similar to: (newbie) Accessing the pieces of a 'by' object

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 R-Helpers: I am using trying to use *ddply* to extract min and max of a particular column in a data.frame. I am using two different forms of the function: ## var_name_to_split is a string -- something like "var1" which is the name of a column in data.frame ddply( df, .(as.name(var_name_to_split)), function(x) c(min(x[ , 3] , max(x[ , 3]))) ## fails with an error - case 1 ddply(

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 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 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 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 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.) --

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

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, 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 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)),

Hi! Attached patch adds NetBSD specific pieces to xenstat and xenstore. Signed-off-by: Christoph Egger <Christoph.Egger@amd.com> -- AMD Saxony, Dresden, Germany Operating System Research Center Legal Information: AMD Saxony Limited Liability Company & Co. KG Sitz (Geschäftsanschrift): Wilschdorfer Landstr. 101, 01109 Dresden, Deutschland Registergericht Dresden: HRA 4896

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I have a large file (millions of lines) and would like to read it in pieces. The file is logically separated into little modules, but these modules do not have a common size, so I have to scan the file to know where they are. They are independent, so I don't have to read one at the end to interpret one at the beginning. Is there a way to read one line at a time and parse it on the fly and do

Dear all, I think this is an easy task, but I don't know how to do it. Specifically, I have 69 columns with 300.000 rows. In each cell there is a code like "2E3", "4RR", etc. I now have a list that replaces this with values, e.g., old new 2E3 5 4RR 3 etc. The list ist about 1600 rows long, so also to extensive for normal solutions Do you how to do that? It would

Sounds great! By `connection error` it looks like the `upsc` client had nowhere to go. Is `upsd` started (and does it `LISTEN` on locahost or asterisk per `upsd.conf`)? Jim On Sat, Oct 26, 2024, 08:13 William R. Elliot <bill at wreassoc.com> wrote: > Hello again. > > I now have large chunks of the new driver working in the foreground and > wanted to see what upsc reports