search for: beperron

Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

Hello folks, I am trying to estimate the two-step Heckman regression model. I would like to make an adjustment for intragroup correlations. Stata can implement this with the "cluster" option, but I am really hoping to stick with R. It seems that the micEcon package is the primary source for this two-step regression model (i.e., heckit), but I can't find a way to make the

Hello all, I just started working with the lme4 package to estimate a multilevel logistic regression and am planning to use this package for a cross-classification / multiple-membership model. I haven't found many worked examples and am trying to figure out how to add variables to the higher-level part of the model. Consider the following example: test.1 <- lmer(y ~ b1 + b2 + b3 +

Hello R folks, Does anybody have code to share for generating (via simulation) clustered data? The type of data I am looking to simulate would allow fitting of a multilevel model with random intercepts. I looked at the mvtnorm package but am not quite sure how to create clusters. (Can this be done by simply changing the seed?) If somebody could point me where to look for the relevant

Hello all, I am fairly new to R and am trying to bring together data from multiple sources. Here is one problem that I cannot seem to crack – I hope somebody can help. Let me simplify the problem: Let’s say I have two datasets: DATA1 and DATA2. I would like to work with all the cases in DATA2. I have additional variables on these cases in DATA1, which is a larger data set with many

Hello folks, I am a relatively new user of R and created multiply imputed data sets with the 'mice' library. This library provides two functions for complete-data analysis on multiply imputed data set objects (lm.mids and glm.mids). I am trying to estimate a series of Cox PH regression models and cannot figure out the best way to do this. Is it possible with the mitools library?

Hello R users, I am relatively new to R and cannot seem to crack a coding problem. I am working with substance abuse data, and I have a variable called "primary.drug" which is considered the drug of choice for each subject. I have just a few missing values on that variable. Instead of using a multiple imputation method like chained equations, I would prefer to derive these