similar to: sorting character vectors

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Wondering if I missed anything or if that's a problem with R: I want to have many x axis tickmarks and labels such that it makes sense to put them on multiple "lines". The newline character seems to be ignored though for axis labels: plot(1:33, axes=F) axis(1, 9, 9) axis(1, 11, "\n11") axis(1) # whereas title("one\ntwo") works as I expect it to work. In

Is there a function in R, which would return index of maximum value in a vector ? e.g. > v <- round(10*rnorm(8)) > v [1] 6 -3 -6 15 7 9 0 -19 > max(v) [1] 15 ??? index.max(v) ??? 4

Is the following a known issue, in particular in terms of message clarity of the latter two? > matrix(0, 10^8, 10^8) Error: cannot allocate vector of size 2064896 Kb > matrix(0, 20, 10^10) Error in matrix(0, 20, 10^10) : negative length vectors are not allowed > matrix(0, 10^10, 10^10) Error: cannot allocate vector of size 0 Kb # looks better for arrays: > array (0, c(20, 10^10))

In e1071 package/svm default epsilon value is set to 0.1 and not 0.5 as documentation says. R

I have a data frame (df) with colums x, y and z. e.g. df <- data.frame(x = sample(4), y = sample(4), z = sample(4)) I can extract column z by: df$z or df[3] I can also extract columns x,y by: df[1:2] or by df[-3]. Is it possible to extract x,y columns in a "symbolic" fashion i.e. by equivalent of df[-z] (which is illegal) ??? Or alternativeley, is there an equivalent of

I don't know the details of pls (in the pls.pcr package, I assume), but if you use validation="CV", that says you want to use CV to select the best number of components. Then why would you specify ncomp as well? Andy > From: ryszard.czerminski at pharma.novartis.com > > When I try to use ncomp parameter in pls procedure I get > following error: > > >

How can I select random subsets (rows!) from a data set ? If I generate simple data set > a <- data.frame(x=1:2, y = NaN, z = 2:1) > a x y z 1 1 NaN 2 2 2 NaN 1 I can select random subsets (colums) very easily using sample function: > sample(a, 2) z y 1 2 NaN 2 1 NaN I expected that using transpose of a would do the same for rows, but I am getting rather unexpected

How can I remove columns with NaN entries ? Here is my simple example: > data <- read.csv("test.csv") > xdata <- data[3:length(data)] > xs <- lapply(xdata, function(x){(x - mean(x))/sqrt(var(x))}) > x <- data.frame(xs) > x C D E F 1 -0.7071068 NaN -0.7071068 -0.7071068 2 0.7071068 NaN 0.7071068 0.7071068

I am trying to produce rather mundane output of the form e.g. pi, e = 3.14 2.718 The closest result I achieved so far with print() is: > print (c(pi, exp(1)), digits = 3) [1] 3.14 2.72 > print(c("pi, e =", pi, exp(1)), digits = 3) [1] "pi, e =" "3.14159265358979" "2.71828182845905" I understand that c() promotes floats to strings and

Greetings - I'm finally finished review, here's what I heard: ============ from Tobias Verbeke: anthony.rossini@novartis.com wrote: > Greetings! > > After a quick look at current programming tools, especially with regards > to unit-testing frameworks, I've started looking at both "butler" and > "RUnit". I would be grateful to receieve real

Apparently row names are dropped when I extract single column from a data frame. Why this behaviour ? > y <- as.matrix(df[,1:2]); length(row.names(y)) [1] 324 > y <- as.matrix(df[,1:1]); length(row.names(y)) [1] 0 Best regards, Ryszard

Full_Name: Ryszard Czerminski Version: 1.8.1 OS: GNU/Linux Submission from: (NULL) (205.181.102.120) prcomp(..., scale = TRUE) does not work correctly: $ uname -a Linux 2.4.20-28.9bigmem #1 SMP Thu Dec 18 13:27:33 EST 2003 i686 i686 i386 GNU/Linux $ gcc --version gcc (GCC) 3.2.2 20030222 (Red Hat Linux 3.2.2-5) > a <- matrix(rnorm(6), nrow = 3) > sum((scale(a %*% svd(cov(a))$u, scale

Full_Name: Peter Gedeck Version: R1.6.2 and R1.7.0 OS: Windows XP Submission from: (NULL) (194.191.169.72) Hello, I marked the bug report Private, as I don't want my email address on the web server. The problem that I found is best explained using an example. index <- 1:6 cluster <- c(1,1,1,2,2,3) tapply(index,cluster,sample) gives $"1" [1] 2 1 3 $"2" [1] 4 5

Any ideas why read.table complains about not correct number of elements in line while readLine/strsplit indicate that all lines have the same number of elements ? R > tbl <- read.table('tmp', header = T, sep = '\t') Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, : line 32 did not have 27 elements > lines <-

Hello All, I am having an issue with AAF and sorting results of a search. Right now, I have results being split onto pages of 10. The results are being sorted alphabetically, but not across multiple pages - it''s just sorting the 10 it pulls down on each page. I noticed another post from April regarding this same issue (http://www.ruby-forum.com/topic/62993#66934) where the issue was

Hey .. maybe someone got a hint for me .. I''ve got a problem with the sorting of search results .. strangely enough, the sorting is fine on my mac development environment, but not okay on my linux live system. I got a field in my index called hierarchy. It contains long strings like: Living together > Family > Brother-In-Law Living together > Family > Marriage > Wife

I want to start R processes on multiple processors from single shell script and I want all of them to have different random seeds. One way of doing this is sleep 2 # (with 'sleep 1' I am often getting the same number) ... set.seed(unclass(Sys.time())) Is there a simpler way without a need to sleep between invoking different R processes ? Ryszard

Hi I think this is a very easy question but here goes: I want to sort my results by a boolean field and then by score, I thought this would be a default configuration but apparently not. sort_fields = [] sort_fields << Ferret::Search::SortField.new(:sponsored, :reverse => :true) that is my current code, how do iu alter it so that the results are then sorted by highest score first?

Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs. With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model,