search for: scheffes

Hi R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

Hello, As a new user of R (less than a month) I have got my hands on several books and am pouting through the net looking for help in gaining understanding of this powerful tool. I am becoming more proficient with using basic functions to conduct basic statistics. I am now looking to learn how to write code. After a few small worthless functions I decided to try to create a function that was

Dear all, Please help me with the R code which compute SCHEFFE TEST Thanking you in advance Kind regards Mangalani Peter Makananisa Statistical Analyst South African Revenue Service (SARS) Segmentation and Research : Data Modelling Tel: +2712 422 7357 Cell: +2782 456 4669 Fax:+2712 422 6579 Please Note: This email and its contents are subject to our email legal notice

Hello everyone I'm trying to use Scheffe's complete cubic model (mixture design). In the bibliographies, they indicate that the term is of the type: A * B * (A-B). But I see that trying to adjust the three cubic terms results in singularities. I know this implies not having the inverse matrix: solve (t (X)% *% X) does not exist. The bibliographies show all three cubic terms. So my

Mangalani Peter Makananisa would like to recall the message, "Scheffe test". Please Note: This email and its contents are subject to our email legal notice which can be viewed at http://www.sars.gov.za/Email_Disclaimer.pdf [[alternative HTML version deleted]]

Dear List, are there post-hoc tests like Scheffe, LSD, etc. available after ANOVA test is performed with significant F-statistic? I have tried help.search("Scheffe"), but "No documentation found" (and I have most of packages installed). Probably there are such tests in R, and I am just searching badly... My second question is: Which test/method I should use for ANOVA-like

Hello, I've fitted a parametric survival model by > survreg(Surv(Week, Cens) ~ C(Treatment, srmod.contr), > data = poll.surv.wo3) where srmod.contr is the following matrix of contrasts: prep auto poll self home [1,] 1 1 1.0000000 0.0 0 [2,] -1 0 0.0000000 0.0 0 [3,] 0 -1 0.0000000 0.0 0 [4,] 0 0 -0.3333333 1.0 0 [5,] 0 0

Dear R People: I have a few questions about multiple comparisons and contrasts for ANOVA, please. I've tried some things but with no success. Suppose I have a completely randomized design, and I want to have the contrast \mu_1 - 0.5 \mu_2 - 0.5 \mu_2 How do I set that up, please? I used the C command, and ran aov, but the results were identical to those with no contrasts. Also, is there

Hi, we are just about to evaluate R as a standard statistics package for our institute. We looked around the help - manual, the FAQ, etc. and did not find anything for the topics of random factors in ANOVA, repeated measures in ANOVA, or within subjects designs in R. Could anyone point us to any information concerning - univariate approach to repeated measures anova - multivariate

Hello all R-users _question 1_ I need to make a statistical model and respective ANOVA table but I get distinct results for the T-test (in summary(lm.object) function) and the F-test (in anova(lm.object) ) shouldn't this two approach give me the same result, i.e to indicate the same significants terms in both tests??????? obs. The system has two restrictions: 1) sum( x_i ) = 1 2) sum(

Buenas tardes   Estoy intentando representar graficamente la elipse de confianza de dos pendiente en un modelo sin intercepto La formula que introduzo esplot(ellipse(recta,c(1,1)),type="l"), pero elipse me sale una recta   Gracias [[alternative HTML version deleted]]

Hello ALL: I am looking for multiple comparison tests for ANOVA models. Does R have any of these? Fisher least significant difference test. Tukey henestly significant difference test. Newman - Keul test. Dunnett's test Scheffe test. Thank you, ANDREW -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Does anyone know of some methods already programmed up ( ie freeware : ) ) in R for the post hoc methods in anova. Particularly Scheffe's method or Tukey's? Anna

Hello, I am interested in parametric multi comparison tests such as Dunnett, Duncan, Tukey, Newman-Keuls, Bonferonni, Scheffe, and non-parametric tests such as Kruskal-Wallis, and Mann-Whitney U. Are there packages that include most of these tests in each category? Many packages exist for an individual test but their outputs vary in great detail (test statistics, p-values, etc.)

All, I have been reading Dr. Harrell's excellent "Regression Modeling Strategies" book and trying out the exercises. I understand that contrast( ) is used to obtain contrasts between two variables for given levels of other nuisance variables; is there a way to use contrast( ) to obtain, for example, Scheffe confidence intervals / hypothesis tests for many post hoc contrasts at

Greetings all, I've done some ANOVAs and have found significant effects across my groups, so now I have to do some post-hoc tests to ascertain which of the groups is driving the significant effect. Usually one would do something like a Newman-Keuls or Scheffe test to get at this but based on googling and browsing through the r-help archives R doesn't support these and they seem to be

Hello everybody, shame on me if I have overlooked something (CRAN, StatLib), but I think I've searched carefully. Maybe it's just too obvious to see for me (happens frequently). Is a function available that extracts multiple comparison of means from objects produced by "aov()" ? Sorry if the term is not correct, I translated from german word by word ("Multiple

This is maybe not directly an R problem but I have used R to try to solve it so I think somebody may be able to help. I have a mixture model with three components and a quadratic Scheffe polynomial p1x1+p2x2+p3x3+p12x1x2+p13x1x3+p23x2x3 fitted to the response. Now I'd like to compute the mixture corresponding the maximum response. Model for Y1 has the parameters p1=124.02 p2=60.973 p3=41.479

To my understanding, a confidence interval typically covers a single valued parameter. In contrast, a confidence band covers an entire line with a band. In regression, it is quite common to construct confidence and prediction bands. I have found that many people are connecting individual confidence/prediction interval values produced with predict(object,sd.fit=T,type="conf/pred") and

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi. I have been using SAS for some time, and now I have discovered R. I am very happy with it, but I have not found out how to perform some of the multiple comparisons I was used to do in SAS. With the SAS/STAT, I generally used the MEANS (for comparison of arithmetic means) and the LSMEANS (for adjusted means) statements of the GLM procedure (I