similar to: Non parametric, repeated-measures, factorial ANOVA

Alguien conoce este un test no paramétrico que se llama de Brunner-Langer model para dependent data, también conocido como modelo longitudinal? Intuyo que es como un Friedman pero que admite algún efecto fijo. Graccie mile. -- Antonio Maurandi López Universidad de Murcia Edif. CAID. Campus de Espinardo. 30100 Murcia @. amaurandi en um.es T. 868 88 7315 F. 868 88 7302 www.um.es/sai

Dear All, I'm new to the list and relatively new to R (but not so to S-Plus) and I?ve been asked to help study some experimental data. In a factorial experiment (5 factors at 2 levels) the response-variable was measured at various times (0, 2, 5, 7, ..., 30 days after start). There's just one run for each factor combination! I intend to use the nlme library of Pinheiro and Bates in a

Dear all, I am trying to analyze data from an experiment like this: Factors: Hormone - Levels: SH, CH (S = without; C=with; H=Hormone) Time - Levels: 19/08/09, 04/09/09, 18/09/09, 08/10/09, 20/10/09 (DD/MM/YY) Nutrition - Levels: Completa, Sem (without) Macronutrition - Levels: Ca, K, Mg, P, Sem (without) Time is the measures day. It reflect the days after germination. Blocks : 4 plants per

Dear all, I studied in tank prey fish behavior. Using the design described below (and R code), I want to test the effects of both habitat and predator (and interaction) on prey fish's vertical distribution, which was recorded (with repeated measures) as a categorical variable. I found that package mlogit might fit to my need but I don't know how to specify my complex design in the

Sorry for the stupid question, but is there the factorial function in R? I tried to find it using help.search('factorial') but got nothing appropriate. Many thanks, -Serge

Thanks to Martin, Marc, Dennis, Rashid and Bill for comment. The short answer is there is no factorial function within the base package, so use the result that gamm(x + 1) = x!. This what the factorial() function in the package gregmisc does: factorial <- function (x) gamma(1 + x) Bill V. also writes: "Your best move is usually to use the lgamma() function (for log gamma) and do

Hi, I tried to calculate the formula for the birthday problem (the probability that at least two people out of a group of n people share the same birthday) But the factorial-function allows me only to calculate factorials up to 170. So is there a way to push that limit? to solve this formula: (factorial(365) / factorial((365-23))) / (365^23) (n=23)

The following R commands were written: >help.search("factorial") >data(npk) >npk >coef(npk.aov) In the output of coef command, please explain me the interpretation of coefficients of block1 to block 6 in this 2^3 confounded factorial experiment. Thanks. [[alternative HTML version deleted]]

Hi everyone, In the package POT, there is a function that computes the L-moments of a given sample (samlmu). However, to compute those L-moments, one needs to obtain the total number of combinations between two numbers, which, by the way, requires the use of a factorial. See, for example, Hosking (1990 , p. 113). How does the function "samlmu" in the package POT avoids overflows? I

Hi Everyone, I found the doe.base package and the FrF2 package to do nice experimental planning and I'm very happy about this tool I was looking for such a long time. But I still try to find out how to add center points to a full factorial design. The FrF2-package has a center point option but is just supporting fractional designs? Is there a possibility to have center points within the

hi, i did a 2^4 factorial experiment, and i got the following result: Effect t p (computer) p -484.52494328125 -5.64590926071629 <0.0001 d -450.67095078125 -5.25142684568607 <0.0001 pd 438.80508046875 5.11316022388923 0.0001 t 351.87216640625 4.1001776078805 0.0008 pt -341.05023984375 -3.97407550262202 0.0011 dt -311.27784109375

Is there a native factorial function in R [my searches have been fruitless], or do I need to use gamma() function? ********************************************************************* Dr Peter Caley CSIRO Entomology GPO Box 1700, Canberra, ACT 2601 Email: peter.caley@csiro.au Ph: +61 (0)2 6246 4076 Fax: +61 (0)2 6246 4000 *********************************************************************

Dear R Gurus: How do you put together a 2^2 (or even 2^k) factorial problem, please? Since you have 2 levels for A and B, do you put in "A+" and "A-" as factors, please? Thanks, Edna Bell

Dear R friends, I was wondering if there were factorial and modulus functions out there that I've somehow overlooked? -P. -- Peter L. Hurd, Ph.D. phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd fax 512.471-3878 Section of Integrative Biology, University of Texas, Austin TX 78712 USA

Hello all, I´m trying to study a factorial design, but I can´t understand why did Df, Sum Sq and Mean Sq of residuals alter when I Split the interaction? I think that Split the interaction must not alter the residuals. Am I doing something wrong? Could anyone help me? My data and functions I tried are: Y<-c(196,213,183, 192,253,199, 251,331,276,

Dear R Gurus: I vaguely remember reading that if interaction was present in a factorial design, then the main effect results were suspect. However, I was reading a text which now uses the tests for main effects even if interaction is present. Which is correct, please? Thanks, Edna Bell

Hello I run a factorial analysis on 20 variables describing the behaviour of insects on 9 different resistant plants. I then biologically interpreted each of the 5 factors obtained, with respect to the variables with the highest loading value for each factor. And I run a GLM on each factor with plant as independent variable. Eventually, I inferred the resistance mechanism by comparing (through

Hello! I have read somewhere (somehow, I can't seem to find it again, it's been a couple of months) that when analyzing factorial block design, the position where you put the block factor is important, even more when there are missing values. I understand that when using anova.lm, the order is sequential, so that if I want to check for a treatment effect, I should put my blocking factor

Hello all, I'm attempting to plot the functions from a generalized linear model while iterating over multiple levels of a factor in the model. In other words, I have a data set Block, Treatment.Level, Response.Level So, the glm and code to plot should be logit.reg<-glm(formula = Response.Level ~ Treatment.Level + Block, family=quasibinomial(link="logit"))) plot(

Hello dear R-help mailing list, I wish to perform a non-parametric repeated measures anova. If what I read online is true, this could be achieved using a mixed Ordinal Regression model (a.k.a: Proportional Odds Model). I found two packages that seems relevant, but couldn't find any vignette on the subject: http://cran.r-project.org/web/packages/repolr/