search for: simtest

...gyPrice-strategyConvenience 0.301 0.904 0.553 strategyQuality-strategyConvenience 0.014 0.043 0.037 strategyQuality-strategyPrice 0.143 0.428 0.305 This gives correct 95% confidence intervals and adjusted p-values for the Tukey multiple comparisons procedure. Next we issued summary(simtest(sales ~ strategy, type="Tukey", data=Xm15.01s)) which produced the output Coefficients: Estimate t value Std.Err. p raw p Bonf strategyQuality-strategyConvenience 75.45 -2.530 29.824 0.014 0.043 strategyQuality-strategyPrice 44.35 -1....

Hi! I use simtest fonction of multcomp package to compile a Dunnett's test. I have 10 treatments and one control group, so i create a matrix with: m<-matrix(0,10,11) m[1,1]<--1 m[1,2]<-1 m[2,1]<--1 m[2,3]<-1 m[3,1]<--1 m[3,4]<-1 m[4,1]<--1 m[4,5]<-1 m[5,1]<--1 m[5,6]<-1 m[6,1]&l...

...es. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use simtest to see the pairs significantly different... but the simtest doesn't work for 2 responses, so we look for a multivariate simtest. Thanks for your advice! So our data: #read the data file >tous_y <- read.table("moyenne.txt", header=TRUE, sep="\t", na.strings=&qu...

Hello all I am working with the package multcomp but I have problems with the function simtest; the program say that can not find this function, nevertheless I doesn't have any problem with the function glht that it is in the same package. Someone knows what could be the problem? Thank you [[alternative HTML version deleted]]

...e of MAP, ITBS, and CogAT data <- data.frame(map, itbs, cogat) #Draw from the multivariate distribution defined by MAP, ITBS, and CogAT means and the covariance matrix sim <- mvrnorm(1000, mu=mean(data), sigma, empirical=FALSE) #Set growth at 0 growth=0 #Combine elements into a single dataset simtest=data.frame (subject=subject, treat=treat,lunch, age=round(age,0),round(sim,0),growth) #Set mean growth by treatment condition with treatd subjects having a mean growth of 1.5 and non-treated having a mean growth of 0.1 simtest<-transform(simtest, growth=rnorm(1000,m=ifelse(treat==0,0.1,1.5),s=1)...

Hi, I've got a problem building a contrast matrix for the Dunnet contrast in package multcopm. The following works fine: > summary(simtest(adiff ~ trial)) Simultaneous tests: Dunnett contrasts Data: adiff by trial Contrast matrix: trial1 trial2 trial3 trial4 trial5 trial2-trial1 -1 1 0 0 0 trial3-trial1 -1 0 1 0 0 trial4-trial1 -1 0 0 1 0 trial...

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the function pmvt ... but, the function mvt returned an error. ) -- example 1 ------------------------------- require(multcomp) Loading required package: multcomp Loading required package: mvt...

...all pairwise comparisons with : mc<- simint(ren~ID,type="Tukey",data=donnees) I cannot get a result in a reasonable time (after 2 hours the computer was still working and I interrupted the process). Can anybody tell me if there is some capacity limitation for simint (as well as for simtest)? Multicomp of Splus handles the problem adequately in a short time, but Splus is practically not accessible for student training... Kind regards, Patrick Giraudoux [[alternative HTML version deleted]]

Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2 1017.8 508.9 4.7548 0.0135041 * Q1 1 1652.7 16...

I'm new to R, but I'm trying to write a program for a dissertation that generates a dataset as follows... subject=1:1000 treat=rbinom(1*1000,1,.13) gender=rbinom(1*1000,1,.5) eth=runif(1*1000, min=1, max=4) cogat=rnorm(1*1000, 100, 16) map=rnorm(1*1000, 200, 9) simtest=data.frame (subject=subject, treat=treat, gender=gender, eth=round(eth,digits=0), cogat=round(cogat,digits=0),map=round(map,digits=0)) simtest I need to add a variable named "growth". If the "treat" variable for an observation is 0 then "growth" needs to be a randoml...

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

...bit along my way of exploring multivariate analysis in R. I want to test wether the means-vector mu1 of X, consisting of the means per column of that matrix , and mu2, i.e. the means per column of Y, are distributed equally under the assumption of a multivariate normal distribution. I thought ?simtest? could be the right function to get the p-value, but I fail to use R correctly. Here is what I tried to do: f1 <- factor(c("8", "10", "12", "14")) X <- matrix(1:16, ncol=4) colnames(X) <- c("Obj1","Obj2","Obj3",&quot...

...I show three attempts that failed. Any help would be much appreciated. > pairwise.t.test(q12a, quota, p.adj = "fdr") $method [1] "t tests with pooled SD" $data.name [1] "q12a and quota" $p.value 1 2 3 4 5 6 7 8 9 2 4.805732e-09 NA NA NA NA NA NA NA NA > simtest(q12a ~ quota) Error in parseformula(formula, data, subset, na.action, whichf, ...) : at least one factor required > simtest(q12a ~ factor(quota)) Error in parse(file, n, text, prompt) : parse error > simtest(q12a ~ factor(quota),na.action=na.exclude) Error in parse(file, n, text, pro...

I've never seen anything written about multiple comparisons, as in the multcomp package or with TukeyHSD, but using a glm. Do such procedures exist? Are they sensible? Are there any packages in R that implement such comparisons? Thank you. -- Ken Knoblauch Inserm U371 Cerveau et Vision 18 avenue du Doyen Lepine 69675 Bron cedex France Tel: +33 (0)4 72 91 34 77 Fax: +33 (0)4 72 91 34 61

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it possible to correctly calculate Tukey contrasts for one factor in a lme model with multiple factors using csimtest or another function in R? If so, how can I do this? Many thanks, René Es...

...es only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function and it gives me only negative t.stat, resulting in a weard distribution... thank you in advance for all the attention you 'll give to my problems. Julie BERTRAND

Hi, I use glht to make multcomp, using Tukey, from a glm model. It is possible to get a more simplified output of result? Somethink like ordering by letters. Thanks Ronaldo -- Human kind cannot bear very much reality. -- T. S. Eliot, "Four Quartets: Burnt Norton" -- > Prof. Ronaldo Reis J?nior | .''`. UNIMONTES/Depto. Biologia Geral/Lab. de Ecologia | : :' :

...r1 vs (r2,r3)/e2 C4 = c(0, 0, 0, 1, 0, -1) # r2 vs r3/e2 C = rbind(C1, C2, C3, C4) row.names(C) = c('r1 vs (r2,r3)/e1', 'r2 vs r3/e1', 'r1 vs (r2,r3)/e2', 'r2 vs r3/e2') lim1 = lm(y ~ aux, data = df) print(anova(lim1)) tc1 = simtest(y ~ aux, data = df, conf.level = 0.9, alternative = 'less', eps = 1e-04, cmatrix = C) print(summary(tc1)) #===Below: verifying E effect in R levels (already analized in av2) # (with auxiliary variable - aux) # a: e1/r1 # c: e1/r2 # e: e1/r3 # b: e2/r1 # d: e2/r2 #...

...isn't corrected. Covariance analysis seems to represent the best way to do this. But how to perform this by using R ? Actually, we have identify some R functions of interest regarding this matter (lme(), lm() and glm()). For example we plan to do as describe : glm(response~baseline) and then simtest(response_corrected~group, type="Dunnett", ttype="logical") If a mixed model seems to better fit our experiment, we have some problems on using the lme function : lme(response~baseline) returns an error ("Invalid formula for groups"). So : Are fitted values represent t...

...ral linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example Tukey all-pair comparisons) are contained in the framework as special cases. The old user interface (functions `simint()' and `simtest()') are still available, however deprecated, and the presentation of their output differs from older versions of `multcomp'. As always, feedback, feature requests and bug reports are more than welcome. Best wishes, Torsten ______________________________________________________________...