similar to: alternate methods to perform a calculation

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,

useR's, Consider some variables and a data frame of points: x1 <- c(1,2,3) x2 <- c(3,4,5) xk1 <- seq(min(x1)-.5, max(x1)+.5,.5) xk2 <- seq(min(x2)-.5, max(x2)+.5,.5) expand.grid(xk1=xk1,xk2=xk2) xk1 xk2 1 0.5 2.5 2 1.0 2.5 3 1.5 2.5 4 2.0 2.5 5 2.5 2.5 6 3.0 2.5 7 3.5 2.5 ... 46 2.0 5.5 47 2.5 5.5 48 3.0 5.5 49 3.5 5.5 I want to restrict the data frame to only contain

This is a continuation from a previous posting of mine: The following algorithm below is what I want to accomplish: Z(xk) = Average(Yi, i belongs to Ik), where Ik contains all i such that for each j, |Xi,j - xkj?? 2. Here, j = 1, 2 and i corresponds to the elements in each X and/or xk >data x1 x2 y 1 1 2 2 2 6 3 3 12 Now, consider a second data frame or matrix (xk):

useR's, Consider the variables defined below: yvals <- c(25,30,35) x1 <- c(1,2,3) x2 <- c(3,4,5) x3 <- c(6,7,8) x <- as.data.frame(cbind(x1,x2,x3)) delta <- c(2.5, 1.5, 0.5) h <- delta/2 vars <- 3 xk1 <- seq(min(x1)-0.5, max(x1)+0.5, 0.5) xk2 <- seq(min(x2)-0.5, max(x2)+0.5, 0.5) xk3 <- seq(min(x3)-0.5, max(x3)+0.5, 0.5) xks <- list(xk1,xk2,xk3) xk <-

useR's, I am trying trying to find out if there is a faster way to do a certain computation. I have successfully used FOR loops and the apply function to do this, but it can take some time to fully compute, but I was wondering if anyone may know of a different function or way to do this: > x [1] 1 2 3 4 5 > xk [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 I want to do:

useR's, I would like to know if there is a way to avoid using FOR loops to perform the below calculation. Consider the following data: > x [,1] [,2] [,3] [1,] 4 11 1 [2,] 1 9 2 [3,] 7 3 3 [4,] 3 6 4 [5,] 6 8 5 > xk Var1 Var2 Var3 1 -0.25 1.75 0.5 2 0.75 1.75 0.5 3 1.75 1.75 0.5 4 2.75 1.75 0.5 5 3.75 1.75

I have the following problem. Consider a `classical' test which works for k .ge. 2 samples. Possible interfaces are e.g. xxx.test(x, g) x ... all data, g ... corresponding groups xxx.test(x1, ..., xk) xxx.test(list(x1, ..., xk)) etc etc. Clearly, the first and the second one are nice, but cannot be combined without making `g' (i.e., `group') a named argument. Hence, in

I'm pulling my hair (and there's not much left!) on this one. Basically I'm not getting the same result t when I "step" through the program and evaluate each element separately than when I use the outer() function in the FindLikelihood() function below. Here's the functions: Dk<- function(xk,A,B) { n0 *(A*exp(-0.5*(xk/w)^2) + B) } FindLikelihood <-

Dear readers of the list, I have a problem a comparison of two data from a vector. The comparison yields FALSE but should be TRUE. I have checked for mode(), length() and attributes(). See the following code (R2.10.0): ----------------------------------------------- # data vector of 66 double data X =

Dear all, Is there any method in R to find all possible nonnegative integer solutions to the linear equation with unit coefficients as follow: X1+X2+...+Xk=N Thank you, Amin Zollanvari

Dear all, I have a simple question which I unfortunately do not seem to be able to solve myself. I have a (NxK) matrix and want to generate a new matrix by multiplying each row with itself such that the new matrix has dimension ((N*K)xK) (or better, generate an array with dimension (K,K,N)). I tried apply, but that did not work. Any suggestions? Thanks! Stephan ## Here is a simple

useRs, I am looking to find the minimum positive value of some data I have. Currently, I am able to find the minimum of data after I apply some other functions to it: > x [1] 1 0 1 2 3 3 4 5 5 5 6 7 8 8 9 9 10 10 > sort(x) [1] 0 1 1 2 3 3 4 5 5 5 6 7 8 8 9 9 10 10 > diff(sort(x)) [1] 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0 1 0 > min(diff(sort(x))) [1] 0

useR's. I am working with an algorithm in which I will need to create combinations of all the points I have in a matrix. When I have 2 variables, I simply use expand.grid() to do this. Sometimes I am faced with 3 or more variables and if I run expand.grid(), R cannot process it due to the huge size. Is there any efficient way to workaround this? Thanks, Derek -- View this message in

Dear R experts, I'm trying to find a code to calculate the p-value from the score test for the logistic regression. My fit is like this: logit=beta0+beta1*x1+beta2*x2 +....+ betak* xk. And my H0 is beta1=beta2=...=betak =0. Any help will be highly appreciated. Thank you! Ying

useR's, I want to randomly generate 500 numbers from the standard normal distribution, i.e. N(0,1), but I only want them to be generated in the range -1.5 to 1.5. Does anyone have a simple way to do this? Thanks, dxc13 -- View this message in context: http://www.nabble.com/simple-random-number-generation-tp18642611p18642611.html Sent from the R help mailing list archive at Nabble.com.

useR's I am trying to color the points on a scatter plot (code below) with two colors. Red for values 0.5 -1.0 and blue for 0.0 - .49. Does anyone know a easy way to do this? x <- runif(100, 0, 1) y <- runif(100, 0, 1) plot(y ~ x, pch=16) Thanks, dxc13 -- View this message in context: http://www.nabble.com/color-ranges-on-a-2D-plot-tp14893457p14893457.html Sent from the R help

I checked the R procedure HCLUST (hierarchical clustering) but it looks like it requires a full triangular n x n similarity matrix as input, where n = number of observations. The number of variables is 200. My data set has n = 50,000 observations (keywords), and I use ad-hoc similarity measures, not available in R, to measure keyword similarity. Here, the vast majority of the n x n similarities

Dear R-list, I'm trying to estimate the relative importance of 6 environmental variables in determining clam yield. To estimate clam yield a previous work used the function Yield = (A^a*B^b*C^c...)^1/(a+b+c+...) where A,B,C... are the values of the environmental variables and the weights a,b,c... have not been calibrated on data but taken from literature. Now I'd like to estimate the

useR's, I have a matrix given by the code: mat <- matrix(c(rep(NA,10),1,2,3,4,5,6,7,8,9,10,10,9,8,NA,6,5,4,NA,2,1,rep(NA,10),1,2,3,4,NA,6,7,8,9,10),10,5) This is a 10x5 matrix containing missing values. All columns except the second contain missing values. I want to delete all columns that contain ALL missing values, and in this case, it would be the first and fourth columns. Any column

Hi all, I cannot think of the technical name of this plot, but I want to create a plot with the data below that looks like two back-to-back horizontal bar plots. Ideally, there would be a vertical line in the center of the plot at zero, and on the right hand side would be 4 bars representing the values for 2007, and on the left side of the vertical line would be the corresponding values for 2005.