search for: heterogenous

My data have heterogeneity of variance (in a categorical variable), do I need to specify a variance structure accounting for this in my model or do GLMMs by their nature account for such heterogeneity (as a result of using deviances rather than variances)? And if I do need to do this, how do I do it (e.g. using something like the VarIdent function in nlme) and in what package? This is my first

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Dear all, I try to use read.table to get the data from a tab delimited file, and some of the data is shown below: 3185 heterogeneous nuclear ribonucleoprotein F 3187 heterogeneous nuclear ribonucleoprotein H1 (H) 3188 heterogeneous nuclear ribonucleoprotein H2 (H') 3189 heterogeneous nuclear ribonucleoprotein H3 (2H9) 3190 heterogeneous nuclear ribonucleoprotein K ///

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

Dear list members, I am looking for an alternative function for a two-way ANOVA in the case of variance heterogeneity. For one-way ANOVA, I found oneway.test(), but I didn't find anything alike for two-way ANOVA. Does anyone have a suggestion? Thank you! Maike Luhmann Freie Universit?t Berlin

Hi all, I have major heterogeneity in variances across labs (100-fold). There is no apparent variance heterogeneity across y-hat. By using lme4 in the following way, am I accounting for the variance differences in labs?: lmer(y ~ fixed1 + covariates + (fixed1|labs)) I'm not sure that it is - I think it is only allowing the means (slopes [conditional means] & intercepts) to differ

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

TL;DR ----- Let's allow to merge to LLVM-IR modules for different targets (with compatible data layouts) into a single LLVM-IR module to facilitate host-device code optimizations. Wait, what? ----------- Given an offloading programming model of your choice (CUDA, HIP, SYCL, OpenMP, OpenACC, ...), the current pipeline will most likely optimize the host and the device code in isolation. This

Hello. I want to estimate the predicted values and standard errors of Y=f(t) and its first derivative at each unique value of t using the smooth.spline function. However, the data (plant growth as a function of time) show substantial heterogeneity of variance since the variance of plant mass increases over time. What is the consequence of such heterogeneity of variance in terms of bias in the

Dear All My data consists in 96 groups, each one with 10 observations. Levene's test suggests that the variances are not equal, and therefore I have tried to apply the classical transformations to have homocedasticity in order to be able to use ANOVA. Unfortunately, no transformation that I have used transforms my data into data with homocedasticity. The histogram of variances is at

I have heterogeneous variance in a two-way mixed effects ANOVA, with more than 2 groups in each factor. Is there something like oneway.test or a Brown-Forsythe test that will let me test for differences in means? Thanks! -Keith

I have numerous replicated goodness-of-fit experiments (observed compared to expected counts in categories) and these replicates are nested within a factor. The expected counts in each cell are external (from a scientific model being tested). The calculations I need within each level of the nesting factor are a heterogeneity G test, with the total G and the pooled G across replicates. Then I

Hi there, This is a hypothetical problem, not one that describes specific hardware at the moment. As we all know, gluster currently usually works best when each brick is the same size, and each host has the same number of bricks. Let's call this a "homogeneous" configuration. Suppose you buy the hardware to build such a pool. Two years go by, and you want to grow the pool. Changes

...authors mention some measure for the assymetry of the yielded compoenents ( uncentered -> unipolar, centered -> bipolar) I find the concept a bit hard to understand. Isn't there a quick way (function) to just say (with numbers of plots of course) "well, it seems that the data are heterogenous looking at between- axes" or the other way around "it looks like the variables differ within, more than between"? Apologies for repeating the same question (trying to understand the problem myself). Thank you, Nikos

Christos, We would be very interested in learning more about this. In my group, we (Prakalp Srivastava, Maria Kotsifakou and I) have been working on LLVM extensions to make it easier to target a wide range of accelerators in a heterogeneous mobile device, such as Qualcomm's Snapdragon and other APUs. Our approach has been to (a) add better abstractions of parallelism to the LLVM instruction

Dear R-help: Does anyone know of a package in R that will do Welch's modified F-test for heterogeneous error variances? Are there other statistical techniques available in R that test the equality of means when homoscedastisity is violated? 't.test' does this in the pairwise sense when var.equal = TRUE. With best wishes and kind regards I am Sincerely, Corey A. Moffet Support

dear R-help: one of my students is struggling to test an ordered alternative hypothesis on a set of groups (e.g., mu_a <= mu_b <= mu_c). There has been some literature on this topic -- a lot of this goes back to Bartholomew (1961); Gaines and Rice (see refs below) are the ones who've popularized it in the ecology community. The topic is closely related to isotonic regression, but

Dear R-users, I am trying to make Snow and Rmpi working on an heterogenous cluster of linux computers. The master computer is built on a 64 bit architecture whereas all nodes are built on a 32 bit architecture. LAM/MPI was installed successfully on all machine. LAM boots correctly on the master computer and it recognizes all nodes defined in the lamhosts file. Howeve...

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as: