similar to: multcomp::glht() doesn't work for an incomplete factorial using aov()?

Hi, I've data from an incomplete fatorial design. One level of a factor doesn't has the levels of the other. When I use lm(), the summary() return NA for that non estimable parameters. Ok, I understant it. But I use contrast::contrast(), gmodels::estimable(), multcomp::glht() and all these fail when model has NA estimates. This is becouse vcov() and coef() has different dimensions. Is

Hello R users, I'm analyzing an experiment in a balanced incomplet block design (BIB). The effect of blocks are assumed to be random, so I'm using nlme::lme for this. I'm analysing another more complex experiments and I notice some diferences from doBy::popMeans() compared multcomp::glht() and contrast::contrast(). In my example, glht() and contrast() were equal I suspect popMeans()

Hello, I'm doing some graphics for a paper and a need customize such with filled region above the density curve. My attempts I get something very near what I need, but I don't solve the problem of use semitransparent filled. Below a minimal reproducible code. Someone has any idea? require(lattice) # toy data... dt <- expand.grid(A=1:2, B=1:3, y=1:50) dt$y <- rnorm(nrow(dt), dt$B,

Colin, If your propose is to create a ternary plot with points and vectors, I think easier do this with graphics based plots instead of trellis based plots. Although, with little work you can do with trellis too. I gave you a reproducible code to put an arrow in a ternary plot. I use the function locator() to extract coordinates. The code is the following

Hello list, I have plots with long strings in main=, ylab= or xlab=. So, in I my script I use break long lines to avoid lines hiden on my monitor and in sweave document pages. I use graphics like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") but I would like a plot result like this plot(1, main="aaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbb") I

Hello list, I'm doing a table with scores and I want include colors to represent status of an individual. I'm using sweave <<results=tex>>= and xtable but I can't get a result I want. My attemps are #----------------------------------------------------------------------------- # code R da <- data.frame(id=letters[1:5], score=1:5*2) col <- function(x){

Hello everyone, I've conducted a Type III repeated-measures ANOVA using Anova() from the car package, based on the suggestions at http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/(option 3) and http://languagescience.umd.edu/wiki/EEG#ERP_ANOVA_in_R. My ANOVA has two factors: Condition (3 levels) and Region (6 levels) and their interaction. Below is code to run the Anova

Windows 2000 . 5.00.2195 with Service Pack 1. R 1.5.1 Output from my split-split plot aov "alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert

Dear R sages, I used the function rbind to combine a matrix (M) and a vector (Fert) to get a new matrix (A). This was fine. The issue is however, that the new matrix A has as its row names the name of the vector Fert, even though I set teh new vector A to have dimnames=NULL. See short code below fyi. *Fert<-c(0,1,5) * *M <- matrix(0, 2, 3) diag(M) <- c(0.3,0.5) * *A<-

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

Hi all, I'm adjusting a nonlinear regression model for data that has a categorigal variable present. So, I can use nls() to do this considering the categorical variable, like this #------------------------------------------------------------ da <- expand.grid(tr=gl(2,1,la=c("tr")), x=1:12) da$y <- 10*da$x/(3+da$x)+rnorm(da$x,0,0.1) plot(y~x, da) n0 <-

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

i ran this model > model2<-glm(rojos~ageandsex+sector+season+sector:season,quasipoisson) > glht(model2,linfct=mcp(ageandsex="Tukey")) General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Linear Hypotheses: Estimate M - H == 0 0.2898 SUB - H == 0 -0.2261 SUB - M == 0 -0.5159 I tried to do the same changing factor season

It's been a few weeks I'm racking my brains on how to use the function glht the package multcomp to test interactions. Unfortunately, the creator of the package forgot to put a sample in pdf package how to do it. I have looked in several places, but found nothing. If someone for the love of God can help me I'll be extremely grateful. The model is glm. -- View this message in context:

Dear R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

Hi R users: I have de following data frame (called "Sx") Descripcion Nitratos Cont85g 72.40 Cont85g 100.50 Cont85g 138.30 Cont80g 178.33 Cont80g 79.01 Cont80g 74.16 Cont75g 23.70 Cont75g 15.80 Cont75g 16.20 Patron80g

I am trying to calculate Poisson regression based adjusted rate ratios in R, but R's default in glm does not code the intercept as the global rate. In SAS I use "cell means" coding so that the intercept is the global rate, but I do not know how to do this in R. If anyone knows a way to make glm use "cell means", or an how to find adjusted rate ratios I would be grateful.

Dear all, I have been working in a plot based on figure 5.6 of the Lattice book (http://lmdvr.r-forge.r-project.org/figures/figures.html). I have already modified it to include the size of the circles as another variable, but I would like to modify the legend to show it (like they do it in http://www.jstatsoft.org/v15/i05/paper). I have divided my variable in intervals: DATA$s_Shape_2 <-

Hi I ran the following sweave file in R2.14.1 and upgraded to R2.15 yesterday with the same setup I got the following error message when I rand the following Sweave file > setwd("D:/Cic/Sweave/Parasite/Comb/12") > Sweave("D:/Cic/Sweave/Parasite/Comb/12/ParasiteComb12.Rnw") Error: c("'ParasiteComb12DS.Rnw'", "'ParasiteComb12.Rnw'")

Hello, I have a question as to how the syntax for glht(package multcomp) and estimable (gmodels) works, since I'm not getting everything from the documents I've googled so far, especially with models with 2nd order terms. A modestly complex model: 2-way anova with one continuous covariate, no random effects(and no repeated measures) to keep it modestly complex: Y = treatmentgroup + sex