similar to: functions applied to two vectors

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

Hi, Forgive a very basic question... I need to take two lists-of-lists, and apply a function to each pair of elements in the lists to return a single list... For example l1 <- list(1:5,6:10,2:15) l2 <- list(1:8,4:12,1:19,4:20) I could easily do an lapply across each of them, but is there a function that does a sort-of pairwise-apply across both together? Does anybody know of a good

Hello, I want to do a cluster analysis with my data. The problem is, that the variables dont't consist of single value but the entries are pairs of values. That lokks like this: Variable 1: Variable2: Variable3: . . . (1,2) (1,5) (4,2) (7,8) (3,88) (6,5) (4,7) (12,4) (4,4) . . . .

Akemi-san has suggested that I turn the following post into a wiki article. (The article would be a bit better organized, since if one bothers to follow the thread, you'll see that I found that one can use the pcf2vpnc in Dag's rpm without worrying about having a plain text password.) http://www.centos.org/modules/newbb/viewtopic.php?topic_id=18444&forum=38&post_id=67625 If

Hi, I calculating the output of a function when applied to pairs of row from a single matrix or dataframe similar to how cor() and pairs() work. This is the code that I have been using: pairwise.apply <- function(x, FUN, ...){ n <- nrow(x) r <- rownames(x) output <- matrix(NA, nc=n, nr=n, dimnames=list(r, r)) for(i in 1:n){ for(j

Hello R experten I have just written a little function to calculate all pairwise combinations of two vector arguments: > pair(c(1,2,3),c(7,8)) [,1] [,2] [1,] 1 7 [2,] 1 8 [3,] 2 7 [4,] 2 8 [5,] 3 7 [6,] 3 8 > I want to generalize this to any number of arguments, for example, <fantasy> > ntuple(c(1,2,3),c(7,8),c(14,15)) [,1] [,2]

Hello RoR! I''m building a rails app for hosting multi-page surveys. I set up questionnaire, question, and question_component models. Now I''m initializing them with some data. survey.rb q1 = Question.new "q1" q1.quote "Welcome to a test of the new interviewing system." q2 = Question.new "q2" q2.quote "Do you like pie?"

hi folks, after some adventures for setup an icecast server , the system is now running, but there where some questions open: 1. ) i use ices as streamer for the server. in some cases (i dont know why) is ices break after an while of streaming. ices re-encode the mp3 files with lame 3.88 to stream them. there is no hint why the stream is broken in the log files. after an restart of ices all looks

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

What's the difference between t.test(x, y) and pairwise.t.test()? Is it just that the former takes two vectors, whereas the latter takes a vector and a factor?

Hi, If I set the p.adjust="none", does it meant that the output p values from the pairwise.t.test will be the same as those from individual t.tests (set var.equal=T, alternative="t")? I actually got different p values from the two tests. See below. Is it supposed to be this way? Thanks Johnny > x [1] 61.6 52.7 61.3 65.2 62.8 63.7 64.8 58.7 44.9 57.0 64.3 55.1 50.0 41.0

Dear R-help, I have a question about pairwise.t.test and adjustment for multiple comparisons for paired data points. I have the following data: n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y", "y", "y", "y", "y",

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

# Hello, # I have a list with 6 categories and with different numbers of rows. # I would like to change each of them into a unique data frame in order to match # values with other data frames and perform some calculations. # Or I could make each category or list element have the same number of rows and create one large data.frame. # below is a creation of a sample list # I apologize for the

There was a BioConductor thread today where the poster wanted to find pairwise difference between columns of a matrix. I suggested the slow solution below, hoping that someone might suggest a faster and/or more elegant solution, but no other response. I tried unsuccessfully with the apply() family. Searching the mailing list was not very fruitful either. The closest I got to was a cryptic chunk

Dear *, I would like to obtain for each factor of my anova model the "response variable vs factor" plot with means and 95% Tukey HSD intervals. I would appreciate any information on how to do that. Cheers -------------------------------------------------------------------- Max MANFRIN Tel.: +32 (0)2 650 3168 IRIDIA - CoDE, CP 194/6

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20081112/618073fe/attachment.pl>

I am writing a little abstraction for a series of tests. For example, I am running an anova and kruskal.test on a one-factor model. That isn't a particular problem, I have an interface like: my.function <- function(model,data) { print(deparse(substitute(data))) a <- anova(lm(formula,data)) print(a) if(a$"Pr(>F)"[1] < 0.05) { pairwise.t.test(???) }

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Hi , I am new to R . I am facing difficulty how to make pairwise comparisions. For example. I have a file which looks like below a b c d x 3 6 7 6 y 7 8 6 5 z 5 4 7 8 Here I need to look for the each pairwise comparisions (ab,ac,ad,bc,bd,cd for each row) For instance ,looking at first row, for x i need to look for ab values and take the min(3,6) >5 ,if its satistfies the count should be