search for: mlmsoftrev

Does anyone know of an existing R package or code to run a mixed Hurdle model? I found glmmADMB, but that seems to be ZIP. Any recommendations? Thanks, Jeff [[alternative HTML version deleted]]

Hi R users! I am trying to learn some multilevel analysis, but unfortunately i am now very confused. The reason: http://www.ats.ucla.edu/stat/hlm/seminars/hlm_mlm/mlm_hlm_seminar.htm http://www.ats.ucla.edu/stat/sas/seminars/sas_mlm/mlm_sas_seminar.htm and MlmSoftRev. pdf from mlmRev package. >From what i see, the first two links seem to declare the level one variable as a random part (i don't know sas synthax, but i think i am right ) while Mr. Bates' pdf says that a grouping variable is the random part of the model, though both models, use rough...

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Hi, I am using lmer (from the package lme4) to predict a binary response variable (REL) from a bunch of fixed effects and two random effects (Speaker_ID and NPhead_lemma): fit <- lmer(REL ~ SPEAKER_GENDER + log(SPEECHRATE) + SQSPEECHRATE + ..... + (1|Speaker_ID) + (1|NPhead_lemma), family="binomial", data=data.lmer, method="Laplace", model=T, x=T) I

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not entirely sure that I have the right solution). Basically I fed chicks in nest boxes over a period of time and weighed them each time I fed them. I presume that "chick id" should be a random factor and s...

Hello, Is this a bug in the lmer routine? > library(lme4) > ### test case based on rats data from Crawley > a<-rnorm(36);b<-rep(1:3,each=12);c<-rep(1:2,each=6,3);d<-rep (1:3,each=2,6) > > ### mixed model works when c & d are numeric, lmer assumes they are factors > m <- lmer(a ~ b + (1|c/d)) > > ### but bails out when they are actually

...ting some big matrix results, I suppose that could explain it. 2. What does "stack imbalance in .Call" mean in lmer? Here's why I ask. Searching for comparisons of lmer and HLM, I went to CRAN & I checked this document: http://cran.r-project.org/web/packages/mlmRev/vignettes/MlmSoftRev.pdf I *think* these things are automatically generated. The version that's up there at this moment (mlmRev edition 0.99875-1) has pages full of the error message: stack imbalance in .Call, Were those always there? I don't think so. What do they mean? 3. In the HLM6 output, there i...

Group, I am new to R. In my work as a program evaluator, I am regularly asked to estimate effect sizes of prevention/intervention and educational programs on various student outcomes (e.g. academic achievement). In many cases, I have access to data over three or more time periods (e.g. growth in proficiency test scores). I usually have multiple independent and dependent variables in each

Hi everone, I have a question on using lme on a mixed effects model. The linear mixed model is in the form of: y = bX +Zu + e where "X" and "Z" are the matrices, "b" is the coefficient vector of fixed effects, "u" is the coefficient vector of random effects, and e is an error vector. I would like to use "lme" function to fit the model and

Your response nicely clarifies a question that I've had for a long time, but which I've dealt with by giving each subject a unique label. Unless I'm missing something, both techniques should work as the toy example below gives exactly the same output in all 3 cases below (forgetting about the convergence problem). Would there be a reason to prefer labeling the levels one way or

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

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same

...SE, niterEM = 0))) [1] 2.22 0.00 2.22 NA NA On my Windows machine, the first model fit using lme took just over a minute whereas the same model using lmer was estimated in 2.22 seconds. There are other examples of how to use the lmer function in the mlmRev package found using vignette("MlmSoftRev") This is anything but comprehenssive, but I hope others can chime in and share their experiences, or correct any errors I may have made above. Best, Harold > -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On...

Dear list members, I'd like to perform a glm analysis with a hierarchically nested design. In particular, I have one fixed factor ("Land Use Classes") with three levels and a random factor ("quadrat") nested within Land Use Classes with different levels per classes (class artificial = 1 quadrat; class crops = 67 quadrats; and class seminatural = 30 quadrats). I have four

Hello All, I would like to retrieve some of the results from the lmer(...) function in library lme4. If I run a model, say fm.1 <- lmer(y ~ 1 + (1 | x), data = dog) and try names(fm.1), I get NULL. Is there anyway to retrieve the information? Thanks

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Dear R-list, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN