similar to: evaluation of expressions

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

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 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

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),

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

-----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

Dear R users, I've found bugs.R : the functions for running WinBUGs from R that is writen by Dr. Andrew Gelman who is a professor from Columbia University. The bugs.R would be very useful for me, and I think many of you know it as well. I followed the instuctions on Dr. Gelman's web to install all of documents that bugs.R needs, but when I try to run the school example the web posted in

Hi, all. I'm trying to automate some regression operations in R leaving the possibility to modify the predictors in the regression. For example, I have saved in a list the results and then exported as a txt file, in which we can modify the predictors, putting for example lm(y~x^2) instead of having lm(y~x) as in the original model. Now, I need to import in R the txt file as a list to

I follow Alan Lenarcic's very helpful tutorial on building R package for Windows (XP), which could be found in www.stat.columbia.edu/~gelman/stuff_for_blog/AlanRPackageTutorial.pdf<http://www.stat.columbia.edu/~gelman/stuff_for_blog/AlanRPackageTutorial.pdf>. The package involves a small dll compiled from some very simple C++ codes. The build process seemed to work smoothly, until i

Dear R-users, I am trying to use the following code to reproduce results from Prof. Gelman's book, but have the listed error for R2WinBUGS version (the openbugs version is good). I am using R-2.6.1 on windows XP, and all the R packages are most current ones. schools.bug can be found at http://www.stat.columbia.edu/~gelman/bugsR/runningbugs.html . Can anyone help me to figure out what's

Greetings: I'm going to encourage some students to try Bayesian ideas for hierarchical models. I want to run the WinBUGS and R examples in Tony Lancaster's An Introduction to Modern Bayesian Econometrics. That features MS Windows and "bugs" from R2WinBUGS. Today, I want to ask how people are doing this in Linux? I have found a plethora of possibilities, some of which are not

Hi: Here's my revised attempt at explaining my problem. Hopefully it won't get sent out weirdly like it did earlier. ? The link to what I'm trying to follow is http://www.stat.columbia.edu/~gelman/stuff_for_blog/AlanRPackageTutorial.pdf ? I've been going through the tutorial but when I got to page 9 and did R CMD SHLIB Xdemo.cc Xdemo_main.cc -o Xdemo.dll I got

Hi, A question. When I run gelman.diag and gelman.plot with mcmc lists obtained from MCMCregress, the results are following. > post.R <- MCMCregress(Size~Age+Status, data = data, burnin = 5000, mcmc = 100000, + thin = 10, verbose = FALSE, beta.start = NA, sigma2.start = NA, + b0 = 0, B0 = 0, nu = 0.001, delta = 0.001) > post1.R <- MCMCregress(Size~Age+Status, data