similar to: Creating table from data frame

Given an nxm matrix A I want to compute the nxm matrix B whose ij-th element is the sum of the elements of A lying on the diagonal that ends with element ij, i.e., b_ij = a_ij + a_(i-1)(j-1) + a_(i-2)(j-2) + ... In APL (which I no longer use), I would use the 'rotate' operator to derive an array whose columns are diagonals of the given array and then cumulate down columns. Is

Dear R Users, Lets assume I have this data frame: x y z 1 1.00 5 0.5 2 1.02 5 0.7 3 1.04 7 0.1 4 1.06 9 0.4 x and y columns are sorted and the values not necessarily integers. z values are not sorted. Now I would like to create a matrix out of this with x as first column values and y as first row values. Matrix element a_11 shall be left NA. The a_ij should have the z value for the

Hi, I've got an array, say with i,jth entry = A_ij, and a vector, say with jth entry= v_j. I would like to multiply each column of the array by the corresponding vector component, i,e. find the array with i,jth entry A_ij * v_j This seems so basic but I can't figure out how to do it without a loop. Any suggestions? Michal.

Hello, Currently it's possible to convert an object of class table to a data frame with as.data.frame.table(), but there's no ready-made function, AFAIK, to do the reverse operation, i.e. conversion of a data frame to a table. Do you think it would be a good idea to add a data.frame method to as.table(), to allow such conversions? The idea is that if `x' is a table and `y <-

I have a data set where one column consists of two numerical factors, separated by a "-". So my data looks something like this: 43-156 43-43 1267-18 . . . There are additional columns consisting of single factors as well, so reading the csv file (where the data is stored) with the sep="-" addition won't work since the rest of the factors are separated by commas. So first

Hello there! I am still struggling with a binomial response over all categorical variables (some of them with 3 levels, most with 2 levels). After initial struggles with glm's (struggle coming from the data, not the actual analysis) I have decided to prefer contingency tables. I have my data such as: response:

Hi, I have a data.frame as following: var1 var2 1 ab_c_(ok) 2 okf789(db)_c 3 jojfiod(90).gt 4 "ij"_(78)__op 5 (iojfodjfo)_ab what I want is to create a new variable called "var3". the value of var3 is the content in the Parentheses. so var3 would be: var3 ok db 90 78 iojfodjfo how to do this? thanks, karena --

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Dear R developers, I was looking for a function analogous to base::diag() for getting and setting bands of a matrix. The closest I could find was Matrix::band(), but this was not exactly what I wanted for two reasons. Firstly, Matrix::band() returns a matrix rather than just the specified band. Secondly, Matrix::band() cannot be used for setting the values for a matrix band. Setting or

I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Dear all, As our previous email did not get any response, we try again with a reformulated question! We are trying to do something which needs an efficient loop over a huge array, possibly functions such as apply and related (tapply, lapply...?), but can't really understand syntax and examples in practice...i.e. cant' make it work. to be more specific: we are trying to apply a mask

I am out of my league with this question. The following code starts the java imaging program ImageJ from within R, and displays an image (assuming ImageJ is installed on your computer). library(RImageJ) img <- IJ$openImage( file.choose() ) #pick an available .tif file img$show() # make the image object visible # An image is now displayed # find out about the objects involved >

I need to refresh my memory on Probability Theory, especially on conditional probability. In particular, I want to solve the following two problems. Can somebody point me some good books on Probability Theory? Thank you! 1. Z=X+Y, where X and Y are independent random variables and their distributions are known. Now, I want to compute E(X | Z = z). 2.Suppose that I have $I \times J$ random number

Dear people, A somewhat clueless question follows: I just discovered that the lme function in contrib package nlme for R, while similar to the lme function in Splus, does not use the cluster function option. This difference does not appear to be documented in the V&R `R Complements' file. I have data which is divided into 6 groups The lme model is of the form (simplified from the actual

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

Hi, I'm trying to concatenate values from two columns in a data frame. For example, I have the following data.frame: C1 C2 C3 C4 C5 A B *F C* Q G H *I J* T K D *R S* E P L *M N* O I'd like to concatenate text from columns C3 and C4, to yield either a list or vector, like so: NewCol FC IJ RS MN Is this feasible in R? Thanks!

Hey, I'm using R as a pre-processor for a large dataset with IDs which are numeric (but has no numeric meaning so can be seen as factors). I do some data formating and then write it out to a csv file. However the problem is that the IDs are very long, 18-22 chars long more precisely. R is constantly rewriting these IDs to the abbreviated +eX which hinders me from exporting the data to the

I have an application that would generate a cross-tabulation in array format in R. In particular, my application would give me a result similar to that of : array(5,c(2,2,2,2,2)) The above could be seen as a cross-tabulation of 5 variables with 2 levels each (could be 0 and 1). In this case, the data were such that each cell has exactly 5 observations. I Now, I want the output to look like the

Hello, I have a R code for doing convolution of two functions: convolveSlow <- function(x, y) { nx <- length(x); ny <- length(y) xy <- numeric(nx + ny - 1) for(i in seq(length = nx)) { xi <- x[[i]] for(j in seq(length = ny)) { ij <- i+j-1 xy[[ij]] <- xy[[ij]] + xi * y[[j]] } } xy } How do I reduce the 2

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object