similar to: improve efficiency of a loop

On Tue, Feb 3, 2015 at 10:39 AM, Marius Hofert <marius.hofert at uwaterloo.ca> wrote: > Hi, > > This is most likely only a minor technicality, but I saw the > following: On page 6 of the 'parallel' vignette > (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), > the random-number generator "L'Ecuyer-CMRG" is said to have seed >

Hi, This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

For 2 or 3 stage least squares, what do you R folks do? Follow-up question. My student wants to estimate this. 2 variables are governed by a system of difference equations. His theory is like so. Y_t and X_t are state variables, we want estimates for a, g, b, and h. X_(t+1) = 1 + a X_t + (a/K)* (X_t)^2 - g Y_t X_t Y_(t+1) = b Y_t + h* X_t * Y_t K is perhaps something to estimate, but it

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

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1.

Dear helpers in this forum, I have the following question: Suppose I have the following data set: id x y 023 1 2 023 2 5 023 4 6 023 5 7 412 2 5 412 3 4 412 4 6 412 7 9 220 5 7 220 4 8 220 9 8 ...... and i want to calculate sum_{i=1}^k sum_{j=1}^{n_i}x_{ij}*y_{ij} is there a simple way to do this within and between subject summation in R?

Hi, I've used the Mathematica to produce 3D graphics, contour plots of a bivariate normal distribution Now I want make these graphics in R, but i do not know how. I would like to: - Plot a 3D graph for some different variance matrix - Plot the contour plots - Find and try to plot (in the 3d graph ou contour plot) the (1-a)% confidence region based in a chi-square(a) with the degrees of

Hello, I have a list in which each element is a list. I want to create a matrix indexed by the two indices of the list. I have been using do.call, but I am not getting what I want. Let me show you: > l.intercepts #the list that nests another list $`1995` $`1995`$`31` (Intercept) 25.37164 $`1995`$`33` (Intercept) 26.66755 $`2006` $`2006`$`31` (Intercept) 25.86621 $`2006`$`33`

NAME xx - twice as dirty SYNOPSIS ~ > gem install "double x" require "xx" include XX::XHTML doc = xhtml_{ html_{ head_{ title_{ " go xx! " } } body_{ " one more and it would be illegal " } } } URI http://rubyforge.org/frs/?group_id=1024 DESCRIPTION xx is a library designed to extend ruby objects

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

You'll find that it is a lot easier to do it in R: # lets first simulate a bivariate normal sample library(MASS) bivn <- mvrnorm(1000, mu = c(0, 0), Sigma = matrix(c(1, .5, .5, 1), 2)) # now we do a kernel density estimate bivn.kde <- kde2d(bivn[,1], bivn[,2], n = 50) # now plot your results contour(bivn.kde) image(bivn.kde) persp(bivn.kde, phi = 45, theta = 30) # fancy contour with

...as promised. This describes the mdct used in my d.m.l patch. I think it is the same as the Lee fast-dct. I typed it in a kind of pseudo-TeX, 'cause the ascii art would kill me. Hope you can read TeX source; if not, ask someone who can to make a .ps/.gif/.whatever of the TeX output, and put it on a webpage or something. I'm to lazy to do it (and besides, I don't have access to TeX,

Hello dear r-gurus! I have a question about the logit-model. I think I have misunderstood something and I'm trying to find a bug from my code or even better from my head. Any help is appreciated. The question is shortly: why I'm not having same coefficients from the logit-regression when using a link-function and an explicite transformation of the dependent. Below some details. I'm

Dear R-Users I have the following matrix > out$desc [,1] [,2] [1,] "" "" [2,] "y_{01}(k-001)" "" [3,] "y_{01}(k-002)" "" [4,] "y_{01}(k-003)" "" [5,] "u_{01}(k-001)" "" [6,] "u_{01}(k-002)" "" [7,] "u_{01}(k-003)" ""

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Dear all, I am trying to model number of samples from a given series. The series are modelled according Gamma function. In order to estimate the # samples, I use BIC/AIC with MLE (computed from dgamma function). Here is the code I have. __BEGIN__ mlogl <- function( x_func, theta_func, samp) { # computing log_likelihood return( - sum(dgamma(samp, shape = x_func, scale=theta_func, log

Hello, I am new to Ubuntu (Linux in general) and I am trying to install R: so far, I am stuck. I have followed the instructions at the CRAN website with little luck. I read an exchange about the same issue posted before, but did not give me enough information to solve things by myself, hence, this email. Based on the former exchange I read, I am trying to include enough information to see if

Dear List, is there an elegant (or even not elegant) way how to transform dissimilarity or distance matrix A (or, in general, arbitrary symmetrical matrix) by transposition of rows and columns into a form closest to "block diagonal" matrix B? The matrix A is adjusted the following way A[A<epsilon] <-0 #(epsilon is given "small" number) B: (in its ideal form)

Hi all I recently started to write a matrix exponentiation operator for R (by adding a new operator definition to names.c, and adding the following code to arrays.c). It is not finished yet, but I would like to solicit some comments, as there are a few areas of R's internals that I am still feeling my way around. Firstly: 1) Would there be interest in adding a new operator %^% that performs