similar to: issue building dataframes with matrices.

Hi, just for information: On Suse 10.3 x86 > make check fails, if configure was called with --without-recommended-packages for the latest beta. The problem I stumbled upon is located in utlis. Here comes the relevant part of utils-Ex.Rout.fail in R-2.7.0/tests/Examples > ### ** Examples > > require(stats) > glmout <- capture.output(example(glm)) Fehler in

Hallo, I didn´t found any facilities for Halbergs cosinor analysis in R. This analysis is well known in the Chronobiology as the least square approximation of time series using cosine function of known period (in my case of 24hours-period). I tried to write a script but crashed... Can you give me some advices, please!? Thanks Anne Berger Institute of Zoo- and Wildlife Research, Berlin, Germany

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

Dear all I would like to ask you if an assignment can be done inside a lapply statement. For example I would like to covert a double nested for loop for (i in c(1:dimx)){ for (j in c(1:dimy)){ Powermap[i,j] <- Pr(c(i,j),c(PRX,PRY),f) } } to something like that: ij<-expand.grid(i=seq(1:dimx),j=(1:dimy)) unlist(lapply(1:nrow(ij),function(rowId) { return

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 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 all, Does anybody have an idea or suggestion how to construct (plot) 4-dimensional hypercube in R. Thanks in advance for any pointers. Regards, Andrej

I'm trying to: Resample with replacement pairs of distinct rows from a 120 x 65,000 matrix H of 0's and 1's. For each resampled pair sum the resulting 2 x 65,000 matrix by column: 0 1 0 1 ... + 0 0 1 1 ... _______ = 0 1 1 2 ... For each column accumulate the number of 0's, 1's and 2's over the resamples to obtain a 3 x 65,000 matrix G. For those

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

Hello, I thought that I should try openblas when building a CRAN package containing lots of old (twentieth century) C-code with frequent calls to blas and lapack routines. I have the following options on my Ubuntu 20.04 machine: Selection Path Priority Status ------------------------------------------------------------ * 0

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

This patch introduces support for two new parameters in libxl: smbios_firmware=<path_to_smbios_structures_file> acpi_firmware=<path_to_acpi_tables_file> The changes are primarily in the domain building code where the firmware files are read and passed to libxc for loading into the new guest. After the domain building call to libxc, the addresses for the loaded blobs are returned and

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

--- El lun, 12/9/11, Cristalina <pa100cia77@yahoo.es> escribió: De: Cristalina <pa100cia77@yahoo.es> Asunto: Re: [R-es] Cosinor Analysis Para: "Carlos Ortega" <coforfe@gmail.com> Fecha: lunes, 12 de septiembre, 2011 08:43 Hola,   Carlos, muchas gracias.   El método empleado en http://tolstoy.newcastle.edu.au/R/e6/help/09/01/0626.html (el url que se referencia

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

Per the message from Alexander Nervedi, 29 April 2006: > I have to be making a riddiculously silly ommission. > when I run the fillowing i get the cloud plot ok. But I cant figure > out what I am missing out when I call wireframe. > Any help would be appreciated. > x<-runif(100) > y<-rnorm(100) > z<-runif(100) > temp <-data.frame(x,y,z) >

On Thu, 12 Apr 2007, Fernando Magno Quintao Pereira wrote: >> I'm definitely interested in improving coalescing and it sounds like >> this would fall under that work. Do you have references to papers >> that talk about the various algorithms? > > Some suggestions: > > @InProceedings{Budimlic02, > AUTHOR = {Zoran Budimlic and Keith D. Cooper and Timothy

Dear all, I have a somewhat unusual linear mixed model that I can't seem to code in lme. It's only unusual in that one random effect is applied only to some of the observations (I have an indicator variable that specifies which observations have this random effect). The model is: X_hijk = alpha_h + h * b_i + r_(ij) + e_hijk , where h = 0 or 1 (indicator) i = 1, ..., N j = 1,

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