similar to: Questions for "domist... subscript out of bounds"

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

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

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

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

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 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

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

Hello R-users, First my settings: R-1.8.1, compiled as a 64bit application for a Solaris 5.8, 64 bit. The OS has 8Gb of RAM available and I am the sole user of the machine, hence pretty much all the 8Gb are available to R. I am pretty new to R and I am having a hard time to work with large data sets, which make up over 90% of the anlyses done here. The data set I imported in R, from S+, has a

To whom it may concern, I get the following message when I run my function: Warning message: multi-argument returns are deprecated in: return(call.fn, repl, time, from, to, last.year, occup.m, ant.occ.m, > version platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.0

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 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",

Hi, I am unable to install packages on my R 2.12.0 Windows 7 machine. Here are the relevant lines: sessionInfo() R version 2.12.0 (2010-10-15) Platform: x86_64-pc-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252

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-

Dear Members, I required to score probability of survival before specified time using fitted cox model on scoring dataset. On the training sample data I am able to get the probability of a survival before time point(t), but on the scoring dataset, which will have only predictor information I am facing some issues. It would be great help for me if you tell me where am I going wrong! Here is the

I am in the process of transferring from an old version of S+ to using R having used a variety of other packages in the past. I'm hugely impressed with R but it has an excellent but depressing habit of exposing that I'm not a professional statistician and has done so again. Someone has run a nice little repeated measures design on my advice, students randomised to four orders of a

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

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

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

I am hoping someone will be kind enough to have a look at the following piece of code and tell me if there is a way to run lme() so it is a lot faster. The inner loop, j in 1:15000, takes about 2 hrs on my 2.8GHz dual Xeon 4GB RAM machine. The timings I have done show the dominant execution time is in lme. options(contrasts=c("contr.sum", "contr.sum"))

Hi, Could you please tell me how I correct the following error message? “Error in object$tables[[v]] : subscript out of bounds” This is the code: library(e1071) data(iris) attach(iris) class_label <- names(iris)[1] myformula <- formula(paste(class_label,"~ .")) mymodel<-naiveBayes(myformula, iris,cross=3) predict(mymodel,iris) ##Error in object$tables[[v]] :