similar to: A one-liner to create a 3-dim array

i'm somehow embarrassed to even ask this, but is there any built-in method for doing this: my_list <- list() my_list[[1]] <- matrix(1:20, ncol = 5) my_list[[2]] <- matrix(20:1, ncol = 5) now, knowing that these matrices are identical in dimension, i'd like to unfold the list to a 2x4x5 (or some other permutation of the dim sizes) array. i know i can initialize the array, then

Hello, I want to find the roots of an equation in two variables. I am aware of the uniroot function, which can do this for a function with a single variable (as I understand it...) but cannot find a function that does this for an equation with more than one variable. I am looking for something implementing similar to a Newton-Raphson algorithm. Thanks. -- Bill Shipley North American Editor for

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

Hi, I have a data file, certain lines of which are character fields. I would like to skip these rows, and read the data file as a numeric data frame. I know that I can skip lines at the beginning with read.table and scan, but is there a way to skip a specified sequence of lines (e.g., 1, 2, 10, 11, 19, 20, 28, 29, etc.) ? If I read the entire data file, and then delete the character

Hi, I deal with 3D array say: , , 1 [,1] [,2] [,3] [,4] [,5] [1,] 1 4 7 10 13 [2,] 2 5 8 11 14 [3,] 3 6 9 12 15 , , 2 [,1] [,2] [,3] [,4] [,5] [1,] 16 19 22 25 28 [2,] 17 20 23 26 29 [3,] 18 21 24 27 30 , , 3 [,1] [,2] [,3] [,4] [,5] [1,] 31 34 37 40 43 [2,] 32 35 38 41 44 [3,] 33 36

Dear UseRs!, Everything about UseR! 2010 was terrific! I really mean "everything" - the tutorials, invited talks, kaleidoscope sessions, focus sessions, breakfast, snacks, lunch, conference dinner, shuttle services, and the participants. The organization was fabulous. NIST were gracious hosts, and provided top notch facilities. The rousing speech by Antonio Possolo, who is the chief

Dear R-users I would like to integrate something like \int_k^\infty (1 - F(x)) dx, where F(.) is a cumulative distribution function. As mentioned in the "integrate" help-page: integrate(dnorm,0,20000) ## fails on many systems. This does not happen for an adaptive Simpson or Lobatto quadrature (cf. Matlab). Even though I am hardly familiar with numerical integration the implementation

Hi, I would like to solve a system of coupled ordinary differential equations, where there is a delay (time lag) term. I would like to use the "lsoda" function "odesolve" package. However, I am not sure how to specify the delay term using the syntax allowed by odesolve. Here is an example of the kind of problem that I am trying to solve: > library(odesolve)

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, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

If I have a two column data frame like: > dat <- cbind("x"=c(1:100),"y"=c(100:1)) How can I create an array that splits every ten rows of that data frame into a third dimension of an array so that: > newarray[,,1] ,,1 x y 1 100 2 99 3 98 ... ... 10 91 ,,2 x y 11 90 12 89 ... ... ... Thanks. [[alternative HTML version deleted]]

Hi, I would like to know how to capture the console output from running an algorithm for further analysis. I can capture this using capture.output() but that yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2]

Hello, A simple question, although I can't find an answer via my google/forum search: I have a 4-dimensional array; call it A[1:M,1:N,1:P,1:Q]. I have a vector x that is N by 1. I would like to "quickly" add x to the 2nd dimension of A; in other words, I want a quicker way of doing the following: for (m in 1:M) { for (p in 1:P) { for (q in 1:Q) { A[m,,p,q] =

Hi, Currently, the "base" has colSums, colMeans. It seems that it would be useful to extend this to also include colMin, colMax (of course, rowMin and rowMax, as well) in order to facilitate faster computations for large vectors (compared to using apply). Has this been considered before? Please forgive me if this has already been discussed before. Thanks, Ravi Ravi Varadhan, Ph.D.

Hi, I apologize for the previous posting, where the message was not formatted properly. Here is a better version: I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi, I have noticed that there is a change in the use of ellipses or . in R versions 2.6.1 and later. In versions 2.5.1 and earlier, the . were always at the end of the argument list, but in 2.6.1 they are placed after the main arguments and before method control arguments. This results in the user having to specify the exact (complete) names of the control arguments, i.e. partial matching is

Dear R group, I have a data frame which contains data on preferences on 7 items (ranks 1 through 7) listed by each participant. I would like to tabulate this in a 7x7 table where the rows would be the items and the columns would be the number of times that item received a particular rank. I tried doing this by creating a matrix by "rbind"ing each vector obtained using

If AIC is giving you a model that is too large, then use BIC (log(n) as the penalty for adding a term in the model). This will yield a more parsimonious model. Now, if you ask me which is the better option, I have to refer you to the huge literature on model selection. Best, Ravi [[alternative HTML version deleted]]

Hi, I am running a simulation study with the survival::coxph. Some of the simulations result in problematic fits due to flat partial likelihood. So, you get the warning message: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, ... : Loglik converged before variable 2 ; beta may be infinite. How can I keep track of the simulations which yield any kind of