similar to: Mixed effects multinomial regression and meta-analysis

Buenas tardes, Estoy interesado en generar observaciones de una distribucion binomial bivariada en la que hay _cierto_ grado de correlacion (denotemoslo rho). Podria por favor alguien indicarme como hacerlo en R? Este es el contexto. Supongamos que se tienen dos experimentos en los que la variable respuesta _sigue_ una distribucion binomial, i.e., X_i ~Binomial(n_i, p_i), i=1,2 y que, por ahora,

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

R-helpers: Assume that I want to create a series of sequentially named R objects. For example, I might want to call these objects V1, V2, V3 ... V50. To do this, I thought of some sort of looping function like: input.value <- seq(1:50) * 3 for(i in 1:50){ paste("V", i, sep="") <- input.value[i] } Of course this loop will not work since the paste function

Attention R users, especially those that are experienced enough to be opinionated, I need your input. Consider the following simple plot: x <- rnorm(100) y <- rnorm(100) plot(x, y, bty='n') A colleague (and dreaded SAS user) commented that she thought that my plots could be "cleaned up" by connecting the X and Y axes. I know that I can do that with bty='l' but I

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at

I am looking for a book that discusses the theory of multiple imputation (and other methods of dealing with missing data) and, just as importantly, how to implement these methods in R or S-Plus. Ideally, the book would have a structure similar to Faraway (Regression), Pinheiro&Bates (Mixed Effects) and Wood (GAMs) and would be very modern (i.e. published within the last couple of years).

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovered that I can reproduce certain items with lmer but not

Many medical journals and publishers require that images, whether photographs or line art, be submitted as high resolution .TIFF images. One option for R users is to produce an image in one format and to convert it to a .TIFF file using a second software program. My experience has been that this option often results in images of poorer quality, often with blurry contours, and a loss of

R plotting experts: I have a bivariate dataset composed of 300 (x,y) continuous datapoints. 297 of these points are located within the y range of [0,10], while 2 are located at 20 and one at 55. No coding errors, real outliers. When plotting these data with a scatterplot, I obviously have a problem. If I plot the full dataset with ylim = c(0,55), then I cannot see the structure in the data in

R-helpers: I am experiencing some odd behavior with the 'matrix' function that I have not experienced before and was wondering if there is something that I was missing in my code. --------------------------------- > sessionInfo() R version 2.6.1 (2007-11-26) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United

I am running R 2.3.1 on a Windows XP machine. I am trying to import some data into R that is stored as an S-Plus 7.0 .sdd file. When I run the following command, I get this error: > library(foreign) > d <- read.S(file='H:\\Research\\data.sdd') Error in read.S(file = "H:\\Research\\data.sdd") : not an S object The dataset is fairly large, roughly 13000 rows

I am running R 2.3.1 on a Windows XP machine. I am trying to import some data into R that is stored as an S-Plus 7.0 .sdd file. When I run the following command, I get this error: > library(foreign) > d <- read.S(file='H:\\Research\\data.sdd') Error in read.S(file = "H:\\Research\\data.sdd") : not an S object The dataset is fairly large, roughly 13000 rows

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Hello, I have interval censored data, censored between (0, 100). I used the tobit function in the AER package which in turn backs on survreg. Actually I'm struggling with the distribution. Data is asymmetrically distributed, so first choice would be a Weibull distribution. Unfortunately the Weibull doesn't allow for zero values in time data, as it requires x > 0. So I tried the

R-Helpers: I would like to perform sample size calculations for an experiment. As part of this process, I would like to know how various assumptions affect the sample size calculation. For instance, one thing that I would like to know is how the calculated sample size changes as I vary the difference that I would like to detect. I tried the following first, but got the associated error

R-Experts: Does anyone know if there are R functions to perform the Freeman-Tukey double arcsine transformation and then backtransform it? Thanks, Brant Inman Mayo Clinic

On July 12, 2004 Spencer Graves wrote an email describing essentially the same issue that I would like help on: calling the confint function from within another homemade function. Because he provided many good examples of the problem, I will not reproduce them here but will instead refer readers to the original posting: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg23826.html It is

R-helpers, Below is a simple example of some output that I am getting while trying to work with a data frame in R 2.12.1 for Mac. ----- > testdat <- data.frame(matrix(ncol=10, nrow=10)) > colnames(testdat) <- c('a','b','c','d','e','f','g','h','i','j') > testdat[seq(1,10,3),] <-

R-helpers, I have had difficulty installing the "pcvsuite" package on R version 2.12.0 (2010-10-15). The pcvsuite package is not available on CRAN, but is located for download at the following website at the University of Washington: Windows version http://labs.fhcrc.org/pepe/dabs/pcvsuite_1.0.zip Mac version

R-experts: I am trying to reproduce some of Paul Allison's results in his little green book on missing data (Sage 2002). The dataset for which I am having problems, "usnews", can be found at: http://www.ats.ucla.edu/stat/books/md/default.htm. I am working on a Windows machine with R 2.5 installed, all packages up-to-date. The problem has to do with the prelim.norm() function of the