similar to: regression with error in predictor

Dear all, I would like to fit a non-linear model of the form: y=g*x/(a+b*x) with nls(). However this model is somehow overparameterized and I get the error message about singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is

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 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]]

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

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]]

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!:)

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

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! 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!

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

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 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

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

Hello all, Currently, the default behavior for system.file() is to return "" for faulty paths. I've found this behavior to be difficult when debugging, since it passes the empty path onto other functions. I initially wrote in errors myself (with code like `if(path == "") stop(?Path not found?)`), but I now use mustWork=TRUE or fs::path_package(), which errors by default.

Dear R users, I have a dataframe (main.table) with ~30,000 rows and 6 columns, of which here are a few rows: id chr window gene xp.norm xp.top 129 1_32 1 32 TAS1R1 1.28882115 FALSE 130 1_32 1 32 ZBTB48 1.28882115 FALSE 131 1_32 1 32 KLHL21 1.28882115 FALSE 132 1_32 1 32 PHF13 1.28882115 FALSE 133 1_33 1

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

I am investigating options for using Samba 3.0.7.2.FC1 (Red Hat Fedora Core 1 basic installation, currently updating via yum) as a primary and only domain controller. We have a Windows user environment, and I'm trying to connect the user machines (XP fully patched as of this writing) to the samba domain but keep getting one of 2 errors: 1) "authentication failed", when I use the

Hello List, 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