similar to: A More efficient method?

Dear R-Help, I wish to simulate a process so that it has certain properties in the frequency domain. What I attempted was to generate a random time-series signal, use spec-pgram(), apply a function in the frequency domain, and then inverse transform back to the time-domain. This idea does not seem as straight forward in practice as I anticipated. e.g. x<-ts(rnorm(1000, 0,1), frequency=256)

Dear list, What on earth is spec.pgram() normalized too? If you would like to skip my proof as to why it's not normed too the mean squared or sum squared amplitude of the discrete function a[], feel free too skip the rest of the message. If it is, but you know why it's not exact in spec.pgram() when it should be, skip the rest of this message. The issue I refer herein refers only too a

Dear List, This is my first post, and I'm a relatively new R user trying to work out a mixed effects model using lme() with random effects, and a correlation structure, and have looked over the archives, & R help on lme, corClasses, & etc extensively for clues. My programming experience is minimal (1 semester of C). My mentor, who has much more programming experience, but a comparable

Dear Prof Ripley, First of all, unless you are an english professor, then I do not think you have any business policing language. I'm still very much a student, both in R, and regarding signal analysis. My competence on the subject as compared too your own level of expertise, or my spelling for that matter, may be a contension for you, but it would have been better had you kept that opinion

Dear R-Help, I am working on a paper in an R course for large file support in R using scan(), relational databases, and XML. I have never used SQL or heirarchical document formats such as XML (except where it occurs without user interaction), and knowledge in RDBs and XML is lacking in my program. I have tried finding a working example for the novices-novice on the topic, read many postings, the

Rhelpers: I'd like to modify this RSQLite statement: rs_stations<-dbSendQuery(con_stations, "select * from stations") so that stations is actually an R variable, e.g.: stations=c("stationA","stationB") How would I modify the above statement to query from stations[[1]] (aka "stationA")? --j

Dear List-mates, I'm trying to read a tk window menu from a file using {svWidgets} and 'menus.txt' but am receiving Warnings without seeing the desired consequences of the call. library(svWidgets) tkWinAdd("KSesnMain",title="kLab Session Manager for R", pos="+0+0") MenuRead(file="menus.txt") Warning messages: 1: Unrecognized menu type for

Dear list, How is the tolerance for a model parameter in an lm() call extracted? I did not see a solution in the documentation for lm(), or predict(), nor in the archives using 'tolerance' as the search string. I also checked into the nlme package, though nothing popped out at me. Sincerely, KeithC.

Dear List, I was trying to verify that I could use extend.series in the wavelets package and kept getting an error when trying to use method="zero". I'm not seeing where my syntax has gone awry. According to the documentation, [see ?extend.series] " method: A character string indicating which extension method to use. Possible values are

Dear List-mates, I have been trying to set up a 4x8 trellis plot (that is, 4 columns, 8 rows), and would like to have the axis limits set based on the data range of rows (for ylim) and columns (for xlim). I've been using the call: foo<-xyplot(y~x|Epoch+Subject, type=c("l","r"), par.strip.text=list(cex=0.5), ...) and updating to see different effects of scale

Dear list mates, Are {utils} dialog box functions, and winMenuAdd... functions used to change (e.g. Console) menus, platform dependent? I'm writing a script loaded with .First that provides first time users in a lab course with the ability to select, load, and change between what I called a 'session' (more specific save that focuses on the session object I defined rather than the

Dear all, I am a relative novice with R, so please forgive any terrible errors... I am working with a GLM that describes a response variable as a function of a categorical variable with three levels and a continuous variable. These two predictor variables are believed to interact. An example of such a model follows at the bottom of this message, but here is a section of its summary table:

Hi Rhelpers, I would like to have some help with a plot which is beyond my capabilities. This plot that I am seeking involves an overlay of two different barcharts and one xyplot. The code that I have used is the following : #save(df1,file="M:\\KBR\\df1.RData") load(file="M:\\KBR\\df1.RData") # df1$Year.ord created to obtain the right order i.e. 2015M < 2015K

Hi, I have two data sets; one is a population and the other a sample of that population. I am trying to plot both on the same trellis display. # Example data set with two numerical attributes and two categorical pop <- data.frame(var1=rnorm(2000, 2000, 500), var2=rnorm(2000, 2000, 500)) cat<-(runif(2000)<=.5)+0 for(i in 1:length(cat)){ if(cat[i] == 0){ pop[i,"cat1"] =

Hello, I'm looking for a solution for the following problem: 1) I have a folder with several csv files; each contains a set of measurement values 2) The measurements of each file belong to a position in a two dimensional matrix (lets say "B02.csv" belongs to position 2,2 3) The size of the matrix is fix 4) I cannot assure to have a csv file for each position 5) Each position

I have a categorical variable in a dataframe similar to the following... cat 1 1 3 2 4 I need to convert it to 4 dichotemous variables for each observations like... cat1 cat2 cat3 cat4 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 Thanks in advance! Shane

Hello, I can get the median for each factor, but I'd like another column to go with each factor. The nm column is a long name for the lvls column. So unique work except for the order can get messed up. Example: x =

Hi, I am having some difficulties with matrix operations. It is a little hard to explain it so please bear with me. I have a very large data set, large enough that it needs to be split in parts in order to deal with. I can work things on these "parts" but the problem lies in adding together these parts for the final answer. So that been said, let's say that i split the data in 2

Hello everyone, My name is Sebastiaan Peters, an undergrad student from The Netherlands. The project for creating drop-in replacements for GNU Binutils peaked my interest. It would make my day (or my summer) being able to contribute to such a project. Since I have no experience with llvm besides some basic usage of clang, the first thing I tried was to find out what llvm tools are out there.

Rhelpers: Having a problem solving this. I have an xyplot call that looks like this: print(xyplot(temp_species_EAM_Pred_Pop$x+temp_species_NULL_Pred_Pop$x~temp_species_EAM_Pred_Pop$Action,main=current_species, xlab="Action",ylab="Predicted Pop", xlim=c(xmin,xmax),ylim=c(ymin,ymax),