similar to: looking for something like "ave" I can pass non numeric to

OK, I am try to produce an array of ranks. I have a set of data (s) that looks like this rt subj 312 dave 467 dave 411 dave 383 kim 398 kim ... Now I want to make a column that is an array of the ranks of rt by subject. The closest of gotten is using the following. r <- by (s, s$subj, function(d) rank(d[1])) This gets the data out with ranks but I cannot figure out how to easily

How does one go about doing a repeated measure regression? The documentation I have on it (Lorch & Myers 1990) says to use linear / (subj x linear) to get your F. However, if I put subject into glm or lm I can't get back a straight error term because it assumes (rightly) that subject is a nominal predictor of some sort. In looking at LME it seems like it just does the right thing

I am new to R, therefore this might be a naive question, sorry in advance for that. I have data on 4 groups of mice (#of subjects: 8,8,8,9) where the first group is the control group and 2-4 are 3 doses of a neuro-inhibitor. For each mouse we measured the exploration time of a familiar and a new object (thus repeated measure design), and I want to test whether there are different exploration

I predicted that y would increase as x increased. However, I only made the prediction on the ranks of the scores. The ranks don't correlate with predicted. And, I don't think a regression on the ranks is warranted. However, the actual scores do yield a significant slope for b, and a significant R^2 using a linear regression (y is the value and x is the predicted rank). What

Hi I've just installed R on our Sun kit in the lab and on my Linux box at home. My statistical expertise is not advanced - I have completed Open University probability and statistics courses, and been using Gary Perlmans !STAT package for analyses of variance, descriptive stats, linear regression, non-parametric stats etc for 10 years or so. (so be gentle with me :-) ) However, R is

Hi, This is probably really obvious, by I can't seem to find anything on it. Is there a fast version of ave for when the data is already sorted in terms of the factor, or if the breaks are already known? Basically, I have: X = 0.1, 0.2, 0.32, 0.32, 0.4, 0.56, 0.56, 0.7... Y = 223, 434, 343, 544, 231.... etc of the same, admittedly large length. Now note that some of the values of X are

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",

Hi Patrick Hausmann <c18g at uni-bremen.de> napsal dne 15.11.2007 18:59:06: > Hello Petr, > > one question solved, the next is standing in front of me... If you > have a minute to look .... great! > > > x > V1 V2 F1 > 1 A 2 0.1552277 > 2 A 3 0.1552277 > 3 A 4 0.1552277 > 4 B 3 0.8447723 > 5 B 2 0.8447723 > 6 C 6 0.2500000

I've been using the ave function to compute some statistics on a data frame. After a while I noticed that, for some reason, it was returning numerical statistics as strings instead of numbers. I delved into the code of the functions and traced the problem to the following fact: ave uses split<- to do its work. Specifically, it does "split(x, g) <- lapply(split(x, g),

Using lmer() on my data results in an error. The problem, I think, is my model specification. However, lm() works ok. I recreated this error with a more simple dataset. (See code below.) # word and letter recognition data # two within factors: # word length: 4, 5, 6 letters # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in 6-letter words) # one dependent variable: #

I know this is a little OT for the list and blatent advertising but inkeeping with the thread topic, I've just finished a Windows playlist generator for internet/radio stations. Suitable for highly automated situations. Features anti-song repeat & anti-artist repeat options. No song maintenance required. Works on a rotation or "clock" basis. Produces a personalised HTML

library(plyr) n<-100000 grp1<-sample(1:750, n, replace=T) grp2<-sample(1:750, n, replace=T) d<-data.frame(x=rnorm(n), y=rnorm(n), grp1=grp1, grp2=grp2) system.time({ d$avx1 <- ave(d$x, list(d$grp1, d$grp2)) d$avy1 <- ave(d$y, list(d$grp1, d$grp2)) }) # user system elapsed # 39.300 0.279 40.809 system.time({ d$avx2 <- ave(d$x, interaction(d$grp1, d$grp2, drop =

Full_Name: Brendan Barnwell Version: 2.9.0 OS: Windows XP Pro Submission from: (NULL) (71.102.131.29) The ave() function returns an incorrect datatype. Specifically, ave(x, g, f) always returns a vector with the same mode as x, rather than using the mode of the vector returned by f. Observe: > x [1] "A" "B" "C" "A" "B" "C"

Dear invaluable R-list, my present problem is arranging/removing some panels in a lattice plot. Please consider the following: df.data <- cbind.data.frame(expand.grid(SUBJ=1:5, TREAT=LETTERS[1:4], REF=letters[1:4] ) ) df.data <-

I have data where where I've taken some measurements three times... twice in rapid succession so I could check test-retest reliability of a piece of equipment, and then a third measurement some time later. Not I'd like to do an analysis where I have two scores... the first being the mean of the first two taken the same day, and the second being the one taken later. I have a lot of

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

> See the 'Writing R Extensions' manual, specifically > Creating R Packages -> Writing package vignettes thank you, i saw this entry. However, this entry is rather about how to include documents (in particular Sweave-based) into a package. But I have meant smth else. Let me explain in example. Today I use emacs as environment for my R-sessions. I am quite happy to use

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

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 )

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