similar to: Setting base level for contrasts with lme

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

Hi I am running some linear and non-linear mixed effect models and would like to do some planned contrasts (a priori contrasts) I have looked in the help and in many forums and it seems possible to do so but don't understand how to write the function and I couldn't find an example in Pinheiro and Bates. lme {nlme} has a contrasts argument but I can't understand how to code it.

Dear all, To draw a lowess line on a plot was a piece of cake; to draw a loess line, however, seems not that easy. Is the loess plotting implemented at all in relation to the loess function, or do I have to look in add-on packages? Thanks, Marcin

Hi All, I've been searching the help archives but haven't found a workable solution to this problem. I'm running an lme model with the following call: >lme.fnl <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID) > anova(lme.fnl) numDF denDF F-value p-value (Intercept) 1 168 19255.389 <.0001 S 1 168 5.912 0.0161 Tr

Hi, Consider this simple plot: > plot(1:25,runif(25,0,1),ylab="First Y-axis label",xaxt="n") I want to add an additional axis as > axis(4,at=seq(0.2,1,.2), labels=1:5) I have no idea how to add now the title of the new axis as "Second Y-axis label". I want this text to be vertically directed from bottom to top. I can't find the function in text() to write

Hello all! R2.0.1, W2k I posted this question to the list last Sunday without getting any replies on the list. I got two off the list though, suggesting me to plot "manually" as a second step, from estimable() or intervals() objects respectively. As this was not really what I wished for, I take the risk to upset somebody with a trivial question, and re-post it (just a little

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Hi, I want to add boxplots to a scatterplot: plot(x,y, xlim=c(80,120),ylim=c(80,120)) boxplot(y,add=TRUE,at=118) boxplot(x,add=TRUE,at=118,horizontal=TRUE) How can I control the width of the boxes (say, I'd like them to be of width 3 in the variables' scales). I've tried the "width" parameter but failed. Thanks in advance, Marcin

Dear R help, In limma package, contrasts.fit() function is very useful. I am wondering whether there is a similar function for lme object, which means given a mixed linear model fit, compute estimated coefficients and standard errors for a given set of contrasts. Thanks, Shirley

Dear All This is probably a very basic question for this list but I just wanted to make sure that I am doing things right: I have an LME model with 4 categorical variables and 2 continuous variables (analysis of covariance model). I had to use a log transformation on the data to achieve normality (log(x)-.1) and then I used contrast treatment to compare differences between a baseline level

I am using RW 1.2.3. on an IBM PC 300GL. Using the data bp.dat which accompanies Helen Brown and Robin Prescott 1999 Applied Mixed Models in Medicine. Statistics in Practice. John Wiley & Sons, Inc., New York, NY, USA which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened and initialized with > dat <- read.table("bp.dat") >

Hello, I''m trying to fit a linear mixed effects model of the form: lme(y ~ x * Sex * Year, random=x|subject) where Sex and Year are factors with two and three levels respectively. I want to compare the fixed effects for each level to the overall mean, but the default in R is to compare to the first level. This can be changed by adding the term -1 to the righthand side of the model

I am trying to run a lme model and some contrast for a matrix . lnY [1] 10.911628 11.198557 11.316971 11.464869 11.575233 11.612101 11.755903 11.722035 11.757705 11.863744 11.846515 11.852721 11.866936 11.838452 11.946680 11.885509 [17] 11.583309 11.750082 11.756005 11.630797 11.705536 11.566722 11.679448 11.703521 NA 11.570949 11.716919 11.573343 11.733770 11.720801 11.804124 11.775074

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Hi, I am clustering objects using the agnes() function and the UPGMA clustering method (function = "average"). Everything works well, but apparently something is wrong with the dendrogram. For example: x<-c(102,102.1,112.5,113,100.3,108.2,101.1,104,105.5,106.3) y<-c(110,111,110.2,112.1,119.5,122.1,102,112,112.5,115) xy<-cbind(x,y) library(cluster) UPGMA.orig<-agnes(x)

Dear R-listers, I am fitting bivariate mixed models for cost-effectiveness data of cluster randomized trials using lme in R. So I have individuals nested within clusters. My response variable is a vector with bivariate response (individual level costs and effects) stacked into a single column. The covariates in my models are a constant and a treatment term. They are response-specific, e.g. a

I have an interesting lme - problem. The data is part of the Master Thesis of my friend, and she had some problems analysing this data, until one of her Jurors proposed to use linear mixed-effect models. I'm trying to help her since she has no experience with R. I'm very used to R but have very few experience with lme. The group calls of one species of parrot were recorded at many

Hi, we have a split-plot experiment in which we measured the yield of crop fields. The factors we studied were: B : 3 blocks I : 2 main plots for presence of Irrigation V : 2 plots for Varieties N : 3 levels of Nitrogen Each block contains two plots (irrigated or not) . Each plot is divided into two secondary parcels for the two varieties. Each of these parcels is divided into three subplots

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as: