similar to: aov - subjects nested within groups & crossed with questions

Hello, I have the following data and I try to properly import it in R and plot the 4th column relative to time 1 2008-249 17:44:17.973 -2.27 00000000: Accepted 2 2008-249 17:44:18.014 -2.28 00000000: Accepted 3 2008-249 17:44:18.064 -2.29 00000000: Accepted 4 2008-249 17:44:18.123 -2.29 00000000: Accepted 5 2008-249 17:44:18.174 -2.29 00000000:

Dear list,I am quite new to the world of biostatistics and I encounter some issues in the precise understanding of the coxph function of the survival package.I have a set of survival data (patient who had (or died from) a breast cancer) I'd like to see which are the variables that might cause dead or not.When trying> summary(coxph(Surv(Time_to_distant_recurrence_yrs, !Distant_recurrence)~

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Hello, A collegue of mine has compared the runtime of a linear model + anova in SAS and S+. He got the same results, but SAS took a bit more than a minute whereas S+ took 17 minutes. I've tried it in R (1.9.0) and it took 15 min. Neither machine run out of memory, and I assume that all machines have similar hardware, but the S+ and SAS machines are on windows whereas the R machine is Redhat

Sounds distinctly like an assignment you've been set for which we wouldn't help. All I'll say is crossed random effects can be dealt with effectively in lmer. See that for more. -- View this message in context: http://r.789695.n4.nabble.com/A-question-about-conducting-crossed-random-effects-in-R-tp2278443p2279508.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I'm trying to test a repeated measures model with random effects using the nlme library. Suppose I have two within subjects factors A, B both with two levels. Using aov I can do: aov.1 <- aov(y ~ A*B + Error(S/(A+B)) following Pinheiro and Bates I can acheive the analagous mixed-effects model with: lme.1 <- lme(y~A*B, random=pdBlocked(list(pdIdent(~1),pdIdent(~A-1),

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 all, I am quite new at R and have a question about using lme with crossed random factors. I followed the instructions of Pinheiro & Bates, but that did not work because of the non grouping of my data. Reading prior threads ( http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg10849.html), I found a solution to deal with non grouped data and crossed random factors in lme, by defining

I need to specify a model similar to this lme.formula(fixed = sqrt(lbPerAc) ~ y + season + y:season, data = cy, random = ~y | observer/set, correlation = corARMA(q = 6)) except that observer and set are actually crossed instead of nested. observer and set are factors y and lbPerAc are numeric If you know how to do it or have suggestions for reading I will be grateful. eal ps I have

Two versions of R 1. R-patched (compiled with X11 support, and tcl/tk for X11) 2. R-devel (compiled with native tcl/tk support, without-x) UI's 1. Terminal.app (or an Xterm) with an X server running. 2. IDLE (i.e. native Python GUI with native tkinter) with Rpy loaded. No X server. Packages with "problems" 1. tcltk 2. methods The "normal" combination is to use

Dear all, In the SASmixed package there is an example of an analysis of a split-plot experiment. The model is fm1Semi <- lme( resistance ~ ET * position, data = Semiconductor, random = ~ 1 | Grp) where Grp in the Semiconductor dataset is defined as ET*Wafer. Is it possible to specify the grouping directly some way, e.g. like fm1Semi <- lme( resistance ~ ET * position, data =

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Dear All, I am trying to fit a simple linear mixed model (see below this paragraph) arising from a crossed factorial design with 2 factors and ubalanced number of replicates (from two to five) in each cell, but I keep getting an error message (see bottom of message). The model is: yijk = intercept + ai + bj + abij + ejik, where: "intercept" is fixed, and the crosss factors, ai, i =

Hello R users, I've read the posts on this topic, and had a look at the R documentation for nlme, but I can't seem to make this work. I'd like to be able to fit a mixed effects model with crossed random effects, but also be able to specify the covariance matrix structure for the residuals. Here's the syntax using the lmer function in lme4 (which doesn't currently allow

Hello, and thanks for your time reading this. I'm trying to test interactions of my dataset, in which the all of the factors are within the same column. Type Vol 1 CMass -4.598 2 BBack -4.605 3 BMass -4.602 4 CMass -4.601 5 CBack -4.605 6 CMass -4.604 7 CMass -4.602 8 CMass -4.604 9 CBack -4.605 10 BBack -4.503 11 CMass -4.605 Im attempting to determine the interaction effects of B or C

mgcv: Constructing probabilities for binomial GAM with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken).

Dear Contributors, I am back asking for help concerning the same type of dataset I was asking before, in a previous help request. I needed to sum data over subsample of three time series each of them made of 100 observations. The solution proposed were various, among which: db<-p dim( db ) <- c(25,4,3) db2 <- apply(db, c(2,3), sum) db3 <- t(apply(db2, 1, function(poff)

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Hi all, I have just received the following e-mail from an Asterisk user: "I just made a call via BT to a mobile. Then an incoming call came in and Ann else answered it - it made my call go completely fuzzy and I could hear what the woman on the other line was saying to Ann but I couldn't hear my conversation! When Ann's call finished - mine went even fuzzier and all I could hear