similar to: pairwise difference operator

Dear all, I''m trying to write a function, that will take as an argument, some aligned genome sequences, and using a sliding window, do pairwise comparisons of sequence similarity. Coding the sliding window I think I can manage but what I''m trying to get to grips with is getting it so as every pairwise comparison is made, no matter how many genomes are added, from 3 to N. So if

Dear R users, I have a data generated as the following, dat <- data.frame(matrix(sample(24), nrow=4)) dimnames(dat) <-list(rownames=c('g1','g2','g3','g4'), colnames=c("A_CH1","A_CH2","B_CH1","B_CH2","C_CH3","C_CH3")) » dat A_CH1 A_CH2 B_CH1 B_CH2 C_CH3 C_CH3 g1 16 24 7 9 14

Carlos: Te agradezco mucho tu rápida respuesta y mucho me apena haber planteado tan mal el problema. Porque la matriz en realidad es: g1 g2 g3 g4 g5 g6 g7 g1 0 18 13 16 11 12 15 g2 18 0 25 13 22 16 10 g3 13 25 0 28 23 13 25 g4 16 13 28 0 6 7 3 g5 11 22 23 6 0 18 17 g6 12 16 13 7 18 0 8 g7 15 10 25 3 17 8 0 Entonces cada cantidad debe conservar la pertenencia al grupo

Chris Mauritz wrote: > > The 32bit PPC release is for the older G3/G4 macs, no? I've got a > pile of unused G4 400-800mhz powermacs just sitting around The ppc32 distro will work on any NewWorld G3, G4 based machine ( and 32bit ibm power, like the older rs6k's ) - it _also_ works fine on the MacMini and G4 Xserve's. With some common sense tweaks, a G4 450Mhz is a very

Hi, I have a data.frame with 294 columns and 211 rows. I am calculating correlations between all pairs of columns (excluding column 1) and based on these correlation values I delete one column from any pair that shows a R^2 greater than a cuttoff value. (Rather than directly delete the column all I do is store the column number, and do the deletion later) The code I am using is: ndesc

Dear R helpers,   Following is a R script I am using to run the Fast Fourier Transform. The csv files has 10 columns with titles m1, m2, m3 .....m10.     When I use the following commands, I am getting the required results. The probelm is if there are 100 columns, it is not wise to define 100 commands as fk <- ONS$mk and so on. Thus, I need some guidance to write the loop for the STEP A and

Does the "legend" command work in the windows version of R? Here is the command that I'm using: legend(1,1,legend=c("G1","G2","G3","G4","G5","G6"),pch=array(1:6,dim=c (1,6))) When I use this on my existing graph in a window I get a solid black box at the lower left. If I use this with the postscript driver my output

Hi, I thought I remember seeing somewhere on the Asterisk website a card that had 16 ports fxo or fxs, that was user selectable with straps on the card. Am I going crazy, I can't seem to find it now. -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20040930/86c8ae69/attachment.htm

I'm trying to sort a DATAFRAME by a column "ID" that contains alphanumeric data. Specifically,"ID" contains integers all preceeded by the character "g" as in: g1, g6, g3, g19, g100, g2, g39 I am using the following code: DATAFRAME=DATAFRAME[order(DATAFRAME1$ID),] and was hoping it would sort the dataframe by ID in the following manner g1, g2, g3, g6, g19,

Hello all. I have two data frames. Group Start End G1 200 700 G2 500 1000 G3 2000 3000 G4 4000 6000 G5 7000 8000 and Pos C0 C1 200 0.9 0.6 500 0.8 0.8 800 0.9 0.7 1000 0.7 0.6 2000 0.6 0.4 2500 1.2 0.8

I have a system with two 4 port T1 cards, with 5 PRI's configured. Each PRI is configured as an individual PRI and belongs to it's own group (groups 1-5) This system is handling roll-over from another system, where any error in processing the call on that system results in it being sent here. This mainly results in all calls resulting in a busy being sent for retry here. I then

Hello, When I run LLC with option "-O0 -march=sparc" on following testcase, fast register allocator crashes with "UNREACHABLE executed" error. LLC generates code successfully with other standard register allocators available. $ cat call.ll define void @test() nounwind { entry: %0 = tail call i32 (...)* @foo() nounwind tail call void (...)* @bar() nounwind ret void }

I have been struggling with this problem for a while and I hope someone could help me. Or if someone could point me to a section in the manual I would be grateful. x <- "my" plot(1:10, main=expression(paste( x, Delta, "values" ))) Q : How do I get the title to say "my (triangle symbol) values" ? The following trial-and-error produced mainly errors :

Dear all, I am having trouble trying to install R-1.8.1 on a Sun Solaris 8 (Generic_108528-23 version) machine. The configuration was successful but make check fails. I traced the the problem to the plot() function. > 1 + 1 [1] 2 > capabilities() jpeg png tcltk X11 GNOME libz http/ftp sockets TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE

Hello R guru's I am a newbie to R, In my research work I usually generate a lot of ELISA data in form of absorbance values. I ususally use Excel to calculate the concentrations of unknown, but it is too tedious and manual especially when I have 100's of files to process. I would appreciate some help in creating a R script to do this with minimal manual input. s A1-G1 and A2-G2 are

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

I have the following code that perform hiearchical clustering and plot them in heatmap. __ library(gplots) set.seed(538) # generate data y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep=""))) # the actual data is much larger that the above # perform hiearchical clustering and plot heatmap test <- heatmap.2(y)

Dear all, I have a matrix X with 47 lines and say 500 columns - values are in {0,1}. I'd like to compare lines. For that, I first did: for (i in 1:(dim(X)[1]-1)) for (j in (i+1):dim(X)[1]) { Y <- X[i,]+Y[j,] etc. but, since it takes a long time, I would prefer avoding loops; for that, my first idea was to add this matrix: X1=X[,rep(1:46,46:1)] to this one: res=NULL for (i in

Could some kindly tell me if I am supposed to be getting the same test statistic value with var.equal=TRUE and var.equal=FALSE in t.test ? set.seed(1066) x1 <- rnorm(50) x2 <- rnorm(50) t.test(x1, x2, var.equal=FALSE)$statistic # 0.5989774 t.test(x1, x2, var.equal=TRUE)$statistic # 0.5989774 ??? Here are my own calculations that shows that perhaps the result when var.equal=TRUE is

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