similar to: Looking for transformation to overcome heterogeneity of variances

Peter You question is difficult to answer without more information about the distribution of your residuals. Different residual patterns call for different transformations to stabilize the variance. One very common form of heterocedasticity is increasing variance with increasing values of an independent predictor, i.e. the variance of the residuals of y=x increase as x increases. In this case a

Dear All, I have a massive dataset from which I would like to draw a histogram. Any ideas on how to accomplish this? Thanks in advance, Paul

Hi All, I wonder if you can help me with an aparently simple task.  I have been searching examples for this without any luck: #Assume x<-1:10  #x ranges from 1 to 10. y<-x*runif(10)+ 1.5*x  #y is a linear function of x with some error. Add uniform error that is scaled to be larger as x values also become larger #error is proportional to x size, this should cause heterocedasticity. #I

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

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

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

anyone know how to add text in the Trellis plot panel ?? i want to add things eg: dot dot dot. in the headrer of the panel. eg: http://old.nabble.com/file/p26486579/hist1.png hist1.png -- View this message in context: http://old.nabble.com/Trellis-Plot-tp26486579p26486579.html Sent from the R help mailing list archive at Nabble.com.

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

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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

Appologies if this has been addressed before, but I can't seem to find it in the help archives. I'm looking to do something like the following but it looks like save.plot is deprecated. save.plot(plot(glm1$residuals,gain,main = "Hist of residuals and gain"),file="Desktop/hist1.png") Thanks in advance, Sean Session Info: R version 2.9.1 (2009-06-26)

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:

Rhelpers: I'm curious what the appropriate analysis to use for testing the hypothesis that two histograms are statistically different from one another? Thanks! --j

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

Dear List, possibly called Frank, I tried to create an ftable lookalike of the following data set in LaTeX/Sweave with summary(formula,..), but I could not get it to work for count tables; numeric tables work fine. summary(formula,...,fun="table") does not give the full cross breakdown. Other suggestions welcome. Note that in the output there should be empty fields on repeated

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

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

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

Hello R community, I am looking for a robust test for equality of variances that can take observation weights. I realize I can do the F-test with weighted variances, but I've read that this test is not very robust. So I thought about maybe adding a "weights" argument to John Fox's code for the Levene Test (in the "car" library, "levene.test"), substituting

Hello, I have some patient data for my masters thesis with three groups (n=16, 19 & 20) I have completed compiling the results of 7 tests, for which one of these tests the variances are unequal. I wish to perform an ANOVA between the three groups but for the one test with unequal variance (<0.001 by both bartlett and levene's test) I am not sure what to do. I thought i would run