similar to: weights in mixed model

Hi, Is there a function in R that I can use to fit the data with skew t distribution? Speaking in detail, I first used the kernel density estimation to fit my data, then I drew the skew t using my specified location, scale, shape, and df to make it close to the kernel density. Now I want to get the parameter estimations of the skew t which give me the closet density to the kernel density.

Hi, I'm trying to find the corresponding Fortran77 subroutines for R function pt(). I tried some Fortran77 subroutines to compute the t distribution function. But none of them are as good as R function pt(). Does anyone can give me some information about it? Thank you very much! Tianyue

I asked this before and I am going to try again in more applied terms. I am trying to use R to extract variance components for a two-factor random effects model with both factors crossed. It would also be nice to generate some confidence intervals as well. For example, a data set using SAS Proc Mixed is below followed by the four variance component estimates and the respective confidence

Dear lattice wizards, I am trying to figure out how to plot predicted values in xyplot, where the intercept, but not the slope, varies among conditioning factor levels. I am sure it involves the groups, but I have been unsuccessful in my search in Pinhiero and Bate, in the help files, or in the archive, or in my attempts on my own. My example follows: FACT is a factor with levels a,b,c

Dear listserv members, In Croissant (2008) “Panel Data Econometrics in R: The plm Package” the authors seem to indicate that the nlme package for R cannot correctly handle unbalanced panel data: “Moreover, economic panel datasets often happen to be unbalanced (i.e., they have a different number of observations between groups), which case needs some adaptation to the methods and is not

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10

Hello all! I have a problem that calls for a better understanding, than mine, of how lme() uses the random part of the call. The dataset consists of eleven field trials (Trial) with three replicates (Block) and four fertiliser treatments (Treat). Analysing for example yield with lme() is easy: m1 <- lme(Yield ~ Treat, data=data, random =~1| Trial/Block) giving estimates of

Dear experts, I'm trying to transfer a mixed model developed in SAS to R. This it what it looks like in SAS: proc mixed method=ml; class a b c subj; model y = a|b|c; repeated /subject=subj type=ar(1); I tried something like this in R: mixed <- lme(y ~ a + b + c + a*b + a*c + b*c + a*b*c, random = ~ 1 | subj, correlation = corAR1(form = ~ 1 | subj)

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT -------------------- (A) Non-?NULL? ?weights? can be used to indicate that different observations have different variances (with the values in ?weights? being inversely proportional to the variances); (B) or equivalently, when the elements of

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

OK. We have now three suggestions to repair the text: - remove the text - add "not" at the beginning of the text - add at the end of the text a warning; something like: "Note that in this case the standard estimates of the parameters are in general not correct, and hence also the t values and the p value. Also the number of degrees of freedom is not correct. (The parameter

To anyone who can help, I have two stupid questions, and one fairly intelligent question Stupid question (1): is there an R function to calculate a factorial of a number? That is...is there a function g(.) such that g(3) = 6, g(4) = 24, g(6) = 720, etc? Stupid question (2): how do you extract the estimated covariance matrix of the random effects in an lme object? Intelligent question

Dear all, I would like to simulate data from a nlme model using fixed effects and the variances of the random effects distribution. I scanned the help files and the mailing lists with no succes. Thanks for your help! Peter

I had build ocfs2-tools-1.2.3 from sources on x86_64 machine (FC6). When I run: ocfs2console I get: Traceback (most recent call last): File "/usr/local/sbin/ocfs2console", line 3, in ? from ocfs2interface.about import process_args ImportError: No module named ocfs2interface.about When I try: locate ocfs2interface.about I get no result. Any ideas what went wrong ? Regards, Andy

Hi, I would be grateful for help with a problem which is irritating me. I am quite sure that I am doing something stupid, but I can't see what it is. I am running R 1.7 on Windows 2000. The graphics device is the PC screen. The graphics from the nlme demonstration in Bates an Pinheiro's manual work just as advertised. The CO2 data and the Orthodont data dsiplay

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

Hi all, I am doing a project that requires counting the number of uses of a value only in all dominated blocks in a function. I checked the manual for llvm::User class, but only get this unsigned <http://llvm.org/test-doxygen/api/classunsigned.html> getNumUses <http://llvm.org/test-doxygen/api/classllvm_1_1Value.html#a380559e99b47632a34f74de7c1d03cc0> () const This gives me all the

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

Hi helpeRs, I've been working through some examples in Pinhiero & Bates( 2000) trying to understand how to translate to the new Lme4 syntax but without much luck. Below is what I think I should do, but either the answers don't come out the same or I get errors. In the Oxide problems I'm particularly interested in obtaining the levels coeficients but this options no longer seems