similar to: non-linear model parameterization

I would like to combine time-series data to test for correlations and interactions using random and fixed effects meta-analysis. So, I am looking for the right packages and documentation. I know about meta and rmeta packages of R. Are there any more? What are the diffrences in brief? Can you please suggest some references that could be used as a guide for meta-analysis in R (or S-plus)?

Hi all! I try to estimate a statistic of the form: (x1-x2)/(y1-y2), where x1,x2,y1,y2 represent variable means, so each has an estimate and standard error associated with it. How is it possible to estimate the mean and the variance of this ratio? Thank you! [[alternative HTML version deleted]]

Dear all, I have a for loop which includes nls model estimation. The loop breaks after the first non-convergence error. How can I make the loop continue and try to estimate all models? I suppose it should be sth like: if(...) { next } but I have no idea how to setup the arguements... Thank you! Irene

I would like to fit a 2-level mixed model: yit=a+a[i]+a[it] +(b+b[i]+b[it])*xit+eps[it] However, the variance of the second level components should depend on the group, i.e. sigma for a[it] and b[it] should be [i] specific. I do not know whether this is conceptually right in the mixed model context... In case it stands, how should the formula look like? Also, the data are unbalanced with

Hi all! I am trying to run a regression where the predictor values are not real data but each is estimated from a different model. So, for each value I have a mean and variance. Which package/function should I use in this case? Thank you! Irene [[alternative HTML version deleted]]

Dear list, I would like to produce a plot of variables where the size of the points will be indicative of their standard errors. How is that possible? Thank you! [[alternative HTML version deleted]]

I am a new R user and so I thought I could start with "Using R for Introductory statistics" by Verzani. In order to use some of the functions and datasets I have to install the simpleR package which is is now inside the UsingR package. I did so using >install.packages("UsingR"). However, the functions such as "simple.freqpoly.R" do not work. I also tried to

Hi all! How is it possible to estimate standard errors for coef obtained from lme? Is there sth like se.coef() for lmer or what is the anaytical solution? Thank you!

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Dear all, I am trying to define a selfStart function for a non-linear model, which is a log-transformed SSmicmen model with multiplicative errors and so it is required to make them additive: log(y)=log(a)+log(x)-log(1+x/b) Any ideas about how to use the "peeling" method to derive the "initial" argument and get the initial values? Thank you for being always there!:)

Hi all! this is a rather statistical question: is it meaningful to consider an interaction effect between 2 continuous covariates? for example: lm(y~x1+x2+x1:x2) Should one of continuous x1, x2 be "transformed" to a categorical variable, i.e. be classified into groups? Is it easier to interpret the effect if 1 or both are centered to the mean or z-transformed? Thank you!

Dear all, I have an annual time-series of population numbers and I would like to estimate the auto-correlation. Can I use acf() function and judge whether auto-correlation is significant by the plots? The acf array, eg: Autocorrelations of series 'x$log.s.r', by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000 0.031 -0.171

Is there a formal way to prove the need of a mixed model, apart from e.g. comparing the intervals estimated by lmList fit? For example, should I compare (with AIC ML?) a model with seperately (unpooled) estimated fixed slopes (i.e.using an index for each group) with a model that treats this parameter as a random effect (both models treat the remaining parameters as random)? Thank you!

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

Dear R-users, A recent post (Feb 16) to R-help inquired about fitting a glmm with a negative binomial distribution. Professor Ripley responded that this was a difficult problem with the simpler Poisson model already being a difficult case: https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html Since we are developing software for fitting general nonlinear random effects models we

Hi, I am following the below instructions for setting up an RStudio server in Windows. https://support.rstudio.com/hc/en-us/articles/360049776974-Using-RStudio-Server-in-Windows-WSL2 I am a novice so I might be making an obvious mistake, but I am receiving an error in step 3 of the RStudio server setup. I entered the below code into ubuntu: sudo add-apt-repository 'deb

Dear all, I have some very big data files that look something like this: id chr pos ihh1 ihh2 xpehh rs5748748 22 15795572 0.0230222 0.0268394 -0.153413 rs5748755 22 15806401 0.0186084 0.0268672 -0.367296 rs2385785 22 15807037 0.0198204 0.0186616 0.0602451 rs1981707 22 15809384 0.0299685 0.0176768 0.527892 rs1981708 22 15809434 0.0305465 0.0187227 0.489512 rs11914222 22 15810040 0.0307183

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

Dear Helpers, I would like to export a large dataset to a txt file in order to use it in an other programm. Unfurtunatly the R the scientific format is a small e: 2 e-1 while the other programm requires the format to be a capital E: 2E-1 How can I change this in R? Thanks for your help PS: I already found the command to turn them into a non scientific format (scientific=FALSE)

Hello, I am on the hook to instrument a piece of legacy LLVM IR code, and then we are planning to feed to the SeaHorn framework for some model checking tasks. After the instrumentation, I tried to use llc (version 3.9) to compile the IR code, and it works fine. However, when I try to use llc (version 3.8.1, the default llvm version of SeaHorn) to compile the IR code, it shows the following