similar to: matrix manipulations

Hi I get a matrix with the columns named like "X13 X22 X1 X14 ...", i.e. not successively. That has it's good reasons, but now how can I rename the columns to "X1 X2 X3 ..."? Thanks a lot, David

Hi I am trying to replicate a vector in n rows for comparison purposes with another matrix. foo <- c(1,2,3) bar <- rbind(foo,foo) # does the trick for 2 rows bar <- rbind(rep(foo,2)) # does something else How do I generate a matrix with all rows=foo without writing 'foo' n times as arg? Thanks, David

Hi all I am very astonished that R generates a "syntax error" when I want to split up a line with a backslash, which usually works in any shell script. R itself generates the "+" symbols at the beginning of following lines in a splitted line so I've tried with them as well, but also without success. Searching in the h-help archive did not reveal any answer either.

Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

Hi I have a matrix of type "character" (strangly) and am trying to apply the function "aggregate" to it, but unfortunately without success. It seems to me that aggregate would work, if I only could get rid of the quotation marks around each item. So for a very simple example which looks as my actual data look (of course here I put the quotation marks on purpose, but I have

Dear all! After hours of trying around, I gave up: I have a 2-dimensional array, and I know how to split it into its rows and how to get the mean for every row using 'sapply'. But what I want is to calculate the mean over the first n rows, and then the second n rows, etc., so that I get a vector like: v == mean1(row 1:5), mean2(row6:10),... (trivial, you might say. I find it rather

Dear all, I would like to use the biological symbols for male and female as plotting symbols in a scatterplot (ideally filled and non-filled). R does not seem to have these symbols using pch= in plot() nor are they implemented via expression() or at least I did not find them. I found that the symbols are e.g. available in the wasysym and the marvosym package in LaTeX. I have coded two very rough

Dear all, I would like to use the biological symbols for male and female as plotting symbols in a scatterplot (ideally filled and non-filled). R does not seem to have these symbols using pch= in plot() nor are they implemented via expression() or at least I did not find them. I found that the symbols are e.g. available in the wasysym and the marvosym package in LaTeX. I have coded two very rough

Hi, there! I finally became a disciple of 'R', after having lost years of my life handling data with a popular, rather wide-spread spreadsheet-software. Now I want to plot the results of many runs of my simulation over time, so that the means +/- Standard error are on the y-axis, and time on the x-axis. I have tried 'boxplot', with timesteps as the grouping variable, but did

I'm using an SVM as I've seen a paper that reported extremely good results. I'm not having such luck. I'm also interested in ideas for other approaches to the problem that can also be applied to general problems (no assuming that we're looking for spirals). Here is my code: library(mlbench) library(e1071) raw <- mlbench.spirals(194, 2) spiral <-

Dear all, this is a rather basic question; i am not sure how to structure my data well: I want to extraxt various measures from my raw-data. These measures are of different sizes, so I decided to store them in a list, like: run1 <- list(Dom = (my_vector), mean = (my_single_number)) I can do that in a for loop for 40 runs, ending up with 40 lists: run1, run2, ..., run40. To have all the

Hi, Another question about graphics... Suppose I have: par(mfrow = c(4, 6)) which will set up a figure region for 24 plots. But, I actually only need 23 plots. In fact I wrote a little function, foo.plot, which will draw all the 23 plots at once with: foo.plot(x) where x is a data frame. Now, suppose I want to draw several set of these 23 plots, but want to put them all in one postscript

I am in need of someone's help in correlating gene expression. I'm somewhat new to R, and can't seem to find anyone local to help me with what I think is a simple problem. I need to obtain pearson and spearman correlation coefficients, and corresponding p-values for all of the genes in my dataset that correlate to one specific gene of interest. I'm working with mouse Affymetrix

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Recently, I was working with some lagged designs where a vector of observations at one time was used to predict a vector of observations at another time using a lag 1 design. In the work, I noticed a lot of negative correlations, so I ran a simple simulation with 2 matched points. The crude simulation example below shows that the correlation can be -1 or +1, but interestingly if you do this

I apologize for how basic a question this is. I am a Stata user who has begun using R, and the syntax differences still trip me up. The most basic questions, involving as they do general terms, can be the hardest to find solutions for through search. Assume for the moment that I have a dataset that contains seven variables: Pollution, Temp, Industry, Population, Wind, Rain and Wet.days. (This