similar to: Selective subsetting

Hi, I am new to R...a recent convert from SAS. I have a dataset that looks like this: SEQ A1 A2 A 532.5 554.5 B 25.5 35.5 C 265.2 522.2 D 245.55 521.56 E 546.52 141.52 F 243.25 32.56 G 452.55 635.56 H 15.14 16.54 I 543.4 646.56 J 54.4 654.5 K 646.5 64.54 L 645.4 614.46 M 646.54 634.46 I want to make a histogram

Hi, I am making a dendrogram with 180 terminal values. Whether I keep it horizontal or vertical, it gives a 'squished' graph that refuses to be stretched beyond the window size and I cannot read the labels. (I'm using hclust and then plot to make the tree.) Is there a way to stretch the graph in R, or by exporting it somehow, so that I can read the 180 values and see what the results

Hi, I'm using R on Windows and upgraded the computer memory to 4GB, as R was telling me that it is out of memory (for making heatmaps). It still says that the maximum memory is 1024Mb, even if I increase it using memory.limit and memory.size. Is there a way to permanently increase R's memory quota to 4GB? Please help. Many thanks, -DS. [[alternative HTML version deleted]]

I'm looking to create a correlation matrix, but I have already obtained the correlations, which are stored in a vector. (Basically, I'm running a simulation which requires a correlation matrix, but I am simulating the various correlations.) My aim is to create a function that can take the vector, and fit the values into their respective locations in a correlation matrix. (The correlations

> I am trying to produce a heatmap of pairwise correlations, but since the matrix is > symmetric, I only need either the upper or the lower triangle. I have scoured the > web and R documentation, but I have not been able to find a way to produce such a > figure. Is there a simple way to produce a heat map with only the part above or > below the diagonal? You might want to check

Hi, Sorry if this is a repost. I searched but found no results. I am wondering if it is an easy way to construct the following matrix: r 1 0 0 0 r^2 r 1 0 0 r^3 r^2 r 1 0 r^4 r^3 r^2 r 1 where r could be any number. Thanks. Wen [[alternative HTML version deleted]]

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

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 a correlation matrix of size 100 x 100 and would like to extract the diagonal matrix from it. I have used the for loop to store tha correlation values of the diagonal matrix. Is there a 'R way' of doing this? Thanks in advance. Kind regards, Ezhil

I've a vector of pairwise correlations in the order low-index element precedes the high-index element, say: corr(1,2)=0.1, corr(1,3)=0.2, corr(2,3)=0.3, corr(3,4)=0.4 How can I construct the corresponding correlation matrix? I tried using the "combn"-function in "combinat" package: library(combinat) combn(c(0.1,0.2,0.3,0.4),2) , but to no avail... Thank you for your

from ?eigen symmetric: if 'TRUE', the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle is used. If 'symmetric' is not specified, the matrix is inspected for symmetry. I think that could mislead a naive reader as it suggests that, with symmetric=TRUE, the result of eigen() (vectors and values) depends only on

Two questions about matrix indexing: Is is correct that V <- V[lower.tri(V, diag=TRUE)] returns the lower triangular of matrix V, that is: all elements above diagonal are set to zero? I understand that the triangle of matrix elements of V for which lower.tri is TRUE are returned while the others (above diagonal) are set to zero (or NA ???). If D and B are vectors of logicals, what

I have output from a program which produces a distance matrix I want to read into a clustering program in R. The output is a .txt file and is 'almost' lower triangular in the sense that it is just the triangle below the diagonal. So for example a 4-by-4 distance matrix appears as, 1 2 3 4 5 6 i.e. it looks like a lower triangular of a 3-by3. I thought I might be able

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Hi, I want to compute the lower triangle of a square matrix (optionally, sans diagonal). With for() loops I can do something like this: ## 5 by 5 matrix rtn for (j in 1:5) { for (k in 1:j) { if (j != k) { ## optional rtn[j, k] <- my.func(j, k) } } } I'd like to do this with apply(). Is there some way I can do this kind of 'short-circuit'? Thanks, Mark Wilkinson

Dear All, I have a correlation matrix say 'M' (4000x4000) for 4000 genes and I want to subset it to 'N' (190x190) for 190 genes. The list of those 190 genes are in variable 't'. So the idea is to read the names of genes from variable 't' and subset the matrix M accordingly. Any thoughts are welcome! Best Lee [[alternative HTML version deleted]]

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

Dear all, I have an n x n matrix of p-values. The matrix is symmetrical, as it describes the "each against each" p values of correlation coefficients. How can I best correct the p values of the matrix? Notably, the total number of the tests performed is n(n-1)/2, since I do not test the correlation of each variable with itself. That means, I only want to correct one half of the matrix,

Dear R-helpers, an rgl-ers in particular, what is the easiest way to plot a section of a plane in 3D, that is given by the xyz coordinates of the outline? Suppose I have a polygon - which I know for sure is a set of coordinates on the same plane. One method I found is to use surf.tri from the geometry package, and then plot the triangles with rgl.triangles. This method is not perfect though,