similar to: Trouble setting up correlation structure in lme

[cc'ing back to r-help] On Fri, Nov 18, 2011 at 4:39 PM, matteo dossena <matteo.dossena at gmail.com> wrote: > Thanks a lot, > > just to make sure i got it right, > > if (using the real dataset) from the LogLikelihood ratio test model1 isn't "better" than model, > means that temporal auto correlation isn't seriously affecting the model? yes. (or

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Dear experts, I'm trying to transfer a mixed model developed in SAS to R. This it what it looks like in SAS: proc mixed method=ml; class a b c subj; model y = a|b|c; repeated /subject=subj type=ar(1); I tried something like this in R: mixed <- lme(y ~ a + b + c + a*b + a*c + b*c + a*b*c, random = ~ 1 | subj, correlation = corAR1(form = ~ 1 | subj)

Despite lots of investigation, I haven't found any R packages might be suitable for the following problem. I'd be very grateful for suggestions. I have three-way nested data, with a series of measures (obs) taken in quick succession (equal time spacing) from each subject on different days. The measures taken on the same day are temporally correlated, so I'd like to use an AR1

When finally got some time to do some coding, I started and stopped right after. The stationary test is a good starting point because it demonstrates how we should be able to move the very basic R matrices. I have a real- world small N data set with rows: id(n=1)---t1---variable1 ... id=(N=20)---T=21---variable1 Thus, a good test case. For first id I was considering something like this: lag

Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"

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",

> myfit1 <- gls(col1 ~ col2+col3+col4+col5+col6-1, data=data2, corr=corAR1( 0.3202), method='ML') Error: cannot allocate vector of size 199712 Kb if I get rid of the "corr=corAR1(0.3202)" option, it works okay... can anybody help me? thanks a lot! [[alternative HTML version deleted]]

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

Dear all; I experienced a memory allocation error while running gnls: > wBlplfe.wc <- update(wBlplfe.w, corr=corAR1()) Error: cannot allocate vector of size 37950 Kb In addition: Warning message: Reached total allocation of 256Mb: see help(memory.size) Increased memory limit to 512, 640 M, the allocation error persisted: > memory.limit(512) NULL > wBlplfe.wc <-

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

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 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