Matrix_0.9975-9.zip This is what happened when I tried to load it in: > library("Matrix") Error in importIntoEnv(impenv, impnames, ns, impvars) : object 'Logic' is not exported by 'namespace:methods' Error:

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'm working with Andrew Gelman on a book project and we're having some difficulties getting coef() to work in some lmer() calls. Some versions of the model work and some do not. For example, this works (in that we can run the model and do coef() from the output): R2 <- lmer(y2 ~ factor(z.inc) + z.st.inc.full + z.st.rel.full + (1 + factor( z.inc) | st.num),

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

Dear All, I am having a bit of problem using the DBI and RMySQL package. I have already installed DBI but now I want to connect to MySQL. So how do I do it. The CRAN package repository does not have RMySQL windows binary to install from? Regards, Indrajit Sen Gupta Business Analyst Mu Sigma Business Solutions Pvt Ltd #21/1-1, Nawab Towers Cunningham Road Bangalore 560 052, India Tel: +91 80

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

Is there a way to stream data into R so that it can be used in a command pipe? Most Unix commands read data via standard input and read the script from the command line. For example: $ seq 1 10 | sed -e 's/^/foo /' $ seq 1 10 | awk '{print "foo " $1}' $ seq 1 10 | perl -lane 'print "foo $F[0]"' In contrast, R seems to read the script

Is there a way to do text plots in R? I'd like to do some simple XY plots in R with the output in text (ascii). For example, with gnuplot I can do the following: echo 'set terminal dumb ; plot sin(x)' | gnuplot To generate a simple sin wave. Since I connect to a remote Linux machine using SSH, being able to generate a rough idea of what a plot will look like in text would be

Is there a cheat-sheet anywhere that describes how to do SQL-like manipulations on a data frame? My knowledge of R is rather limited. But from my experience it seems as though one can think of data frames as being similar to tables in a database: there are rows, columns, and values. Also, one can perform similar manipulations on a data frame as one can on a table. For example: