similar to: simint or TukeyHSD for lme?

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

Dear R-developeRs, Attached follows a patch against svn 34959 that adds the printing of p-values to the TukeyHSD.aov function in stats package. I also updated the corresponding documentation file and added a 'see also' reference to the simint function of the multcomp package. As it was already brought up in a previous thread [1] in R-help, one can obtain the adjusted

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

Dear R-supporters, I have results of lme similar to those shown in Fig. 1.21, p.51 of Pinheiro/Bates. However, In my data set, one of the panels is missing, leading to an ugly frame shift of the following panels. How can I replace one of the panels by an empty one to restore the raster? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

I have problem getting the concept of a nested fixed variable into the nlme scheme. I fear the question is very stupid. In the past I had asked this before, and never got a reply (in other cases, the response was within hours). I also checked the S-list, where several similar enquiries of other people are orphaned. We have a cross-over design, where patient are treated two weeks with placebo,

Hi all, I search the archive for finding a simple solution for using TukeyHSD with a multistratum aov result (a repeated emasure anova). The Question have been asked but I've found no clear answer. res<-aov(y~Fa*Fb+Error(Subject/(Fa*Fb)) ) I think that the problem is that res is an aovlist object instead of the "aov" object required by TukeyHSD. Is there an easy solution to

Hi, the function TukeyHSD gives incorrect results for balanced incomplete block designs, as the example below shows, but I can only half fix it. There are two problems, 1. It uses model.tables to estimate treatment means, 2. It uses the wrong standard error The first problem can be fixed using dummy.coef, if the lines > TukeyHSD.aov function (x, which = seq(along = tabs), ordered = FALSE,

Hello, I am trying to determine the most appropriate way to run post-hoc comparisons on my lme model. I had originally planned to use Tukey HSD method as I am interested in all possible comparisons between my treatment levels. TukeyHSD, however, does not work with lme. The only other code that I was able to find, and which also seems to be widely used, is glht specified with Tukey:

Hi all, I've done some anovas, found some significance and proceeded to do the post hoc tests to determine the source of the significance. I used the multcmp package as suggested by Andy Liaw (thank you). I am, however, unsure how to interpret the results of the call to simint. Could someone help me with this? I've attached the code and the results it produced. There are 3 groups

Hi Dieter, No, because I'm looking for the CIs on the means of baLO of an additive model which has 20 cells, exactly as stated. Essentially I want to have the values of 'lower' and 'upper' to plug into xYplot when used in the form xYplot(Cbind(baLO,lower,upper) ~ bar | sub, groups = delta, data=e7). Thanks. On Dec 12, 2005, at 4:53 AM, Dieter Menne wrote: >

I can prove I've done this before, but I recently installed Rexcel (and it was easiest to reinstall R and some other bits to make it work) and now its no longer working. Before I would do an ANOVA and a tukey post-hoc like this: >data1.aov=aov(result~factor1*factor2, data=data1) then... >TukeyHSD(summary(data1.aov)) and it would give me a nice tukey table of all the pairwise

Using formulas constructed from strings only partially works for me in lme: library(nlme) data(Orthodont) fm2<-lme(as.formula("distance~age"),data=Orthodont,random=~1|Subject) summary(fm2) # works augPred(fm2) # fails #Error in inherits(object, "formula") : #Argument "object" is missing, with no default I assume that my use of as.formula is wrong, but

Hi Everyone, I’m trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each

New on CRAN: phpSerialize Version 0.8 Dieter Menne, dieter.menne at menne-biomed.de Serializes R objects for PHP import into an associative array. Main use is for building web pages with R-support. Has mainly been tested with lm,lme, nlme and their summaries. A web example is provide, showing -- How to start R from php/Apache -- How to pass variables from php to R via Environment -- How to

New on CRAN: phpSerialize Version 0.8 Dieter Menne, dieter.menne at menne-biomed.de Serializes R objects for PHP import into an associative array. Main use is for building web pages with R-support. Has mainly been tested with lm,lme, nlme and their summaries. A web example is provide, showing -- How to start R from php/Apache -- How to pass variables from php to R via Environment -- How to

Why do I see this error? > library(stats) > require(stats) [1] TRUE > > tHSD <- TukeyHSD(aov) Error in TukeyHSD(aov) : no applicable method for "TukeyHSD" In case it helps: > aov Call: aov(formula = roi ~ (Cue * Hemisphere) + Error(Subject/(Cue * Hemisphere)), data = roiDataframe) Grand Mean: 8.195069 Stratum 1: Subject Terms: Residuals Sum

I have a question about TukeyHSD and the glht function because I'm getting different answers when a covariate is included in model for ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for repeatability.? If I include HeadWt as a covariate, then I get different answers when performing multiple comparisons using TukeyHSD and the glht function. The difference appears

Is there an easy way to remove data frame columns by name instead of by index? The following gives the idea remove<-c("subj","drug") data[-remove] I found a solution with a few evals and substitutes, similar to that used in reshapeLong, but there must be an easier way out. Dieter --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Hi, I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous covariate (e.g., AGE). I did for example: library(multcomp) data('litter', package = 'multcomp') litter.aov <- aov(weight ~ gesttime + dose, data = litter) TukeyHSD(litter.aov, which =

Dear R-Helpers, I was calling the TukeyHSD function and not getting confidence intervals or p-values. It turns out this was caused by missing data and the fact that I had previously turned on R Commander (Rcmdr). John Fox knew that Rcmdr sets na.action to na.exclude, which causes the problem. If you have this problem, you can either exit Rcmdr before calling TukeyHSD or you can set na.action to