similar to: multiple comparisons and generalized least squares

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

Dear R-group, I am trying desperately to get this Tukey test working. Its my first time here so I hope my question is not too stupid, but I couldn't find anything helpful in the help or in the forum. I am analysing a dataset of treated grasses. I want to find out, if the grasses from different Provenances react differently. In the aov test I found a significance for the combination Treatment

I'm trying to do an ANCOVA with two factors (clipping treatment with two levels, and plot with 4 levels) and a covariate (stem diameter). The response variable is fruit number. The minimal adequate model looks like this: model3<-lm(fruit~clip + plot + st.dia + clip:plot) I'd like to get some multiple comparisons like the ones from TukeyHSD, but TukeyHSD doesn't work with the

I've got a gls formula that's a mix of continuous and ordered variables. I wanted to use gls because I wanted to use the varIdent structure. Anyway, attempts to use "predict.gls" choke with the error that the levels I use are not allowed for one of them -- the first one alphabetically, so I'd guess the second would have the same problem. So I have three linked questions --

Hi, I am working with R version 2.1.0, and I seem to have run into what looks like a bug. I get the same error message when I run R on Windows as well as when I run it on Linux. When I call anova to do a LR test from inside a function, I get an error. The same call works outside of a function. It appears to not find the right environment when called from inside a function. I have provided

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:

I have a bayesian hierarchical normal regression model, in which the regression coefficients are nested, which I've wrapped into one regression framework, y = X %*% beta + e . I would like to run data through the model in a filter style (kalman filterish), updating regression coefficients at each step new data can be gathered. After the first filter step, I will need to be able to feed

Dear All, I am using the function gls (in the nlme package) and I would like to fit a heteroscedastic model, with different variances for each of the levels of two stratification variables. In p. 210 of Pinheiro & Bates ("Mixed effects models in S and S-Plus", 2000, Springer), the authors show the use of the "*" operator. However, that is not what I want, because it

Dear R-Users, I am currently working with LME to analyse repeated measures data. I encounter a problem when including both a random effect and a correlation structure with different grouping levels into the LME model. The error message is: Error in lme.formula(diameter ~ flowers*timef + competition*timef + population*timef, : Incompatible formulas for groups in "random" and

Hi All, I have used glm to model my data, I have two factors and a covariate as described in the example code below (mod.1). I have been able to "force" glht to perform multiple comparisons by creating a combined variable for the factors, accepting that there will be a loss of statistical power as it seems to do what I want. I then use the cld function to generate the letters of

Hi, I have data with binomial response variable (survival) and 2 categorical independent variables (site and treatment) (see below).? I have run a binomial GLM and found that both IVs and the interaction are significant.? Now I want to do a post-hoc test for all pairwise comparisons to see which treatment groups differ.? I tried the glht function in the multcomp package, but I get surprising

Hi, I'm trying to make sense of the options for multiple comparisons options in R. I've found the following options: pairwise.t.test, which provides standard t-tests, with options for choosing an appropriate correction for multiple comparisons TukeyHSD, which provides the usual Tukey test glht(package multcomp), which provides a variety of options >From the help list, it appears

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

Hi all, I understand that gls() uses generalized least squares, but I thought that maybe optimum weights from gls might be used as weights in lm (as shown below), but apparently this is not the case. See: library(nlme) f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights = varIdent(form = ~ 1 | Species)) aa <- attributes(summary(f1)$modelStruct$varStruct)$weights f2 <-

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Hello, I have data on the maturity of two morphs of fish. I want to test whether their maturity is evolving differently or not on a temporal scale (month). The maturity variable (independent variable) is continuous and the morph and month variables (dependant variables) are categorical. Because the data show variance heterogeneity, I modeled it with the function gls: kg1 =

There is a bug in 'anova.gls' in the 'nlme' package (3.1-90). The=20 bug is triggered by calling the function with a single 'gls' object=20 and specifying the 'Terms' argument but not the 'L' argument: > library(nlme) > fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont, + correlation =3D corSymm(form =3D ~ 1 |

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Dear all, I've been reading for many days trying to predict with lmer but I haven't managed to do it. I've fitted an allometric model for trees where I have included climatic variables and diameter in the fixed part and in the random part I've included the experimental sites where trees are and also their provenance region. The model is like this :

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to