similar to: removing for loop

Dear all, First I apologize if my question is quite simple, but i'm very newbie with R. I have vectors of the form v = c(1,1,-1,-1,-1,1,1,1,1,-1,1) (longer than this one of course). The elements are only +1 or -1. I would like to calculate : - the frequencies of -1 occurences after 2 consecutives -1 - the frequencies of +1 occurences after 2 consecutives +1 It looks probably something like

Dear Rusers, I would like some comments about the following results (under R-2.2.0) > m = matrix(1:6 , 2 , 3) > m [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 > z1 = m[(m[,1]==2),] > z1 [1] 2 4 6 > is.matrix(z1) [1] FALSE > z2 = m[(m[,1]==0),] > z2 [,1] [,2] [,3] > is.matrix(z2) [1] TRUE Considered together, I'm a bit surprised about

Dear Rusers, My question is about "recode variables". First, i'd like to say something about the idea of recoding: My dataset have three variables:type,soiltem and airtem,which means grass type, soil temperature and air temperature. As we all known, the change of air temperature is greater than soil temperature,so the values in those two different temperaturemay represent different

R and DDE (Dynamic Data Exchange) Dear Rusers, I run an application (not mine) which acts as a DDE server. I would like to use R to get data from this application, say once per minute, and do some processing on it. I didn't find much info on the R DDE abilities, apart the tcltk2 package in which I will try to go deeper. I would be very thankful for any info, pointer or advice about the

I am struggling with R code optimization, a recurrent topic on this list. I have three arrays, say A, B and C, all having the same number of columns. I need to compute an array D whose generic element is D[i, j, k] <- sum_n A[i, n]*B[j, n]*C[k, n] Cycling over the three indices and subsetting the columns won't do. Is there any way to implement this efficiently in R or should I resign to

Dear Rs: outer(1:3, 1:3, function(df1, df2) qf(0.95, df1, df2)) I compare this F distribution results with the table, the answers were perfect. But I need to see for chi-sqaured distribution. When I employed the similar formula outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1, df2)) , I am getting unexpected results. I need to see the following values: p=0.750 ..... 1 1.323

Dear R list, I calculated a two-sided p values according to 2*(1-pnorm(8.104474)), which gives 4.440892e-16. However, it appears to be 5.30E-16 by a colleague and 5.2974E-16 from SAS. I tried to get around with mvtnorm package but it turns out to be using pnorm for univariate case. I should have missed some earlier discussions, but for the moment is there any short answer for a higher

Hi, I would like to generate a correlation matrix with a particular structure. For example, a 3n x 3n matrix : A_(nxn) aI_(nxn) bI_(nxn) aI_(nxn) A_(nxn) cI_(nxn) aI_(nxn) cI_(nxn) A_(nxn) where - A_(nxn) is a *specified* symmetric, positive definite nxn matrix. - I_(nxn) is an identity matrix of order n - a, b, c are (any) real numbers Many attempts have been unsuccessful because a

Dear R helpers: Thanks for the previous reply. I am using Friedman racing test. According the the book "Pratical Nonprametric Statistic" by WJ Conover, after computing the statistics, he suggested to use chi-squared or F distribution to accept or reject null hypothesis. After looking into the source code, I found that R uses chi-sqaured distribution as below: PVAL <-

Dear R-helpers, I apologize for this certainly simple question. I have the following R lines : > m = matrix(1:12 , 3 , 4); > m [,1] [,2] [,3] [,4] [1,] 1 4 7 10 [2,] 2 5 8 11 [3,] 3 6 9 12 > m1 = subset(m , m[,2]>=5); > m1 [1] 2 3 5 6 8 9 11 12 but in fact I would appreciate m1 to be also a matrix, and thus would like to get :

Hi, I apologize for this repeat posting, which I first posted yesterday. I would appreciate any hints on solving this problem: I have two matrices A (m x 2) and B (n x 2), where m and n are large integers (on the order of 10^4). I am looking for an efficient way to create another matrix, W (m x n), which can be defined as follows: for (i in 1:m){ for (j in 1:n) { W[i,j] <-

Hi, I want to make the following array of numbers: -3 -3 -3 -3 -3 -3 -3 -2 -2 -2 -2 -2 -2 -2 ... 3 3 3 3 3 3 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ... -3 -2 -1 0 1 2 3 (3 would be N, a painful example to type number). Here is my dirty attempt to do it, > N<-3 > > x<-c(-N:N) > > rj<-rbind(matrix(outer(matrix(1,1,2*N+1),x),nrow=1),rep(x,2*N+1)) > It

Dear R helpers, I am having trouble with an array inside a loop. I wish to accumulate the results of a function in an array within the function, over several loops of a program outside the function. The problem is that the array seems to re-set at every entry to the function. Here is an example, so you can see what I mean.

Dear all, I have a population with three age-classes, at time t=0 the population is: n.zero <- c(1,0,0) I have a transition matrix A which denotes "fertility" and "survival": A <- matrix(c(0,1,5, 0.3,0,0, 0,0.5,0), ncol=3, byrow=TRUE) To obtain the population at t=1, I calculate: A %*% n.zero To obtain the population t=2, I calculate: A %*% (A %*% n.zero) ... and so

Dear, I am looking for the simplification of a formula to improve the calculation speed of my program. Therefore I want to simplify the following formula: H = sum{i=0..n-1 , [ sum {j=0..m-1 , sqrt ( (Ai - Bj)^2 + (Ci - Dj)^2) } ] } where: A, C = two vectors (with numerical data) of length n B, D = two vectors (with numerical data) of length m sqrt = square root Ai = element of A with index

Dear all, I have a question about the glmulti package. I want to include some variables in all models. To that end I applied the wrapper function as shown in the examples (http://www.inside-r.org/packages/cran/glmulti/docs/glmulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...)

hi all, sorry for this basic question, I think I know I should use ?apply, but it is really confusing me... I want to create a matrix by comparing two vectors. Eg: test<-seq(1:10) fac<-c(3,6,9) and i want to end up with a 10*3 matrix with a boolean that tests if test<fac, so something like: 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 I can't find the solution

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

Suppose I have a Variance-covariance matrix A. Is there any fast way to calculate correlation matrix from 'A' and vice-versa without emplying any 'for' loop? [[alternative HTML version deleted]]

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate