similar to: within group variance of the coeficients in LME

Dear listers, I have two questions: (1) Is there a way in R to change the base-n of the calculations. I wnat to run some calculations either in binary (base-2) or base-4. Is there a way to specify that in R - to chnage from the decimal? (2) I also want to extract the digits from a larger number and store them as a vector. I could do it through converting top string, parsing the string and

HI all, I might add some more information in order to possibly solve my problem. I'm really stuck and no obvious solutions do the trick. I'm using R 1.7.1 on Windows 2000 with the packages regurarly updated. I'm using hypothetical data constructed as a pseudo population conforming to a certain Var-Cov structure. I might add that just > predict(level2) works. But when I add the

Hi! I am a relatively new user of R and I use it to prepare my dissertation. I have come to some very usefull and specific libraries written for S-PLUS 4 and would like to use them in R. Is that possible? I just found out that one of these libraries has already been transfered to R, while 3 others have not. For the matter of beeing more exact I''m interested in the dealing with missing

Dear listers, I must first say that all the suggestions and help I got from the list so far was of great help to my work. I approach you with another question. Is it possible to generate an automatic specification of the formula for lme (or other models in R)? Let me clarify. I'm using models in a simulation run and can have variable number of variables in the data. I would like to generate

Dear listers, First let me appologize if the same mail arrives multiple times. Recently I had some probelms sending my e-mails to the list. I encountered a problem when running glmmPQL procuedure doing multilevel modeling with a dichotomous outcome. Those are the two error messages I usually get: Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3)

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

I am trying to write a function that will run a linear model and plot the regression coeficients with their corresponding means. I am having two problems. I can get the plot with the function below, but I am having trouble labeling the points. function(y,x1,x2,x3,x4){ outlm<-lm(y~x1+x2+x3+x4) imp<-as.data.frame(outlm$coef[-1]) meanvec<-c(mean(x1),mean(x2),mean(x3),mean(x4))

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

I have a factor (with "n" observations and "k" levels), with only "nobs" < n of the observations not missing. I would like to produce a (n x k) model matrix with treatment contrasts for this factor, with rows of NAs placeholding the missing observations. If I use model.matrix() I get back a (nobs x k) matrix. Is there an easy way to get the (n x k) without

I am still fairly new to R and have a fairly rudimentary question. I am trying to name a vector of coefficients retrieved from a multilevel model using the coef function. I guess the default name is "Intercept" and I cannot figure out how to rename it. I have tried the using the code below to name the column of coefficients ind.y derived from an lme model. Unfortunately, the

Dear Users, I am trying to understand the inner workings of a repeated measures linear model. Take for example a situation with 6 individuals sampled twice for two conditions (control and treated). set.seed(12) ctrl <- rnorm(n = 6, mean = 2) ttd <- rnorm(n = 6, mean = 10) dat <- data.frame(vals = c(ctrl, ttd), group = c(rep("ctrl", 6), rep("ttd",

I've got a PhD student starting this year. She'll be working on data mining. She has asked for a reading list while she's still in her home country, which is a really good sign. Her cosupervisor and I don't (think we) have any problem with choosing things that are specifically about data mining, but there are some statistical ideas (sampling, exploratory-vs-confirmatory,

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Hi , I am programming on a windows system and have problems using notepad which is my main editor.Each time I try to open the editor from the R IDE, R crashes. So I always have to copy my codes from notepad and paste in R to run them. CAn someone tell me if I am doing anything wrong or is there a better editor(freeware) which I could get. thanks cilver ===== ===================== Sylvie B.

Can a single random number be generated in R? I have an exercise that wants to simulate coin tosses, and I cannot seem to find a good example of the use of random number generation in R. Any help? Joshua Gramlich Chicago, IL

Greetings, This seems too rudimentary to ask but for the life of me I cannot locate a readily easy method to compute the univariate mode. I know "mode" is not correct and "table" provides a reasonable count but I figured there would be an easy way to extract the value from the table after I do something like: max(table(mydadat$myvar)) unfortunately it only returns the max

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

Dear listers, I would like to get the bootstrap estimates form my lme model. I have an HLM (multillevel) 2-level model with the dichotomous outcome. I used glmmPQL procedure. However I have a problem since I have a rather unbalanced proportion (90-99% of events, i.e. ones and only 1-10% of nonevents - zeros) although sample sizes are not that small between 500 and 1000 I get pretty weird and

Dear list, i'm trying to test if a linear combination of coefficients of glm is equal to 0. For example : class 'cl' has 3 levels (1,2,3) and 'y' is a response variable. We want to test H0: mu1 + mu2 - mu3 =0 where mu1,mu2, and mu3 are the means for each level. for me, the question is how to get the covariance matrix of the estimated parameters from glm. but perhaps there

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects: