similar to: Testing the equality of two variances

Dear All, I have two samples as the following: conjps<-c(9.41,10.45,10.78,10.73,11.11,11.12,11.59,11.04,11.63) ms<-c(4.11,5.10,5.70,6.46,6.04,6.16, 6.24,6.32,7.33) I want to test the change of sample is same to the another one. conjps_ch<-c(1.04,0.33.....) ms<-c(0.99,0.60,0.76....) which test I should use, which conclusion can we drive based on the test? I used the following

Hello. I am fitting a two-level mixed model which assumes equality of variance in the lowest-level residuals across groups. The call is: fit3<-lme(CLnNAR~CLnRGR,data=meta.analysis, + na.action="na.omit",random=~1+CLnRGR|study.code) I want to test the assumption of equality of variances across groups at the lowest level. Can someone tell me how to do this? I know that one

Hi all, I know that I can run an one way ANOVA on the absolute residual values to check the assumption of equal variances: model<- lm(y~x) summary(lm(abs(model$resid~x))) What if I have two factors? As far as I know I have to check this assumption on very factor/level combination. So if I have 3 factors with three levels, I would have to test 9 factor/level combinations. Here comes my

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

Hi List. It may be a very simple question, but I couldn’t find an answer on the internet. Which function (maybe in a specific package) would I use to perform a Cochran C Test for Homogeneity of Variances? Stats package have the mantelhaen.test, but I’m “almost sure” that It’s not what I want, it’s probably the Cochran Q Test. So, any ideas? Thank you for the pacience.

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

Dear R users, I'm using the function coxme of the package coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F,

Hello, I am searching for the classic Levene test of variances in R but I don't find the function. Is there any command or do I have to programm the test by myself? Does anybody know? Help would be fine because R is very new to me. S.Zank [[alternative HTML version deleted]]

Hello All I am new to this list. I have a problem where for a single sample drawn from normal population, null hypothesis is that variance = k (say). Alternative hypothesis is variance > k. Now if we know the true variance, then I would like to calculate the sample size required to produce certain power (for some significance). How do I do this ? I thought of using pwr.chisq.test, and I

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-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

Hi, is there a function in R that will give me the variances of a predicted values obtained using predict.lm(). If no function is available I would need to calculate them myself - which involves taking the inverse of X'X (' indicating transpose) where X is my model matrix. I know that calculating an inverse directly is not a good idea in general - could anybody suggest a way around

I want to use boot.ci to generate confidence intervals over the bootstrapped mean(s) of a group of observations (i.e. I have 10 observations and I want to know how confident I can be on the value for the mean). I don't know (or want to know) the details of bootstrapping - I just have the simplistic idea of taking samples, measuring a statistic on the sample, and getting some confidence in the

Hello, how do I obtain standard errors of variances and covariances of the random effects with LME comparable to those of for example MlWin? I know you shouldn't use them because the distribution of the estimator isn't symmetric blablabla, but I need a measure of the variance of those estimates for pooling my multiple imputation results. Regards, Roel.

I am testing the homogeneity of variances via bartlett.test and fligner.test. Using the following example, how should I interpret the p-value in order to accept or reject the null hypothesis ? set.seed(5) x <- rnorm(20) bartlett.test(x, rep(1:5, each=4)) Bartlett test of homogeneity of variances data: x and rep(1:5, each = 4) Bartlett's K-squared = 1.7709, df = 4, p-value =

Hello and thanks in advance for any help, I am using the 'multinom' function from the nnet package to calculate a multinomial logistic regression. I would like to get a matrix estimates of the estimated coefficient variances and covariances. Am I missing some easy way to extract these? Grant [[alternative HTML version deleted]]

Hello All I am new to this list. I have a problem where for a single sample drawn from normal population, null hypothesis is that variance = k (say). Alternative hypothesis is variance > k. Now if we know the true variance, then I would like to calculate the sample size required to produce certain power (for some significance). How do I do this ? I thought of using pwr.chisq.test, and I

This is a mixed question, between theory and practice. I have a dataset with a continous variable grouped by a 33 levels factor. After having log-tranformed my original data I can assume the normality of my data but I have two strong departures from the basic assumptions for anova and t tests: *unbalanced data* (some groups contain ten samples, others hundreds) and *non homogenity of variances*

I am trying to create basic descriptive statistics for a spatial dataset using moving windows of variable sizes. I found the filter(dataset, rep(1/a,1)) very useful for obtaining averages for a given moving window size "a" (a is the window size within a column of data in a matrix - the filter is applied to all columns automatically) I could not find anything analogous for

Hi all, I have the output of summary() of an lme object called "lme.exp1", for example ############################################# > summary(lme.exp1) Linear mixed-effects model fit by REML Data: DATA Log-restricted-likelihood: -430.8981 Fixed: fixed.exp1 .... Random effects: Formula: ~-1 + mu1 + log.sig1 | animID Structure: Diagonal mu1 log.sig1