similar to: modeling variance heterogeneity in lme4

Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is

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

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

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

As I am reading ?arima, only NA entries in the argument fixed= imports. The following seems to indicate otherwise: x <- arima.sim(model=list(ar=0.8), n=100) + (1:100)/50 > t <- 1:100 > mod1 <- lm(x ~ t) > > init1 <- c(0, coef(mod1)[2]) > fixed1 <- c(as.numeric(NA), 0) > > arima(x, order=c(1,0,0), xreg=t, include.mean=FALSE, init=init1, fixed=fixed1)

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

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

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

I'm dealing with count data that's nested and has spatial dependence. I ran a glmm in lmer with a random factor for nestedness. Spatial dependence seems to have been accommodated by model. However I can't add a variance strcuture to this model (to accommodate heterogeneity). Is there a model that can have a poisson distribution *AND* a variance structure *AND* have AIC in output (for

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

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:

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

I am modeling the effects of an environmental variable (X) on fish recruitment (Y) for several, ecologically related species (i) using a mixed-effects model. The linear relationship between X and Y includes a fixed effect that is common across all species and random effects that vary by species. In the lmer() notation from the package lme4: Model<- lmer(Y ~ X +(X|i)) Because the residuals of

Dear R users, We have encountered a slight problem when using the lmer() function: 1. Data description: 11 locations; Nt: monthly mosquito population density from 1994-2005 in each location. 2. Question: to examine the degree of spatial heterogeneity in the system by testing model support for single versus multiple intercepts and slopes for the location effect. We applied the lmer()

Dear R-ians, I'm looking for a computational simplified formula to calculate a measure for heterogeneity (let's say H ): H = sqrt [ (Si (Sj (Xi - Xj)?? ) ) /n ] where: sqrt = square root Si = summation over i (= 0 to n) Sj = summation over j (= 0 to n) Xi = element of X with index i Xj = element of X with index j I can simplify the formula to: H = sqrt [ ( 2 * n * Si (Xi) - 2 Si (Sj

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 postulate the following model AC <- glmer(Accuracy ~ RT*Group + (1+RT|Group:subject) + (1+RT|Group:Trial), data = da, family = binomial, verbose = T) Here I predict Accuracy from RT, Group (which has values 0 or 1) and the interaction of Group and RT (those are the fixed effects). I also estimate the random effects for both intercepts and slopes for subjects and different trials.

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

Hello all, I am using a glm() and would like to fix one of the regression coefficients to be a particular value and see what happens to the fit of the model. E.g.: mod1 <- glm(Y ~ X1 + X2,family='binomial') mod2 <- glm(Y~[fixed to 1.3]X1 + X2,family='binomial') The beta for X1 is freely estimated in mod1 but is constrained to be 1.3 in mod2. Is there a way to do this?

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