similar to: Applying function to a TABLE and also "apply, tapply, sapply etc"

On 2/8/07, Albrecht, Dr. Stefan (AZ Private Equity Partner) <stefan.albrecht at apep.com> wrote: > Dear all, > > Thanks a lot for your comments. > > I very well agree with you that writing efficient code is about optimisation. The most important rules I know would be: > - vectorization > - pre-definition of vectors, etc. > - use matrix instead of data.frame > - do

Dear R helpers I have following data stocks <- c("ABC", "DEF", "GHI", "JKL") prices_df <- data.frame(ABC = c(17,24,15,22,16,22,17,22,15,19),                                          DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                          

two OutputsHello! I am Amelia from Auckland and work for a bank. I am new to R and I have started my venture with R just a couple of weeks back and this is my first mail to R-forum. I need following assistance Suppose my R code generates following outputs as > X [[1]] [1] 40 [[2]] [1] 80    160 [[3]] [1] 160   80  400 > Y [[1]] [1] 10 [[2]] [1] 10    30 [[3]] [1] 5  18  20

Dear sir, I am extremely sorry for messing up the logic asking for help w.r.t. my earlier mails   I have tried to explain below what I am looking for.     I have a database (say, currency_rates) storing datewise currency exchange rates with some base currency XYZ.   currency_rates <- data.frame(date = c("12/31/2010", "12/30/2010", "12/29/2010",

Hi: I have two matrices, A and B, where A is n x k, and B is m x k, where n >> m >> k. Is there a computationally fast way to count the number of times each row (a k-vector) of B occurs in A? Thanks for any suggestions. Best, Ravi. [[alternative HTML version deleted]]

hI I have to calculate V statistic for each row of a large dataframe (28000). I can not use multtest package for paired wilcox test. I have been using for loop which are. Is there a way to speed the computation with another method like using apply or tapply? My data set looks like this: 11573_MB 11911_MB 11966_MB 12091_MB 12168_MB 12420_MB................ cg00000292

Hi, Reading about a "Heads and Tails" game in http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf Introduction to Probability (Example 1.4, pp. 5-8). You toss a coin 40 times. If heads, Peter wins $1, tails, he loses $1. I think I can do that ok with: winnings <- sum(sample(c(-1,1), 40, replace=TRUE)) But I have to do it 10,000 times

Dear R forum helpers,I have following datatrans <- data.frame(currency_transacted = c("EURO", "USD", "USD", "GBP", "USD", "AUD"), position_amt = c(10000, 25000, 20000, 15000, 22000, 30000))date <- c("12/31/2010", "12/30/2010", "12/29/2010", "12/28/2010", "12/27/2010",

library(plyr) n<-100000 grp1<-sample(1:750, n, replace=T) grp2<-sample(1:750, n, replace=T) d<-data.frame(x=rnorm(n), y=rnorm(n), grp1=grp1, grp2=grp2) system.time({ d$avx1 <- ave(d$x, list(d$grp1, d$grp2)) d$avy1 <- ave(d$y, list(d$grp1, d$grp2)) }) # user system elapsed # 39.300 0.279 40.809 system.time({ d$avx2 <- ave(d$x, interaction(d$grp1, d$grp2, drop =

Hi, I have a list in which element is a vector (all of the same length and all numeric). I want to find the mean of the first elements of the vectors, the mean of the second elements of the vectors and so on. Currently I convert the list to a data.frame and apply rowMeans(). But is there a way to to do this directly on the list? I seem to recall a post in which there was such a function (or

Hey fellas: I would like to integrate the following function: integrand <- function (x,t) { exp(-2*t)*(2*t)^x/(10*factorial(x)) } with respect to the t variable, from 0 to 10. The variable x here works as a parameter: I would like to integrate the said function for each value of x in 0,1,..,44. I have tried Vectorize to no avail. Thanks in advance, jose romero

Dear R forum helpers, I am extremely sorry for the receipt of my incomplete mail yesterday. There was connectivity problem at my end and so I chose to send the mail through my cell, only to realize today about the way mail has been transmitted. I am again sending my complete mail through regular channel and sincerely apologize for the inconvenience caused. ## Here is my actual mail Dear R

Dear R helpers Suppose following is an output due to some R process. I wish to save it as a table in 'temp.db' df <- data.frame(x = c(5, 4, 3, 11), y = c(25, 16, 9, 121))     library(RSQLite)     write('** Initializing','')     drv <- dbDriver("SQLite", shared.cache = TRUE)     con <- dbConnect(drv, dbname = "temp.db",

Dear list members, Any help on this efficiency issue would be greatly appreciated. I would like to find the most efficient way to run a non-vectorized function (here: fisher exact test p-value) iteratively using 4 matrices with identical dimensions. And as a result I aim for an array with identical dimensions containing the corresponding p-values. Please consider some code using a trivial

Hi, I am just wondering if there is an easy way to count in a numeric vector how many numbers don't have replicates. For example, a=c(1,1,2,2,3,4,5), how can I know there are three numbers (3, 4 and 5) without replicates? Thank you! Jun =====

Hi, I would have thought that these two constructions would produce the same result but they do not. Resp <- rbinom(10, 1, 0.5) Stim <- rep(0:1, 5) mm <- model.matrix(~ Stim) Xb <- mm %*% c(0, 1) ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) pnorm(as.vector(Xb), lower.tail = Resp, log.p = TRUE) > ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) [1] -0.6931472 -1.8410216

Hi, I need to write a function that would look something like this: S <- function(b=betas){ expression(b[1] * f(b[2] * x * f(b[3] * x * f(...b[n-1] * x * f(b[n] * x)))...) } Where n is the number of element in b. Further I need to be able to evaluate S at some x numerically of course and I need to use "deriv" and produce dS/dx such that I can evaluate it also at some x. I

Dear All, I am running a simulation to obtain coverage probability of Wald type confidence intervals for my parameter d in a function of two parameters (mu,d). I am optimizing it using "optim" method "L-BFGS-B" to obtain MLE. As, I want to invert the Hessian matrix to get Standard errors of the two parameter estimates. However, my Hessian matrix at times becomes

With some chagrin after spending a couple of hours trying to debug a script, I realized I had typed in something like ans<-optimx(start, myfn, mygr, lower<-lo, upper=up) that is, the "<-" rather than "=". The outcome on my machine was a non-obvious error several layers deep in the call stack. For info, optim() seems to stop much more quickly. The error is

Hi, I apologize for the long message but the problem I encountered can't be stated in a few lines. I am having some problems with the function constrOptim. My goal is to maximize the likelihood of product of K multinomials, each with four catagories under linear constraints on the parameter values. I have found that the function does not work for many data configurations. #The likelihood