search for: heterogeneous

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 /// heterogeneous nuclear ribonucleoprotein K 319...

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

...matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and <http://blog.lib.umn.edu/moor0554/canoemoore/2009/09/sem_complex_samples_r_update.html>). However, since I have categorical/ordinal variables, I would like to compute a weighted heterogeneous correlation matrix with hetcor(). Is there a way to do this in R? I couldn't find any guidance in the r-help archives or in the polycor help files. Should I truncate the sampling weights to integers and then populate the data set with redundant rows/cases so that the number of rows equals t...

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... ##

...is problematic as it makes everything from simple constant propagation to kernel splitting/fusion painfully hard. The proposal is to merge host and device code in a single module during the optimization steps. This should not induce any cost (if people don't use the functionality). But how do heterogeneous modules help? -------------------------------------- Assuming we have heterogeneous LLVM-IR modules we can look at accelerator code optimization as an interprocedural optimization problem. You basically call the "kernel" but you cannot inline it. So you know the call site(s) and argument...

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

...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 in drive size, hardware, cpu, etc will be such that it won't be possible (or sensible) to buy the same exact hardware, sized drives, etc... A heterogeneous pool is unavoidable. Is there a general case solution for this problem? Is something planned to deal with this problem? I can only think of a few specific corner case solutions. Another problem that comes to mind is ensuring that the older slower servers don't act as bottlenecks to the whole...

Hi! My question(s) in the end might be silly but I am no expert on this, so here it goes: Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered PCA being in some cases useful. They clearly explain that "it is the case" when multi-dimensional data display distinct clusters (which have zero, or near-zero, projections in some subset of the axes) and the task is

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 set that can be mapped down to a wide range of parallel hardware accelerators; and (b) to develop optimizing "back-end" translators...

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 Scientist...

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

...for the 64 bit architecture and: R_SNOW_LIB="/usr/local/lib/R/library" export R_SNOW_LIB R_HOME_LIB="/usr/local/lib/R/library" export R_HOME_LIB for the 32 bit architecture. In addition the same version of R is installed on all computers (nodes + master). Nevertheless, the heterogeneous cluster is not working... Does anyone can help me solving this problem? Thanks a lot in advance. Florent -- -------------------------------------------------- Dr Florent BATY Pulmonary Gene Research, Universit??tsspital Basel Petersgraben 4, CH-4031 Basel, Switzerland tel: +41 61 265 57...

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: