search for: csimint

...a coefficients of a # 2x2 factorial design (see Piantadosi 2005, p. 509) cm1 <- matrix(c(0, 1, 0, 0, 0, 1), nrow = 2, ncol=3, byrow=TRUE, dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3"))) # cm1 = contrast matrix for main effects v1 <- csimint(estpar=c(100, 6, 5), df=4*n-3, covm=cov1*sigma^2/n, cmatrix=cm1, conf.level=0.95) summary(v1) The adjusted p-values are almost the Bonferroni p-values. If I understood right: You need not to adjust for multiple testing on main effects in a 2x2 factorial design assuming the absence of interaction...

...ta=df, random= ~ 1|id) summary(m1) par4 <- m1$coef$fixed cov4 <- vcov(m1) cm4 <- matrix(c(0, 0, 1, 0, 0, 0, 1, 1), nrow = 2, ncol=4, byrow=TRUE, dimnames = list(c("diff/v=0", "diff/v=1"), c("C.1", "C.2", "C.3", "C.4"))) v4 <- csimint(estpar=par4, df=n-6, # I'm not sure whether I found # the correct degrees of freedom covm=cov4, cmatrix=cm4, conf.level=0.95) sv4 <- summary(v4) # 2. Method: I found in Handbook of Statistics Vol 13, p.616, # same can be found in http://home.clara.net/sisa/bonhlp.htm # Bonferroni on...

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 | : :' :

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

I suppose that multcomp in R and multicomp in S-Plus are related and it appears that it is possible to use multicomp with lme in S-Plus given the following correspondence on s-news sally.rodriguez at philips.com 12:57 p.m. 24/04/03 -0400 7 [S] LME summary and multicomp.default() Is it possible to use multicomp with lme in R and if so what is the syntax from a simple readily available

I just realized that in the call to `csimint' the argument `asympt=TRUE' is missing since we need to compute the confidence intervals for a glm based on the normal approximation. Torsten --------------------------------------------------------------------- library(multcomp) set.seed(290875) # a factor at three levels group <-...

Dear Madam or Sir, I need to do multiple comparisons following nlme analysis (Compare the effects of different treatments on a response measured repeatedly over time; fixed = response ~ treat*time). On the web I found the notion that one might use the L argument from ANOVA. Do you have an example to show how this works together with nlme? Are there other ways to do a post-hoc analysis in

...es confidence intervals for the common single step procedures (`simint'). This approach can be uniformly improved by applying the closed testing principle, what is implemented in the second function (`simtest'; but no confidence intervals are available for the latter procedure). Use either `csimint' or `csimtest' if you want to pass the estimates by hand. For testing and validation purposes we included some examples from Westfall et al. (1999). This is a first release and needs some improvement, therefore the user interfaces may change in forthcoming versions. Suggestions for...

...es confidence intervals for the common single step procedures (`simint'). This approach can be uniformly improved by applying the closed testing principle, what is implemented in the second function (`simtest'; but no confidence intervals are available for the latter procedure). Use either `csimint' or `csimtest' if you want to pass the estimates by hand. For testing and validation purposes we included some examples from Westfall et al. (1999). This is a first release and needs some improvement, therefore the user interfaces may change in forthcoming versions. Suggestions for...

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it