similar to: Unir coordenas en un plano mediante linea

Dear R listers, I am trying to create a new variable that uniquely identifies groups of observations in a dataset. So far I couldn't figure out how to do this in R. In Stata I would simply type: egen newvar = group(dim1, dim2, dim3) Please, find below a quick example to show what I am dealing with: I have a dataset with 4 variables: var <- runif(50) ## a variable that I want to group

Full_Name: Manuel L?pez-Ib??ez Version: R version 2.6.2 (2008-02-08) OS: linux-gnu Submission from: (NULL) (164.15.10.156) This is only relevant for CPU intensive child processes. Otherwise, the problem is not obvious. Therefore, we need a CPU intensive program like this one: /************************************/ /*** Compile with: gcc -o timer-test -O0 timer-test.c -lm */ #include

Hi, I would like to use the multinomRob function to test election results. However, depending on which independent variables I include and how many categories I have in the dependent variable, the model cannot be estimated. My data look like this (there are 68 observations): > head(database) RESTE09 GAUCHE09 PDC09 PLR09 UDC09 MCG09 RESTE05 GAUCHE05 PDC05 D1 1455

Dear all, I had to extracts/set elements from/in a matrix. Let say I have two vectors dim1 and dim2 of indices in the respective two dimensions of a matrix: I want to extract all the corresponding elements. I the case of a nxn matrix, dim1 <- 1:n and dim2 <- 1:n would extract the diagonal. I know one way would be to use the functions 'row' and 'col', but the matrixes I

foo <- rnorm(30*34*12) dim(foo) <- c(30, 34, 12) I want to make a data.frame out of this three-dimensional array. Each dimension will be a variabel (column) in the data.frame. I know how this can be done in a very slow way using for loops, like this: x <- rep(seq(from = 1, to = 30), 34) y <- as.vector(sapply(1:34, function(x) {rep(x, 30)})) month <- as.vector(sapply(1:12,

Dear R-help community, I am working with multidimensional contingency tables and I am having trouble getting loglm to run on all dimensions without typing out each dimension. I have generated random data and provided output for the results I want below: d1.c1 <- rnorm(20, .10, .02) d1.c2 <- rnorm(20, .10, .02) d2.c1 <- rnorm(20, .09, .02) d2.c2 <- rnorm(20, .09, .02) d3.c1 <-

Hi suppose I have two arrays x1,x2 of dimensions a1,b1,c1 and a2,b2,c2 respectively. I want x = x1 "+" x2 with dimensions c(max(a1,a2), max(b1,b2),max (c1,c2)) with x[a,b,c] = x1[a1,b1,c1] + x2[a2,b2,c2] if a <=min(a1,a2) , b<=min (b1,b2), c<=min(c1,c2) and the other bits either x1 or x2 or zero according to whether the coordinates are "in range" for

Hello all, I have been using the function ordirgl to plot 3D dynamic ordinations. The ordirgl function works just fine. IN fact, I was even able to write a function that allows me to identify points in the 3D plot: identify.rgl<-function(env_var,ord,dim1,dim2,dim3) { tmp<-select3d(button="left") tmp.keep<-tmp(ord[,dim1],ord[,dim2],ord[,dim3])

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

Dear R users, I'am using marray and Limma packages to analyze genepix output. 1) how can I filter bad spots from my data (data contains 3 types of bad spots). my experiment contains 12 samples and the bad spot are not associated to the same probes 2) how can I remove control probes from my data ? I'm sorry, i'm new with R and I can't find answer in packages doc. best regards,

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

Hello there I wish to get the "coordinates" of the minimum element of an array. For example, if the array were > H = array(c(8:5,1:4),dim=c(2,2,2)) > H , , 1 [,1] [,2] [1,] 8 6 [2,] 7 5 , , 2 [,1] [,2] [1,] 1 3 [2,] 2 4 then > min(H) [1] 1 and > max(H) [1] 8 Say "idx" were the function I'm looking for. Then, what

Hi, See my example below. a<-array(1:12,c(2,3,2)) > a , , 1 [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 , , 2 [,1] [,2] [,3] [1,] 7 9 11 [2,] 8 10 12 I want to get a result something like dim1 dim2 dim3 elements 1 1 1 1 2 1 1 3 2 2 1 2 I tried the data.frame and expand.grid functions,

High! How can I add variable labels (rownames for instance) to a plot created with ‚clouds’ ( clouds(dim3~dim1*dim2)) – package lattice? Thanks in advance! Martina Renninger [[alternative HTML version deleted]]

I have a large array and would like to extract from it the row and column indices just of values for which a particular boolean condition is true. I assume there's a simple way to do this, but I haven't figured it out yet. Any help would be appreciated. Tim

I am trying to multiply a 3D array of 4x4x4 by the 4 3D arrays of a 4D array with dimensions 4x4x4x4 (the last dimension being the one that I want to split by). (4x4x4 array) > hiaAry , , a1 i1 i2 i3 i4 h1 9.5936098 6.001040 0.08772 0.3138600 h2 1.2003500 1.454570 2.79248 0.0000000 h3 0.1346500 0.201220 0.39256 0.5464000 h4 0.0109000 0.012270 0.16417 0.2766900 ,

Hi all, I would like to get the array index for a range of values, say 0 < x < 1.5. I'm wondering if there is an alternative for the following which I've done x0 <- rnorm(100) x1 <- ifelse(x0 > 0 & x0 < 1.5,"t","f") x2 <- which(x1=="t",arr.ind=TRUE) x0[x2] Thanks. -- Muhammad

Hi, I have been dealing with some problems finding a fast way of getting to know in what dimension a specific value is located out of an array (like the 'which' function for a vector returns its position). Unable to find anything about this on the internet I wrote a function myself. Could you please comment if such a function already exists, and if not, please feel free to comment the

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Below is code that introduces a list comprehension syntax into R, allowing expressions like: > .[ sin(x) ~ x <- (0:11)/11 ] [1] 0.00000000 0.09078392 0.18081808 0.26935891 0.35567516 0.43905397 [7] 0.51880673 0.59427479 0.66483486 0.72990422 0.78894546 0.84147098 > .[ .[x*y ~ x <- 0:3] ~ y <- 0:4] [,1] [,2] [,3] [,4] [,5] [1,] 0 0 0 0 0 [2,] 0 1 2