similar to: multiple comparisons with non homogeneus variances

As a newer R practicioner, it seems I stump myself weekly (at least) with issues that have spinning my wheels.  Here is yet another... I'm trying to turn a list of numeric vectors (of uneual length) inot a dataframe.  Each vector held in the list represents a row, and there are some rows of unequal length.  I would like NAs as placeholders for "missing" data in the shorter vectors. 

hi, i'm trying to figure out if there's any possibility to write a whole vector into a matrix or data.frame or something like that. i don't mean transormation. Here an example: [,1] [,2] [1,] "a" "d" [2,] "b" "e" [3,] "c" "f" where e.g. a is a<-c(0,1) vector of length 2, b a vector of length 4,... (i know that

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

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 all I would like to show my audience that some variables are homogenous inside groups but different outside. I can use by with summary for all variables by(iris[,1:4], iris$Species, summary) what can be quite messy in case of more than few variables and about 8 groups or densityplot for one variable densityplot(~Petal.Length | Species, iris) I have two questions: 1. Is there

Hi, Does anyone know how to do a coord_trans() in which the y-axis is tranformed into (for example) -1000/y? Thanks, _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road Charlottesville, VA 22903 Office: B011

Hello, R-Help answers questions on R code, your question is about statistics. You should try posting the question to https://stats.stackexchange.com/ Hope this helps, Rui Barradas Em 23-10-2017 18:54, kende jan via R-help escreveu: > Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...). I have

Dear all,?I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...).?I have realised a sqrt(variable) transformation...?The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in

Hello, Is there like the kruskal wallis test in relation to ANOVA (no restrictions on normallity and variance homogenity) something (in R) for MANOVA? thanks -- Dr.Nicolaas Busscher Universit?t GH Kassel Nordbahnhofstrasse: 1a, D-37213 Witzenhausen Phone: 0049-(0)5542-98-1715, Fax: 0049-(0)5542-98-1713

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 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. 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

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.