search for: csimtest

Hello all, Quick question: I want to do posthoc contrasts for a linear mixed effects model. However, when trying to use the csimtest function in multcomp package I receive an error message saying it cannot find the function, even after installing and loading package multcomp. Any pointers would be greatly appreciated Daniel

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...

...cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels: high-use road, low-use road, high-use trail, low-use trail, and railway line I would like to determine whether wolves are more likely to cross some features than others and am using csimtest in the package multcomp to do so. x <- coef(model) var.cov <- vcov(model) df <- model$df.residual contrast.matrix <- contrMat(rep(2,length(x), type = "Tukey") post.hoc <- csimtest(estpar = x, df=df, covm = var.cov, cmatrix = contrast.matrix) summary(post.hoc) My questions...

...))) # a binary response z <- factor(rbinom(30, 1, 0.5)) # estimate the model gmod <- glm( z ~ group, family=binomial(link = "logit")) summary(gmod) # exclude the intercept # Should be the following, but does not work due to a confirmed # bug in the CRAN-binary version 5.10 #summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3], # cmatrix=diag(2), df=27,asympt=T)) summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3], cmatrix=diag(2), df=27)) ------- This works and can be extended to to lme, but only gives TWO of the three possible Tukey contrasts. Set...

...800 s 1.0 75 Al800 s 0.8 76 Al800 s 0.8 77 Al800 s 0.7 > attach(tab) > library(nlme) > lm1<-lme(response~treatment,random=~1|box) > library(multcomp) Loading required package: mvtnorm > # first way to do (seem uncorrect) > summary(csimtest(coef(lm1),vcov(lm1),cmatrix=contrMat(table(treatment), type="Tukey"),df=59)) Error in csimtest(coef(lm1), vcov(lm1), cmatrix = contrMat(table(treatment), : estpar not a vector > #indeed > coef(lm1) (Intercept) treatmentAl200 treatmentAl400 treatmentAl600 treatmentAl800 a 1...

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

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 | : :' :

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

...erent for each sample (8 - 34). To extract, I've used: >drop1(dat.glm, .~., test="Chisq") However, how do I do a post-hoc multiple comparison to see which treatment(s) and interaction(s) are giving significant effects? I have found the "multcomp package" with the "csimtest" but it requires the "parameter estimates", how do I get these for all my "treatments" and "sites? Yours sincerely Micke ************************************************************************ ****** Mikael Niva Dept. of Plant Ecology EBC, Uppsala universitet -....

...(2, 10), rep(3,10))) # Williams contrasts contrasts(group) <- mginv(contrMat(table(group), type="Will")) # a binary response z <- factor(rbinom(30, 1, 0.5)) # estimate the model gmod <- glm( z ~ group, family=binomial(link = "logit")) # exclude the intercept summary(csimtest(coef(gmod)[2:3], vcov(gmod)[2:3,2:3], cmatrix=diag(2), df=27, asympt=TRUE)) > Thank you very much. > > Peter B. _______________________________________________________________________ | | | Dipl.-Stat. Torsten Hothorn | | Institut fuer Medizininformatik,...

...ervals 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 improvements are...

...ervals 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 improvements are...

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