similar to: Inverting a scale(X)

Hi, I'm faced with the following problem and would appreciate some advice. I could have a data frame x that looks like this: aa bb a 1 "A" b 2 "B" The advantage of this is that I could access all the individual components easily. Also I could access all the rows and columns easily. Alternatively, I could have a list of

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very

For example, the correlation matrix is 3x3 and looks like 1 0.75 0 0 0 0.75 1 0 0 0 0 0 0 0 0 Can I write the code like this? p<- 3 # number of variables per observation N<- 10 # number of samples # define population correlation matrix sigma sigma<-matrix(0,p,p) #creates a px p matrix of 0 rank<-2 for (i in 1:rank){ for (j in 1:rank){ rho<-0.75

I'm fairly new to R, and I'm trying to write out a population model that satisfies the following; the system consists of s species, i= 1, 2,...,s network of interactions between species is specified by a (s x s) real matrix, C[i,j] x[i] being the relative population of the "ith" species (0 =< x[i] =< 1, sum(x[i]=1) the evolution rule being considered is as follows;

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

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

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

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

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

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) >

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

--- 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

Dear All, I'm using betadisper {vegan} and I'm interested not only in the dispersion within the group but also the distances between the groups. With betadisper I get distances to group centroids but is it possible to get distances to other groups centroids? It might be possible to do it by hand by the formula given in the description of the betadisper (below) but I'm a bit confused

Dear all; Working with the following code extracted from the document S Poetry by Patrick Burns (from CRAN), I haven't been able to load the resulting dll into R. The code is basically the calculation of the quadratic form x'Qx: static double quad_form(double *Q, double *x, long n) { long i, j, ij; double ans = 0.0; for(i=0; i < n; i++) { for(j=0, ij = i * n; j < n; j++,

The topic of Ra and jit has come up on this list recently (see http://www.milbo.users.sonic.net/ra/index.html) so I thought people might be interested in this little demo. For it I used my machine, a 3-year old laptop with 2Gb memory running Windows XP, and the good old convolution example, the same one as used on the web page, (though the code on the web page has a slight glitch in it). This

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

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