similar to: Welch versus Satterthwaith (PR#2111)

This is not a bug report but didn't see another way to ask a question. For the approximate t-test assuming unequal variances, the R docs cite Welch's method for the df of the approximating distribution. I have several methods books, and they all uses Satterthwaite's method. Why does R use Welch's method where can I learn about Welch's method? Sincerely, David Allen

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 *

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

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 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'

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 ...

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

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

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

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

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

I know when I enter this into R: > x = c(15, 10, 13, 7, 9, 8, 21, 9, 14, 8) > y = c(15, 14, 12, 8, 14, 7, 16, 10, 15, 12) > t.test(x,y,alt="less",var.equal=TRUE) it shows: Two Sample t-test data: x and y t = -0.5331, df = 18, p-value = 0.3002 alternative hypothesis: true difference in means is less than 0 95 percent confidence interval: -Inf 2.027436 sample

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

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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 Please I need to add "Maurizio" to this MSSQL DB, but I don't now how to add this step in my opinion I'think that the user can't be found, so I will have the "unknown user" Can give here any little help to fix this? Feb 8 12:09:56 caloro dovecot: auth: Debug: auth client connected (pid=13300) Feb 8 12:09:56 caloro dovecot: auth: Debug: client

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

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

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.

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply