similar to: Suggested add to the documentation for the identify() function

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,

Gentlemen, In the documentation for reshape, in the function signature, the argument "direction" is not listed. However, it is explained in the explanation of parameters below. I am using R 2.2.1. Out of curiosity: Is the R core team still an all-male affair? I don't think I have seen a single lady's name. -- -- Vivek Satsangi Student, Rochester, NY USA

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, 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, 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 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 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, 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

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,

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 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:

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Dear list, I just tried to do the same thing, and did not find anything on a weighted qqplot. My weights are actually counts (positive integers). Here is a modification of qqplot, following Duncan Murdoch's suggestion. Any feedback would be welcome! Thanks, Jean-Christophe weighted.qqplot <- function (x, y, plot.it = TRUE, xlab = deparse(substitute(x)), ylab = deparse(substitute(y)),

Hello all, I wrote a small function to add labels for outliers in a boxplot. This function will only work on a simple boxplot/formula command (e.g: something like boxplot(y~x)). Code + example follows in this e-mail. I'd be happy for any suggestions on how to improve this code, for example: - Handle boxplot.matrix (which shouldn't be too hard to do) - Handle cases of complex

On Tue, Jun 21, 2016 at 8:58 PM, John Criswell <jtcriswel at gmail.com> wrote: > On 6/20/16 11:29 PM, Mehdi Amini wrote: > > > On Jun 20, 2016, at 11:12 AM, John Criswell via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > > On 6/20/16 9:39 AM, vivek pandya via llvm-dev wrote: > > Dear Community, > > To improve current interprocedural register

On Tue, Jun 21, 2016 at 12:31 AM, Matthias Braun <matze at braunis.de> wrote: > I just discussed this with vivek on IRC (and I think we agreed on this): > > Let me first state the motivation clearly to ease later discussions: > As far as the motivation for this change goes: Changing the calling > convention allows us to choose whether a register is saved by the callee or >

Dear Community, To improve current interprocedural register allocation (IPRA) , we have planned to set callee saved registers to none for local functions, currently I am doing it in following way: if (F->hasLocalLinkage() && !F->hasAddressTaken()) { DEBUG(dbgs() << "Function has LocalLinkage \n"); F->setCallingConv(CallingConv::GHC); } but we think threre

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