similar to: Mixed Modeling in lme4

Dear All, I would like to make a quadratic with a plateau model in R. Is there a package in R doing this? The bentcableAR package seems won't work. The link below describes what I am looking for in R exactly: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_nlin_sect033.htm -- Thanks so much! Orange help.ly2005@gmail.com [[alternative HTML version

DeaR experts, I was asked for a log-rank pairwise survival comparison. I've a straightforward way to do this using the SAS system: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#/documentation/cdl/en/statug/63033/HTML/default/statug_lifetest_sect019.htm What I've found in R is shown below, but it's not a logrank test, I suppose. (The documentation

Hi again, thank you for your previous answers. Just another question, though ... I get the following variance components after fitting a mixed model. Groups Name Variance Std.Dev. Corr PlantID TreatmCtrl 0.51784 0.71961 TreatmNoAccess 4.77469 2.18511 -0.063 TreatmNoKeel 4.22726 2.05603 0.513 0.751 TreatmNoSpur 0.45918

Hi I am trying to convert SAS codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt

Dear R-gurus, I am pretty much new on R. I am trying to to do a repeated analysis of a linear mixed model with R, and I consistently fail... The problem is: Cow is the random factor, treatment is the fixed factor. The dependent variable is milk yield, which is measured several times (repeatedly over time), thus there is another variable which is time (i.e. week). The model would be

Hi All, I'm working with some SAS code to analyze an experiment set up as follows: 66 subjects (colonies) treated with a random treatment (1-8) and measured at three time points. The data structure looks like: input colony tmt y1 y2 y3; y=y1; date=*1*; output; y=y2; date=*2*; output; y=y3; date=*3*; output; datalines; 1

Hello, R community, I have been using the lmer and mcmcsamp functions in R with some difficulty. I do not believe this is my code or data, however, because my attempts to use the sample code and 'sleepstudy' data provided with the lme4 packaged (and used on several R-Wiki pages) do not return the same results as those indicated in the help pages. For instance: > sessionInfo() R

Hi, I am new to this list as a poster, but a reader for some time. I've using R for several weeks now, and I have a lot of questions about certain procedures. Here I go: I want to test if there are differences in the time spent by pollinators visiting flowers of a given plant species, according to a number of experimental manipulations made on those flowers. All experimental

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

My Mail StationeryIs it possible to have interactive graphic display in R just like S-Plus? ______________________ Indrajit SenGupta Department Of Statistics St. Xavier's College Calcutta University indra_calisto at yahoo.com indrajitsg at vsnl.net -------------- next part -------------- An HTML attachment was scrubbed... URL:

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

Buenos días; Estoy intentando aprender algo de sobre los modelos lineales mixtos. Estoy siguiendo el libro "Multilevlel analysis: techniques and applications" de Joop Hox. Estoy intentado reproducir las tablas y gráficos que trae el libro con el paquete lmer. En este libro se indica que en la comprobación de los supuestos del modelo hay que estudiar los gráficos de residuales tanto

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

In the file MkRules.local.in, I see the line: USE_ATLAS = NO which I believe needs to be changed to YES. But how do I specify the BLAS file *libopenblas_haswell-r0.2.20.a *and its location? Regards, Indrajit On Tue, Feb 13, 2018 at 6:41 PM, Indrajit Sen Gupta <indrajitsg at gmail.com> wrote: > I was able to compile the R from the github by running build-r-devel.bat! > > Now need

>>>>> Therneau, Terry M , Ph D <therneau at mayo.edu> >>>>> on Sun, 15 Feb 2015 17:31:00 -0600 writes: > I'm testing out a new version of coxme and R CMD check fails with "could not find function > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > line below >

On 16/02/2015 8:20 AM, Therneau, Terry M., Ph.D. wrote: > >> > I'm testing out a new version of coxme and R CMD check fails with "could not find function >> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the >> > line below >> >> > importFrom(nlme, ranef, random.effects, fixef,