similar to: Simulation confidence interval

I have the following code (which I did not write) that generates data based on a logistic model.? I'm only getting a single record with y=1.? It seems implausible that in 50k cases that have a single y=1.? Does that ring alarm bells for anyone else? ? beta<-c(-1.585600,-0.246900) betasize<-length(beta) meanpred<-c(0,35.900000) varpred<-c(0,1.000000) #loop code

I'm trying to get the following section of code to work, I think the problem is being caused by the assignment of data to the lm function not evaluating to "train" in the parent environment but I can't seem to figure out how to do this. fitmodel <- function(trial,data) { wrap.lm <- function(formula,data,...) { cat("in wrap lm",NROW(data),"\n");

Hi Being able to do object oriented programming in R is really good. I now started using the Reference Classes and really like it. Though, I have one problem: I cannot find a way to update a method on an existing object. The flexibility that scripting gives (really needed for interactive data analysis) is lost if everything have to be recalculated all the time. For example I would normally

I'm using an R program (which I did not write) to simulate multilevel data (subjects in locations) used in power calculations. It uses lmer to fit a mixed logistic model to the simulated data based on inputs of means, variances, slopes and proportions: ? (fitmodel <- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1)) where modelformula is set up in another part of the program.?

Dear R-help folks: Here is my platform: > version platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status major 1 minor 5.0 year 2002 month 04 day 29 language R I have a

What is a good way to enter a very long model formula. For example: y ~ Input.2 + Input.3 + ... + Input.1000 (assuming the corresponding dataframe has many other columns). Is there a way to convert a character string to a formula? Are there command line expansions in R besides the simple '.'? Thanks. [[alternative HTML version deleted]]

Dear addresses, I need perform a batch of 10 000 simulations for each of 4 options considered. (The idea is to obtain the parameter estimates in a heteroskedastic linear regression model - with additive or mixed heteroskedasticity - via the Kenward-Roger small-sample adjusted covariance matrix of disturbances). For this purpose I wrote an R program which would capture all possible options (true

Greetings- Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to develop a three-level (two levels of nesting) model with a dichotomous oucome variable. The unconditional model is thus: > doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 | groupid/participantid, + family = binomial, data = fgdata.10statements.df) iteration 1 iteration 2 iteration 3 iteration 4

I'm getting the following error in a script: "Error: could not find function "lmer."??? I'm wondering of my lme4 package is installed incorrectly.? Can someone tell me the installation procedure?? I looked at the support docs but couldn't translate that into anything that would work.

> > I have a function called FitModels(), which simply takes in the > names of a data.frame and two variable names within that data.frame, and > fits and returns a list of objects from 2 coxph() fits, one main effects and > one interaction model. Sometimes the two variables are such that there is a > warning message: convergence has not been reached, or the X matrix is >