similar to: matrix vectorization or something else??

Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a

This is a statistics question not an R question. When calculating the sample correlation coefficient cor(x_t,y_t) between say two variables, x_t and y_t t=1,.....n ( one can assume that the variables are in time but I don't think this really matters for the question ), does someone know where I can find any piece of literature that says that each (x_j,y_j) pair has To be independent from the

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with

I discovered recently that the phrase "Oja median" produces no hits in Jonathan Baron's very valuable R search engine. I found this surprising since I've long regarded this idea as one of the more interesting notions in the multivariate robustness literature. To begin to remedy this oversight I wrote a bivariate version and then decided that writing a general p-variate version

Hello I am running a regression without the intercept, and want to compute dfbetas. How do I do this? The dfbetas function only works when the intercept is included in the model. Regards K [[alternative HTML version deleted]]

Hello Can anyone tell me what I am doing wrong below? My Y and y_hat are the same. A<-scale(stackloss) n1<- dim(A)[1];n2<-dim(A)[2] X<-svd(A) Y<- matrix(A[,"stack.loss"],nrow=n1) Y y_hat <-matrix((X$u%*% t(X$u))%*%Y,nrow=n1,byrow=T) y_hat [[alternative HTML version deleted]]

Dear everyone, I'm coding the Horowitz-Spokoiny (2001) test [1], and I would be very grateful or some advice regarding the Kernel density (apologies beforehand if my terminology is not fully correct). I have looked into ksmooth and npreg, but with no success. Given a (n x p) matrix of covariates X, I need to construct the following matrix of Kernel densities or weights: w(x_i, x_j) =

Saludo cordial para cada uno. Les pido ayuda para generar números aleatorios de una mixtura k-puntos. Sabemos que la función de distribución F es una mixtura k-puntos si es de la forma F(x) = p_1 F_1(x) + p_2 F_2(x) + … + p_k F_k(x), donde F_j es una función de distribución de probabilidad, p_j > 0 y suma(p_j) = 1, para j = 1, 2, …, k. En mi caso particular F es la suavización de la

Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta

Hi all, I have been trying to add the line: h = n - p0 + 1; just after h = n / 2 + 1; (line 131) in the source code (the original paper mention this is possible for p0&lt;n). with p0 declared as an int (actually i used the same declaration protocol as n everywhere in the code). The &quot;new&quot; source compiles, but when i give it reasonable values of p0, it runs unto

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Hi: I need to plot two data sets and then I need to change labels of xaxis. I can manage to achieve something like it with this: x<-data.frame(a=c(1,2,3,4,5),b=c(2,4,6,8,10)) y<-data.frame(a=c(3,4,5),b=c(1.5,2,2.5)) xts <- ts(x$b,start=x$a[1]) yts <- ts(y$b,start=y$a[1]) ts.plot(xts,yts,col=c("red","blue")) But ts.plot does not allow to change x labels. Besides,

...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, and apologies for the upcoming naive questions. I am a biologist who is trying to teach himself the appropriate areas of math and stats. I welcome pointers to suggested background reading just as much as I do direct answers to my question. Let's say I have a function F() that takes variables (a,b,c,a1,b1,c1) and returns x, and I want to find the values of these variables that result in

Hi, i have that table Thesis Day A B C 1 0 83.43 90.15 22.97 1 0 85.50 94.97 16.62 1 0 83.36 95.38 20.70 1 0 84.47 92.16 23.58 1 0 83.98 95.33 19.39 1 0 82.86 93.78 24.55 1 0 83.39 92.67 19.56 1 0 85.17 95.24 17.95 1 0 81.62 93.32 28.49 1 0 82.99 92.85 19.73 1 0 81.11 95.67 27.20 1 0 83.39 94.69 16.51 1 0 79.56 89.87 30.39 1 0 80.54 93.32 21.76 1 0 82.11 92.58 22.17 1 14 85.65 94.00 19.19 1 14

Hello, I have the idea brewing for some time in my mind - to build Linux based HTPC. It would be used for movies, cable (analog for now) recording, Stepmania, backup station, etc. And I would like to get some information before proceeding to estimate the cost. Which one of the Linux projects would you suggest and why? MythTV, Freevo or something else? Is CentOS good for such task or I should

Hey gang, I have these classes: class Image < UploadFile validates_file_format_of :filename, :in => ["gif", "png", "jpg"] end class UploadFile < PropertyItem file_column :filename validates_presence_of :filename end class PropertyItem < ActiveRecord::Base validates_presence_of :name end What I''m trying to achieve is a class