similar to: Quantiles of sums of independent discrete random variables

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

Hello This may have been answered elsewhere, and I have looked on the web, but nothing helps. I am trying to do the following: X<-matrix(c(1:15),nrow=3,byrow=T) Y<-matrix(c(2,4,6,8,10),ncol=1) I need to sum the product of each row of X by the remaining j rows multiplied by j y values (i.e sum( t(x_i) x_j y_j) ) Hope this makes sense. Thanks in advance. Ps: how do I reference all

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

Hello, I need to integrate the absolute difference between two lines measured on different points. # For example : x <- seq(0, 1, 1/100) f_x <- runif(101) + x y <- seq(0, 1, 1/23) f_y <- runif(24) + (1 - y) plot(x, f_x, type="l") lines(y, f_y) Then I would like to compute Integral( | f_x - f_y | )dx. (This is not the same as | Integral(f_x)dx - Integral(f_y)dx |.)

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

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

Greetings, Minimal reproducible example as requested by the technical expert Jeff Newmiller: library(bayesmeta) # density of $(1/10)*\sum_{j=1}{10}N(j,0.01$ # (convex sum of normal distributions) # f <- Vectorize(function(s) sum(vapply(1:10, ?? FUN = function(j) dnorm(s,mean=j,sd=0.01)/10, FUN.VALUE=0 ))) g <- function(s) dnorm(s,mean=0,sd=0.01) cat("\n\n") for(i in 1:5){ ? ? ?

...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,

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

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

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

I was pleased to find integrate() using the quadpack routines "dqags" and "dqagi". However, I would also like to have the routine "dqawoe" included. This routine is summarized as follows... c***keywords automatic integrator, special-purpose, c integrand with oscillatory cos or sin factor, c clenshaw-curtis method, (end point) singularities,

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

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Hi there, I've sent this e-mail to the list twice but didn't get it back from the list. Have it reach list members? cheers, Ronaldo ---------- Forwarded message ---------- From: Ronaldo Prati <rcprati at gmail.com> Date: 14/12/2006 11:59 Subject: Model formula question To: r-help at stat.math.ethz.ch Hi all, I'm not familiar with R programming and I'm trying to

Dear R users, subject: How to filter XY pairs of inacurate gps position along track, taking into account the time index to not mix track from different days in one average track or subject as: How to filter XY pairs of inacurate gps position: (latitude, longtitude) to get estimated correct position before calculating track i.e. How to estimate (time series? )? How to filter XY pairs of

>>>>> "FrSa" == SABIDO =?iso-8859-1?Q?MART=CDN?= <SABIDO> writes: FrSa> Hello. I am a student from Spain. We are working on 'R' (a FrSa> programming environment for data analysis and graphics). Our FrSa> teacher has told as to make a job about tests in non complet rank FrSa> linear regresion models (I hope you could understand

Hi, I wonder if there is a function in (some package of) R which computes marginal effects of the variables in a glm, say, for concretness, a probit model. By marginal effects of the covariate x_j I mean d P(y=1 | x), which is approx g(xB)B_j dx_j where g is the pdf of the normal distribution, x is the vector of covariates (at some points, say, the mean values) and B is the estimated

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