similar to: Help with problem - multilevel model?

I think this is an easy question, but I would be grateful for any advice on how to implement this in R. I simply have a response variable (y) that I am trying to predict with one explanatory variable (x) but the shape of the scatter plot is distinctly bilinear. It would be best described by two straight lines. Is there a way of fitting a linear model to give me a bilinear fit and (more

I would be very grateful for any help from members of this list for what might be a simple problem... We are trying to simulate the behaviour of a clinical measurement in a series of computer experiments. This is simple enough to do in R if we assume the measurements to be Gaussian, but their empirical distribution has a much higher peak at the mean and the distribution has much longer tails.

Hi R users I want to use the lme package for a multilevel analysis on the following example: > math<-c(2, 3,2, 5, 6 ,7 , 7) > sex<-c(1, 2, 1, 2, 2, 2, 1) > school_A<-c(1,1,1,2,2,2,2) > school_B<-c(10,10,10,20,20,20,20) > mydata<-data.frame(math, sex, school_A, school_B) > mydata School_A and school_B are two different school characteristics, math is an

I have upgraded one of my servers from a 3.0.22 implementation using the rfc2307 patch I supplied some months ago to the 3.0.23 release. I am now getting some unexplaned failures and would like some pointers as to where to start looking. I am getting the following logged in the samba logs when trying to start the servers. zebra.log: Sid S-1-5-32-544 -> BUILTIN\Administrators(4)

It is not a pure R question,but I hope some one can give me advices. I want to use analysis my data with the multilevel model.The data has 2 levels---- the second level has 52 units and each second level unit has 19-23 units.I think the sample size is quite small,but just now I can't make the sample size much bigger.So I want to ask if I use the multilevel model to analysis the data set,will

Goodmorning everybody, i'm an italian statistician and i'm using R for research. Could someone tell me some indices to see the goodness of fit in multilevel modelling? I'm using the lmer function, and I want to know if my model fit well my data. I actually want to justify the use of multilevel model instead the classical one. Hope someone can help me. Thank you. Greetings

Dear all, I am struggling with a problem which I have been reading on the forums about and it did not seem to me that there is a precise answer to my question. However, I still hope there is one. I am working with http://timss.bc.edu/ PIRLS data and trying to conduct multilevel analysis. There are different weights for each level of analysis in the PIRLS dataset (e.g. there is a school

I would be grateful if members of the list could point me in the direction of any code (preferably in R) that will allow me to estimate 95th percentiles from a set of repeated measurements. For example, we are interested in a clinical measurement where we have 3 measures for 14 subjects and 2 measurements on 24 subjects and single measurement on 36 subjects. We want to combine these to form a

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

Hi R folks (Dr. Bates in particular), In August 2005, Dr. Bates mentioned that the documentation for lme4 "will be in the form of a book with the working title 'Multilevel Modeling in R'" and I'm just wondering if there is an estimated date of publication or if it's still a long way off. The Rnews article does a great job of introducing the package, but I'm

Hello -- I am curious if lmer can be used to fit a multilevel factor model such as a two-parameter item response model. The one parameter model is straightforward. A two-factor model requires a set of factor loadings multiplying a single random effect. For example, a logit model for the ith subject responding correctly to the jth item (j=1,..,J) is logit[p(ij)] = a1*item1(i) + ... + aJ *

Dear ListeRs, I am trying to fit a heteroscedastic multilevel model using lmer{lme4- package). Take, for instance, the (fictive) model below. lmer(test.result ~ homework + Sex -1 + (1 | School)) Suppose that I suspect the error terms in the predicted values to differ between men and women (so, on the first level). In order to model this, I want the 'Sex'-variable to be random on

Hi, I'm trying to reshape my data set from wide to tall format for multilevel modeling. Unfortunately, the function I typically use (make.univ from the multilevel package) does not appear to work with unbalanced data frames, which is what I'm dealing with. Below is an example of the columns of a data frame similar to what I'm working with: ID a1 a2 a4 b2 b3 b4 b5 b6 Below

* Please cc me if you reply as I am a digest subscriber * Hi, I am wondering how I can run a multilevel survival model in R? Below is some of my data. > head(bi0.test) childid famid lifedxm sex age delta 1 22.02 22 CONTROL MALES 21.36893 0 2 13.02 13 MAJOR MALES 21.18001 0 3 64.02 64 CONTROL MALES 20.09377 0 4 5.02 5 CONTROL FEMALES

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 All, I use R to conduct multilevel modeling. However, I have a problem about the interpretation of random effect. Unlike the variables in fixed effects, the variables in random effects have not shown the p-value, so I don't know whether they are significant or not? I want to obtain this figure to make the decision. Thanks a lot! Below is the syntax and output of my program:

I have a multilevel dataframe (df): ID Date Segment Slice Tract Lesion 1 CSPP005 12/4/2007 1 1 LCST 0 2 CSPP005 12/4/2007 1 1 LPC 2 3 CSPP005 12/4/2007 1 1 RPC 3 4 CSPP005 12/4/2007 1 1 RCST 1 5 CSPP005 12/4/2007 1 1 LGM 0 6 CSPP005 12/4/2007 1 1

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <-

data from multilevel units,first sample the class ,and then the student in calss.following is the 2-level model. and the level-1 model deals with the student,and the level-2 model deals with the class level the students belong to. Level-1 Model Y = B0 + B1*(ZLEAD) + B2*(ZBUL) + B3*(ZSHY) + R Level-2 Model B0 = G00 + U0 B1 = G10 + G11*(ZWARMT) + U1 B2 = G20 + G21*(ZWARMT) + G22*(ZABLET) +