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R friends, In a matrix of 1s and 0s, I'm getting a singularity error. Any helpful ideas? lm(formula = activity ~ metaF + metaCl + metaBr + metaI + metaMe + paraF + paraCl + paraBr + paraI + paraMe) Residuals: Min 1Q Median 3Q Max -4.573e-01 -7.884e-02 3.469e-17 6.616e-02 2.427e-01 Coefficients: (1 not defined because of singularities)

Hi all, I am currently doing a consistency analysis using an lmer model and trying to use parametric bootstrapping for the confidence intervals. My model is like this: model<-lmer(y~A+B+(1|C/D)+(1|E),binomial) where E is the individual level for consistency analysis, A-D are other fixed and random effects that I have to control for. Following Nakagawa and Scheilzeth I can work out the

Hello, I would appreciate *any* ideas on this problem. I'm the maintainer of a package ("subselect"), which on CRAN's Daily Package Checks is OK on all flavours of R, except r-devel Linux x86_64, where there is a "memory not mapped" segfault with the very first example that is tried out (output below). Additionally, a user with an AMD64 machine has just reported a

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.

Hello! I am performing coupling of chains in MCMC and I need the same value of seed for two chains. I will show demo of what I want: R code, which might show my example is: niter <- 3 nchain <- 2 tmpSeed <- 123 for (i in 1:niter) { # iterations for (j in 1:nchain) { # chains set.seed(tmpSeed) a <- runif(1) cat("iter:", i, "chain:", j,

I don't know the details of pls (in the pls.pcr package, I assume), but if you use validation="CV", that says you want to use CV to select the best number of components. Then why would you specify ncomp as well? Andy > From: ryszard.czerminski at pharma.novartis.com > > When I try to use ncomp parameter in pls procedure I get > following error: > > >

In their paper, "Lexical Scope and Statistical Computing", the authors ( Gentleman and Ihaka ) go to great length explaining why R's use of lexical scoping creates advantages when doing statistical computations. If anyone has or is familiar with this paper, could they provide the main program code for how the "newton" function would be called in their example on page 500

Hello, I'm working on a problem using nested for-loops and I don't know if it's a problem with the order of the loops or something within the loop so any help with the problem would be appreciated. To briefly set up the problem. I have 259 trees (from 11 different species, of unequal count for each species) of which I am trying to predict biomass. For each tree species I have 10000

I've only been using R on and off for 9 months and started using the glasso package for sparse covariance estimation. I know the concept is to shrink some of the elements of the covariance matrix to zero. However, say I have a dataset that I know has some underlying "baseline" covariance/correlation (say, a value of 0.3), how can I change or incorporate that into to the

Hello. I've developed an algorithm in R which I need to package. The implementation uses S4 objects and it's divided in 5 files. Everything is working fine when I load the files into the R console but when I try to make a package I get an error that I don't quite understand. Here's what I do: *1.* in R console, I do and get: > package.skeleton(name='remora') Creating

Hello, My problem is, that I have to build hundreds of GARCH models to obtain volatility forecasts. I would like to run a loop, that would build those forecasts for me. There is no problem, with writing only the results of the forecasts, but I'd like to have stored results of the models in some file, that I could check later, what are the models like, to be able to compare if I should use

Dear R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL function to model the proportion of fish caught in one net to the total caught in both nets by length. I started with a polynomial regression full model with three length terms: l, l^2, and l^3 (l=length). The length terms and intercept were the fixed effects and the random effect was a paired haul (n=18).

Hi All, 1) Is it possible to set the options such that R opens a new script editor every time I start the R and 2) specify the size of windows. Thanks for the suggestion and Best regards, Krishna [[alternative HTML version deleted]]

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Can anyone shed any light on this? > doubt.demographic.pql<-glmmPQL(random = ~ 1 | groupid/participantid, + fixed = r.info.doubt ~ + realage + minority + female + education + income + scenario, + data = fgdata.df[coded.resource,], + na.action=na.omit, +

Thanks to the help of people from this forum I was able to bootstrap my data and then apply a model to it. Thanks for all your help. Everything worked out well, but I am having a difficult time getting the new parameter values. I bootstrapped the data 300 times and I want to get the 300 sets of parameter estimates and plot them in Excel. Here is my code:

Hi, I'm having trouble with glmmPQL from the MASS package. I'm trying to fit a model with a binary response variable, two fixed and two random variables (nested), with a sample of about 200,000 data points. Unfortunately, I'm getting an error message that is difficult to understand without knowing the internals of the glmmPQL function. > model <- glmmPQL(primed ~

Dear R experts: I am running some calculations using polr() in MASS library, and found some differences in results obtained on two different machines (IRIX 6.5, and Linux RH 7.1). It is not clear to me whether this is due to some error in my programming the calculation and how to resolve the differences, if possible. The polr() call is the following:

The package minpack.lm allows nonlinear regression problems to be addressed with a modification of the Levenberg-Marquardt algorithm based on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of the package is now available on CRAN. Changes in version 1.0-8 include: o possibility to obtain standard error estimates on the parameters via new methods for