similar to: Minor documentation improvement

Folks, I know that I can do the following using a loop. That's been a lot easier for me to write and understand. But I am trying to force myself to use more vectorized / matrixed code so that eventually I will become a better R programmer. I have a dataframe that has some values by Year, Quarter and Ranking. The variable of interest is the return (F3MRet), to be weighted averaged within the

Folks, I do a series of regressions (one for each quarter in the dataset) and then go and extract the residuals from each stored lm object that is returned as follows: vResiduals <- as.vector(unlist(resid(lQuarterlyRegressions[[i]]))); Here lQuarterlyRegressions is a vector of objects returned by lm(). Next, I may go find outliers using identify() on a plot or do some other analysis which

Folks, This is probably a "help me google this properly, please"-type of question. In TIBCO Spotfire, there is a procedure called "line similarity". I use this to determine which observations show a growing, stable or declining pattern... sort of like a mini-regression on the time-line for each observation. So of the input is

Folks, I was drawn to R, like many others, partly for the opportunity to draw nice, colorful graphs (occasionally ones with meaning, too :-) ). I am still quite a newbie to R. As such, I have been trying to understand the code for the graphics on the home page (the ones from the 2004 contest -- the dendrogram, the cluster plot with different coloured circles, etc.) I was wondering whether anyone

Folks, I want to make the following function more efficient, by vectorizing it: getCriterionDecisionDate <- function (quarter , year) { if (length(quarter) != length(year)) stop ("Quarter and year vectors of unequal length!"); ret <- character(0); for (i in 1:length(quarter)) { currQuarter <- quarter[i]; currYear <- year[i]; if ((currQuarter < 1) |

Folks, I have the following code, that works fine on smaller data sets. For larger datasets, it runs out of memory and runs way too slow because we are essentially creating large vectors with rep() and then calling median() on it. (I learned this approach from a post on the web). Below that, I have written the corresponding SAS code. The SAS code works fast because I can just tell the proc

Folks, 1. Is there a more appropriate list (r-devel?) for posting such suggestions? I am a newbie to R, and doubtless will have some suggestions for the documentation -- some good, others not quite so. I would actually like to help give back to the community (I was motivated by Prof. Ripley's 2001 talk in which he had commented that open source software users rarely give back anything.) --

Disclaimer/Apology: I am an R newbie I am seeing some behaviour that seems to me to be the result of some cacheing going on at some level, and perhaps this is expected behaviour. I would just like to understand the basic rules. What I have is a file with some data. I read it in and then do a summary on the resulting dataframe. I find the some values are completely outside the expected range,

Folks, Sorry if this question has been answered before or is obvious (or worse, statistically "bad"). I don't understand what was said in one of the search results that seems somewhat related. I use aggregate to get a quick summary of the data. Part of what I am looking for in the summary is, how much influence might the NA's have had, if they were included, and is excluding

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hello gentlemen and lady's, I am trying to filter ssh traffic regardless of the port the connection is opened on. I want to do the same for rlogin and telnet. I know it would be easier to use a proxy server and only allow users to access the web... but it's more complicated... they also need other ports open... and they use public IP

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

Folks, I have a very basic question. The solution eludes me perhaps because of my own lack of creativity. I am not attaching a fully reproducible session because the issue may well be becuase of the way the data file is, and the data file is large (and I don't know whether I can legally distribute it). If people can suggest things that might be wrong in my data or the way that I am reading it,

I am having a hard time understanding how to perform a "repeated measures" type of ANOVA with R. When reading the document found here: http://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_repms.html I find that there is a reference to a function make.rm () that is supposed to rearrange a "one row per person" type of frame to a "one row per observation" type

Dear R-helpers, We marked 6000 leaves from 5 SPECIES - 10 individuals/species - in two different TREATMENTs: a control and a dry-plot from which 50% of incoming precipitation was excluded. We followed those leaves for 42 months and noted the presence and absence at each visit. I then carried out a Cox Harzard model to see differences in leaf mortality between parcels and among species over time:

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Hello - R-experts, Is there any way with which we can specify the number after decimal point to take. Like I have a situation where the values are comming 0.160325923 but I only want 4 place to decimal say 0.1603. Is there any way for that. I am no expert in R- and this may sound simple to many.sorry Thanks for any help. With Regards Subhabrata

Dear list I'm using the reshape command and want to reshape a wide data set to a long one e.g. I have the variables y1,y2,y3,age1,age2,age3,sex,ethnic I want my new long data set to consist of the variables y (which has been created from y1,y2,y3), age (which has been created from age1,age2,age3), sex and ethnic I have tried to use the command:

Hi there, I am trying to reorganized my data sets so that it is easy for MARK to read it. Basically I have the encounter histories of 1837 butterflies The data looks like this the first 4 columns are the occasions and the last two code for male and female > t1 t2 t3 t4 M F > 1 0 0 0 1 0 male capture on time1 but not seen on time 2, 3 > and 4 > 1 0 0 0

Dear ladies and gentlemen! When I use the plot funtion how can I change the size of the title for the x and y axes (xlab, ylab)and the size of the axes label ? Thank you very much. With best regards Claudia

Dear Ladies and Gentlemen, I have a fairly large database (N=13,000) and a single main categorical discriminator between the groups. I want to look at the time course of a number of continuous biochemical variables over chronologic age. Therefore I believe I need to prepare hexbinplots in two columns with simple regression lines in them (with useOuterStrips (in library(latticeExtra) if