similar to: Please help -- Converting a 2D matrix to 3 columns for graphical representation

I have this function and want to run it parallel with different sets of data. Using SNOW and clusterApplyLB. system.time(out <- mclapply(cData, plotGraph)) #each cData contains 100X6000 doubles system.time(out <- mclapply(cData2, plotGraph)) system.time(out <- mclapply(cData3, plotGraph)) system.time(out <- mclapply(cData4, plotGraph)) system.time(out <- mclapply(cData5,

Dear users, I have for the moment a function which looks for the best correlation for each file I have in my correlation matrix. I'm working on a list.files. Here's the function: get.max.cor <- function(station, mat){ mat[row(mat) == col(mat)] <- -Inf which( mat[station, ] == max(mat[station, ],na.rm=TRUE) ) } If I have a correlation matrix like this

Hi useRs, I'm trying for the first time to use a sem. The model finally runs, but gives a warning saying : "In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. " I found in R-help some posts on this warning, but my attemps to modify the code didn't change

I believe the problem is here: cor1 <- cor(x1, y1, method="spearman") cor2 <- cor(x2, y2, method="spearman") The x's and y's are not looked for in data (i.e. NSE) but in the environment where the function was defined, which is standard evaluation. Change the above to: cor1 <- with(d, cor(x1, y1, method="spearman")) cor2 <- with(d, cor(x2, y2,

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Hi, I wish to calculate the characteristic path length of a graph. Is average.path.length(g) in 'igraph' the same as calculating the characteristic path length of the graph? Thanks, Kumar -- View this message in context: http://r.789695.n4.nabble.com/Characteristic-Path-length-calculation-tp3398048p3398048.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have a huge table with 134,000 entries. I want to choose only rows 60,000 to 90,000. Please help me on how to do this. V1 V2 V3 V4 1 31.10267 168.3204 41 0.4 2 31.19941 168.1488 41 0.4 3 31.29580 167.9764 41 0.4 4 31.39183 167.8034 41 0.4 5 31.48750 167.6297 41 0.4 6 31.58280 167.4553 41 0.4 7 31.67774 167.2803 41 0.4 8 31.77229 167.1045 41 0.4 9 31.86648 166.9281

Hi, I am plotting a graph with kamada.kawai layout. But I am getting direction arrows in the graphs. Please let me know how to avoid this. g <- watts.strogatz.game(1, 2000, 5, 0.4) comps <- clusters(g)$membership colbar <- rainbow(max(comps)+1) V(g)$color <- colbar[comps+1] png(filename) plot(g, layout=layout.kamada.kawai, vertex.size=2, vertex.label=NA) dev.off() Please find

In barplot for degree distribution x-axis is not seen. See the example below > g = barabasi.game(500, 0.4) > dd1 = degree.distribution(g) > plot(dd1, xlab="degree", ylab = "frequency") whereas barplot doesnot have any x-axis > barplot(dd1, xlab = "degree", ylab = "frequency") Please see the figures attached.

Hi. I have a 925 by 925 correlation matrix corM. I want to identify all variables that have correlation greater than 0.9. Can anyone suggest an "R way" of doing this? Thank you. -- View this message in context: http://www.nabble.com/search-through-a-matrix-tp23153538p23153538.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am plotting degree distribution of a graph using the function, library(igraph) dd1 = degree.distribution(G) plot(dd1, xlab = "degree", ylab="frequency") I would like to plot the mean of the distribution as a vertical line in the attached plot. Please let me know how to do this. Thanks, Kumar http://r.789695.n4.nabble.com/file/n3334375/cdata3_dd.png cdata3_dd.png --

Hi! All, I have 2 correlation matrices of 4000x4000 both with same row names and column names say cor1 and cor2. I have extracted some information from 1st matrix cor1 which is something like this: rowname colname cor1_value a b 0.8 b a 0.8 c f 0.62 d k 0.59 - - -- -

Hello all, I believe this can be done using bootstrap, but I am wondering if there is some other way that might be used to tackle this. #Let's say I have two pairs of samples: set.seed(100) s1 <- rnorm(100) s2 <- s1 + rnorm(100) x1 <- s1[1:99] y1 <- s2[1:99] x2 <- x1 y2 <- s2[2:100] #And both yield the following two correlations: cor(x1,y1) # 0.7568969 (cor1) cor(x2,y2)

R-Experts, Here below my R code working without error message but I don't get the results I am expecting. Here is the result I get: [1] "All values of t are equal to 0.28611928397257 \n Cannot calculate confidence intervals" NULL If someone knows how to solve my problem, really appreciate. Best, S ######################################################### # Difference in Spearman

This might be easy but I'm very new to R and this question doesn't seem to have any nice keywords that bring up relevant search results when I search the CRAN search engine. Therefore, I'll plead (as I have in the recent past) Newbie status. I have a data frame with two factors (Factor 1 and 2) which together specify another unique level. I want to create a third factor in the

Hi I have a couple of functions that work on the object created by another R command and then print out or summarise the results of this work. The main function is defined as: hotelling.t <- function(obj) { #internal commands } I then have print.hotelling.t() that takes the list returned by hotelling.t and prints it with some extra significance calculations, formatting, etc. I want to

Hello all, I have a data file table.txt which i have attached. I am trying to pass the columns as arguments to a function "totnorm" where i am displaying a total normalization plot. The function is given below: totnorm<-function(x,y){scale<-sum(x)/sum(y);xlab<-colnames(x);ylab<-colnames(y);x1<-x[[1]];y1<-scale*y[[1]];plot(x1,y1,xlab=xlab,ylab=ylab,col=6, col.lab=4);}

Hello all, I have a data file table.txt which i have attached. I am trying to pass the columns as arguments to a function "totnorm" where i am displaying a total normalization plot. The function is given below: totnorm<-function(x,y){scale<-sum(x)/sum(y);xlab<-colnames(x);ylab<-colnames(y);x1<-x[[1]];y1<-scale*y[[1]];plot(x1,y1,xlab=xlab,ylab=ylab,col=6, col.lab=4);}

Hi, I''m trying to convert an older Rails application to use Ajax. I''ve already moved my edit and show actions so that, instead of displaying as separate pages, they appear in a <div> on my list view. However, I have two problems: 1) When the user saves the changes, I want to put all the ajax changes in an RJS template. However, form_remote_tag :url =>{:action =>

I get it now, I can't believe I didn't understand that before. Thank you all for your help! - John On 4/8/06, Chris Lattner <sabre at nondot.org> wrote: > > On Sat, 8 Apr 2006, Jim Laskey wrote: > > If you look at the stoppoint calls you'll see that you can find the line > > number and if you follow the compile unit argument on the call you will > find >