similar to: simple repeated measures model: dumb user baffled!

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

Dear R Users, I am trying to write a wrapper around summarize and xYplot from Hmisc and am having trouble understanding how to pass arguments from the function I am writing to the nested functions. There must be a way, but I have not been able to figure it out. An example is below. Any advice would be greatly appreciated. Thanks, Dan # some example data df=expand.grid(rep=1:4,

I have a list of dataframes, and want to apply a function to subsets of the rows of each dataframe. It seemed natural to write a function that takes a dataframe as an argument, and uses 'by() within it to apply the function to the dataframe subsets. However, I cannot get it to work. The problem seems to be passing the data argument of by() as a function argument. Is this bug, or am I

Dear R users,I am looking for an easy (i.e., direct) way in obtaining the F and p values from the intercept in anovas with within-subject designs. Data are from a psychophysics expeirment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject.I would like to know not only whether there is an effect of modality, but also wether the main effect is

Hi, I'm new to R, and I'm STATA user before, could you help me where I can get document about comparison command between STATA and R. Thank you very much, Best regards, -iip-

Hi. Thank you very much in advance for your help. I have generated data from two simple linear models and used k-means clustering (stats4) to identify two clusters in the generated data. Next, I would like to do simple linear regression for each separate cluster. I can do this if I first use the cluster labels to define two separate data frames with the subset function. However, I would

Dear R users, I am looking for an easy (i.e., direct) way of obtaining the F and p values from the intercept in anovas with within-subject designs. My data are from a psychophysics experiment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject. I would like to know not only whether there is an effect of modality, but also whether the main effect is

List, I want to reshape my data, but I'm not sure how to do it... it might be a simple task, but don't know which package does this. "occ.data" (see below) is how my original data are arranged, and I know that with melt() I can reshape it like "y" (see below). However, I just want to build a matrix like the "y" matrix, but with only 2 dimensions. Something

Hi guys, I need some help to analyzing my data. I start to describe my data: I have 21 matrices, every matrix on the rows has users and on columns has items, in my case films. Element of index (i, j) represent the rating expressed by user i about item j. I have a matrix for each of professions. An example of a this type of matrix is: item 1 item 2 item 3 item4 id

Dear all, As part of a larger function, I am randomly removing rows from a data frame. The number of removed rows is determmined by a Poisson distribution with a low mean. Sometimes, the random number is 0, and that's when the problem starts: My data frame: > temp occ x y dbh age 801 0 2977.196 3090.225 6 36.0 802 0 2951.892 3083.769 8 40.6 803 0 2919.111

Hi all, I am a new user for R. I faced a questions about subscript out of bounds. Although I checked some other helps before I sent this email, I still don't know how to deal with this problems. I want to use "domain" function to run species distribution model in R. I used "sp.occ.do<-domain(env.pre, pred_train, factors=c('continent')) &

Hi All, I have a doubt. I used macros and i try to pass a value to a macro by concatenating a bunch of strings. But it does not seem to work. Please help. I have written down my code and the error message please tell me how to pass the value that a string points to. Thanks in advance #macro defined

I need a function which is similar to duplicated(), but instead of returning TRUE/FALSE, returns indices of which element was duplicated. That is, > x <- c(9,7,9,3,7) > duplicated(x) [1] FALSE FALSE TRUE FALSE TRUE > duplicates(x) [1] NA NA 1 NA 2 (so that I know that element 3 is a duplicate of element 1, and element 5 is a duplicate of element 2, whereas the others were

Dear R-users, I have written the following code to generate some trellis plots. It works perfectly fine except that it is quite slow when it is apply to my typical datasets (over several thousands of lines). I believe the problem comes from the loops I am using to subset my data.frame. I read in the archives that the tapply function is often more efficient than a loop in R. Unfortunately ,

Dear list, I'm having trouble to see how my elements on a 4 dimensional array are listed. For example, I generated the following array: junk.melt=melt(occ.data,id.var=c("Especie", "Site", "Rep", "Año"), measure.var="Pres") y=cast(junk.melt, Site ~ Rep ~ Especie ~ Año) Now, I want to be able to look at how my species (Especie) are listed, in

Dear R-users, I am a dentist (so forgive me if my question looks stupid) and came across a problem when I did simulations to compare a few single level and two level regressions. The simulations were interrupted and an error message came out like 'Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1'. My collegue suggested that this might be due to my codes

Hello everyone, I would like to perform a 2-way manova test, but I'm having some issues. I implemented like this Y<-cbind(Resp1,Resp2,Resp3,....,Respn) model<-manova(Y "tilda" FactorA*FactorB) summary.aov(model) 1. I don't know at what level I have to do the Type I error correction. Is it on p-values returned by "summary.aov(model)? Or is it when I compare each

Dear list, I'm working with a large data set, where I grouped several species in one group (guild). Then I reshaped my data as shown below. Now, I just want to have "Rep" only as 1 or 0. I'm not being able to change the values of rep>=1 to 1... tried many things and I'm not being successful! > melting=melt(occ.data,id.var=c("guild", "Site",

Dear R-Users, I am comparing the nlme package in S-Plus (v. 7.0) and R (v. 2.2.1, nlme package version 3.1-68.1; the lattice, Matrix, and lme4 have also just been updated today, Jan. 23, 2006) on a PC (2.40 GHz Pentium 4 processor and 1 GHz RAM) operating on Windows XP. I am using a real data set with 1,191 units with at most 4 repeated measures per unit (data are incomplete, unbalanced). I

Dear MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for