similar to: lme model specification problem (Error in MEEM...)

Dear list, I would like to expand a DF with all the missing levels of a variable. a <- c(2,2,3,4,5,6,7,8,9) a.cut <- cut(a, breaks=c(0,2,6,9,12), right=FALSE ) (x <- data.frame(a, a.cut)) # In 'x' the level "[0,2)" is "missing". AddMissingLevel <- function(xdf) { xfac <- factor( c("[0,2)", "[2,6)", "[6,9)",

Hi, I posted the message below last week, but no answers, so I'm giving it another attempt in case somebody who would be able to help might have missed it and it has now dropped off the end of the list of mails. I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no

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

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

Hi , I am new to R. I am trying to run a simple R script as shown below: aov.R ------ data1<-c(49,47,46,47,48,47,41,46,43,47,46,45,48,46,47,45,49,44,44,45,42,45,45,40 ,49,46,47,45,49,45,41,43,44,46,45,40,45,43,44,45,48,46,40,45,40,45,47,40) matrix(data1, ncol= 4, dimnames = list(paste("subj", 1:12), c("Shape1.Color1", "Shape2.Color1", "Shape1.Color2",

Hello everyone: It's the first time I write to this mailing list. Sorry in advance if my doubt has already been posted before, but I have been checking the archives and I haven't been able to find anything satisfactory. I am running a mixed effects model with nested effects (site and pair, referred to barn swallow nests located in different places in different farms). My dependent

After some fiddling around with the tapply command, I discovered that the factors (the INDEX argument) given to tapply must be specified in fastest-cycling first order. The following code shows how I discovered my error: (R version 1.2.1) -o-o-o-o-o- x <- as.data.frame(list(data=c(-9,0,3,1,-9,1,0,-9,0,3,1,-9,1,0), subj=c(rep(1,7),rep(2,7)),

I am extremely puzzled by this behavior in R. I have a data frame called Trials in which I have results from an experiment. I am trying to do a subjects analysis, but getting weird results. Each row has 1 trial in it, which includes a column for the subject number I get the list of subject numbers like so: > Subj=unique(sort(Trials$Subj)) Then I loop over them. But I get strange results. As

Hello, I am having a problem where code that plots lines using a different data frame plots bars with the current data frame (I am intended to plot lines). The code specifies lines (see below), so I can't figure out why the results are bars. I suspect that it may have something to do with the fact that in the data frame where the code worked as intended, the both variables specifying

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

Hello: I am new R user stumped why the R code after this paragraph generates "Error: syntax error" messages after each of the last 2 lines. I have tried searching the manuals, Hmisc documentation, contributed manuals, help archives, and Internet. I am running R 1.7.1 under Windows 2000 (I will upgrade when my imminent OS upgrade happens). My data was successfully entered and

Dear all, I have discovered that there are many things that I used to do in my GIS which are easily done directly in R, for example calculating interpoint distances using geoR and pick out points inside a polygon using splancs. I now wonder, is there a function to create a line object like a watercourse and then calculate the distances between many points in space and this line? I couldn't

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights

All, I'm trying to plot an all.effects() object, as shown in the help for all.effects and also Crawley's R book (p.178, 2007). The data has a repeated measures structure, but I'm using all.effects for the simple lm() fit here. Below is a reproducible example that yields the error message. fm.ex = lm(dv ~ time.num*drug*X, data = dat.new) fm.effects = all.effects(fm.ex, xlevels =

Dear all: I m a newer on R.? I have some problem when I use?anova function.? I use anova function to get Type I SS results, but I also need to get Type III SS results.? However, in my code, there is some different between the result of Type I SS and Type III SS.? I don?t know why the ?seqe? factor disappeared in the result of Type III SS.? How can I do?? Here is my example and result.

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming

I'm implementing a custom bootstrap resampling procedure in R. This procedure resamples clusters of data points obtained by different subjects in an experiment. Since the bootstrap samples need to have the same size as the original dataset, `target.set.size`, I select speakers compute their data point contributions to make sure I have a set of the right size. set.seed(1)

Hi, I have a data frame and want to merge adjacent rows if some condition is met. There's an obvious solution using a loop but it is prohibitively slow because my data frame is large. Is there an efficient canonical solution for that? > head(d) rt dur tid mood roi x 55 5523 200 4 subj 9 5 56 5523 52 4 subj 7 31 57 5523 209 4 subj 4 9 58 5523 188 4 subj 4 7