similar to: Weighted.mean(x,wt) vs. t(x) %*% wt

Dear Rdevelopers The background for this email is that I was helping a PhD student to improve the speed of her R code. I suggested to replace calls like t(AA)%*% BB by crossprod(AA,BB) since I expected this to be faster. The surprising result to me was that this change actually made her code slower. > ## Examples : > > AA <- matrix(rnorm(3000*1000),3000,1000) > BB <-

Hi, I have a 5x731 array A, and I want to compute the sums of the columns. Currently I do: apply(A, 2, sum) But it turns out, this is slow: 70% of my CPU time is spent here, even though there are many complicated steps in my computation. Is there a faster way? Thanks, Martin

Full_Name: Myung-Hoe Huh Version: 2.10 OS: Windows Submission from: (NULL) (116.120.84.194) New Version (2.10.0) weighted mean produces unreasonable result: see below. wt <- c(5, 5, 4, 1)/15 x <- c(3.7,3.3,3.5,2.8) x[4] <- NA (xm <- weighted.mean(x,wt,na.rm=T)) Outcome is > (xm <- weighted.mean(x,wt,na.rm=T)) [1] 3.266667 The number is obtained

Hi all. I tried to calculate sum of deviations from the weighted mean and i didn't get what i expected - 0. Here is an example: > wt <- c(10,25,38,22,5) > x <- 6:10 > wm <- weighted.mean(x,wt) > (x-wm)*wt [1] -18.70 -21.75 4.94 24.86 10.65 > sum((x-wm)*wt) [1] -1.24345e-14 With simple mean I got 0: > sum(x-mean(x)) [1] 0 Could someone explain me why we

Dear all -- I want to compute weighted.mean() for grouped rows. Data frame extract is just below. For each Key, I want the mean of IAC weighted by Wt. DP0[1:20,] Key IAC Wt 2 C3-PD030020050.PD030020050.3.12.3.0 0.765 0.8590000 3 C3-PD030020050.PD030020050.3.12.3.0 0.764 0.8449651 4 C3-PD030020050.PD030020050.3.12.3.0

Hello, The problem I'm working now requires to operate on big matrices. I've noticed that there are some packages that allows to run some commands in parallel. I've tried snow and NetWorkSpaces, without much success (they are far more slower that the normal functions) My problem is very simple, it doesn't require any communication between parallel tasks; only that it divides

Hi R-users, I would like to calculate weighted mean of several variables by two factors where the weight vector is the same for all variables. Below, there is a simple example where I have only two variables: "v1","v2" both weighted by "wt" and my factors are "gender" and "year". set.seed(1) df <- data.frame(gender = rep(c("M",

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Hi, Couldn't is.unsorted() bail out immediately here (after comparing the first 2 elements): > x <- 20000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.084 0.040 0.124 > x <- 200000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.772 0.440 1.214 Thanks! H.

Hello Sir, I am Reetesh Ranjan, a 3rd year undergraduate student at the *INDIAN INSTITUTE OF TECHNOLOGY BHU, Varanasi-*one of the premier engineering colleges of India. I have gone through your webpage thoroughly and I am very interested in the work that you are undertaking on *Project: Weighting Schemes.*. I earnestly wish to work under your guidance, learn and progress through this experience.

Dear all, I have been looking for a while for ways to simulate from Langevin distributions and I thought I would ask here. I am ok with finding an algorithmic reference, though of course, a R package would be stupendous! Btw, just to clarify, the Langevin distribution with (mu, K), where mu is a vector and K>0 the concentration parameter is defined to be: f(x) = exp(K*mu'x) / const where

Hi, I am experiencing a long delay when using dataframes inside loops and was wordering if this is a bug or not. Example code: > st <- rep(1,100000) > ed <- rep(2,100000) > for(i in 1:length(st)) st[i] <- ed[i] # works fine > df <- data.frame(start=st,end=ed) > for(i in 1:dim(df)[1]) df[i,1] <- df[i,2] #takes for ever R: R 2.0.0 (2004-10-04) OS: Linux, Fedora Core 2

hi all If I wanna get the total number of lines in a big file without reading the file's content into R as matrix or data frame, any methods or functions? thanks in advance. Regards

Hi, Encouraged by a tip from Simon Urbanek I tried to use the S3 machinery to write a faster version of the data.frame class. This quickly hits a snag: the "[.default"(x, i) for some reason cares about the dimensionality of x. In the end there is a full transcript of my R session. It includes the motivation for writing the class and the problems I have encountered. As a result I see

Hi I have a square matrix Ainv of size N-by-N where N ~ 1000 I have a rectangular matrix H of size N by n where n ~ 4. I have a vector d of length N. I need X = solve(t(H) %*% Ainv %*% H) %*% t(H) %*% Ainv %*% d and H %*% X. It is possible to rewrite X in the recommended crossprod way: X <- solve(quad.form(Ainv, H), crossprod(crossprod(Ainv, H), d)) where quad.form() is a little

Luke, Thank you for sharing the benchmark results. The improvement is very substantial, I am looking forward to the release of the byte compiler! The arithmetic shows that x[i]<- is still the bottleneck. I suspect that this is due to a very involved dispatching/search for the appropriate function on the C level. There might be significant gain if loops somehow cached the result of the initial

> wt.filter("d8") #### HOW COME ???? Error in validObject(.Object) : invalid class "wt.filter" object: invalid object for slot "transform" in class "wt.filter": got class "function", should be or extend class "character" > wt.filter("d10") # OK An object of

Hello, is it possible to create my own connection which I could use with read.table or scan ? I would like to create a connection that would read from multiple files in sequence (like if they were concatenated), possibly with an option to skip first n lines of each file. I would like to avoid using platform specific scripts for that... (currently I invoke "/bin/cat" from R to create a

I am trying to run PCA on a matrix (the first column and row are headers). There are several cells with NA's. When I run PCA with the following code: ______________________________________ setwd("I:/PCA") AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1) attach(AsianProp) AsianProp AsianProp.pca<-princomp(AsianProp, na.omit)

> wt.filter("d8") Error in validObject(.Object) : invalid class "wt.filter" object: invalid object for slot "transform" in class "wt.filter": got class "function", should be or extend class "character" How come ? The above function works fine with the other Daubechies filters. Thank you. Best regards, Maura tutti i