similar to: Welch versus Satterthwaith (PR#2111)

>>>>> "TL" == Thomas Lumley <tlumley@u.washington.edu> >>>>> on Sun, 6 Oct 2002 09:19:27 -0700 (PDT) writes: TL> On Sat, 5 Oct 2002 roxburg@kih.net wrote: >> This is not a bug report but didn't see another way to >> ask a question. TL> Well, you could try the r-help or r-devel mailing lists >> For

Dear R experts, I was wondering if there are any R functions that give the tail area of a sum of chisquare distributions of the type: a_1 X_1 + a_2 X_2 where a_1 and a_2 are constants and X_1 and X_2 are independent chi-square variables with different degrees of freedom. Thanks, Klaus -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ...

I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version

Dear All: Significance Analysis of Microarrays(SAM) As we know sam do multiple t.test as following ## Default S3 method: t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE'

Hi all. I'm just writing to draw your attention to this paper, which is Open Access: Curtis, D. Welch?s t test is more sensitive to real world violations of distributional assumptions than student?s t test but logistic regression is more robust than either. Stat Papers (2024). https://doi.org/10.1007/s00362-024-01531-7

Hello to R uers, I am wondering if there is an easy way to perform a cross-power spectral density estimation of ?two timeseries (x and y) using the Welch's method. Both packages "bspec" and "oce" provide a function to calculate the PSD with the Welch's method, but only for a timeserie. Thank you in advance. Regards, Pascal

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for

Greetings, I've tried to update the power.t.test function to allow for different sample sizes and sample variances in the case of a two-sample t test. I'd gladly update the corresponding help page if the code is to replace the current power.t.test function. The modified power.t.test code is included below. The changes are as follows: - Added three new arguments to the function *

Dear all, I'm performing a t-test on two normal distributions with identical mean & standard deviation, and repeating this tests a very large number of times to describe an representative p value distribution in a null case. As a part of this, the program bins these values in 10 evenly distributed bins between 0 and 1 and reports the number of observations in each bin. What I have noticed

Hi, Typical things you read when new to stats are cautions about using a t-statistic when comparing independent samples. You are steered toward a pooled test or welch's approximation of the degrees of freedom in order to make the distribution a t-distribution. However, most texts give no information why you have to do this. So I thought I try a little experiment which is outlined here.

Anyone know if coin can run a permutation test based on a (user-defined) statistic other than the mean difference? The function independence_test does the permutation t-test via difference in means. I'm wondering if it's possible to use independence_test to run a permutation test for some other statistic than the difference in means. For example, I'd like to run a permutation test

Hi Ted, yes this was the problem. Thank you very much. best idaios On Wed, Jan 9, 2013 at 4:51 PM, Ted Harding <Ted.Harding@wlandres.net>wrote: > Ah! You have aqssigned a parameter "equal.var=TRUE", and "equal.var" > is not a listed paramater for t.test() -- see ?t.test : > > t.test(x, y = NULL, > alternative = c("two.sided",

dear r community... it loks like i won't be able to reach homogenity of variance for my dataset, so i end up with welch anova instead of regular anova. documentation on this test is rather scarce, so maybe someone here can enlighten me a bit: - do i understand that no two-way implementation of the welch anova has been developed yet? - is there a post-hoc test for welch anovas implemented in

Does oneway.test do what you want? Hope this helps, Matt Wiener -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Sven Hartenstein Sent: Monday, June 21, 2004 3:23 PM To: r-help at stat.math.ethz.ch Subject: [R] Welch-JM-Test or Brown-Forsythe-Test in R? Hi, I want to test mean differences of > 2 groups with

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Hello Using R 2.2.1 on a Windows machine. Has anyone programmed the Welch test for equality of variances? I tried RSiteSearch, but this gave references to t test and oneway.test, which are not quite what I need.....I need the Welch test itself, for use in a meta-analysis (to determine if variances are equal). TIA Peter Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis

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Hello, I have tested a distribution for normality using the Shapiro-Welch statistic. The result of this is the following: Shapiro-Wilk normality test data: mydata W = 0.9989, p-value = 0.8791 I know that the p-value > 0.05 (for my purposes) means that the data IS normally distributed but what I am not sure is with the W value, what values tell me that the data is normally