similar to: Generalized cumsum?

I'm trying to figure out how to get the text captured by capturing groups out of a regex match. For instance, let's say I have the pattern "foo ([^ ]+)" and I match it against the string "This is a foo sentence I am reading." The group in the pattern will match the word "sentence" in the target string. How can I get access to this matched group? All I

I've been using Tinn-R with RGui on Windows for quite some time now, but it's very irritating how the Windows RGui allows only very rudimentary line-editing. I can't even use shift-arrow key to select text! I searched through the archives and found what seemed to be a few people asking about this, but I couldn't find a clear answer. I saw some references in the docs to

Full_Name: Brendan Barnwell Version: 2.9.0 OS: Windows XP Pro Submission from: (NULL) (71.102.131.29) The ave() function returns an incorrect datatype. Specifically, ave(x, g, f) always returns a vector with the same mode as x, rather than using the mode of the vector returned by f. Observe: > x [1] "A" "B" "C" "A" "B" "C"

I've been using the ave function to compute some statistics on a data frame. After a while I noticed that, for some reason, it was returning numerical statistics as strings instead of numbers. I delved into the code of the functions and traced the problem to the following fact: ave uses split<- to do its work. Specifically, it does "split(x, g) <- lapply(split(x, g),

Hi! I noticed that cumsum behaves different than the other cumulative functions wrt. NaN values: > values <- c(1,2,NaN,1) > for ( f in c(cumsum, cumprod, cummin, cummax)) print(f(values)) [1] 1 3 NA NA [1] 1 2 NaN NaN [1] 1 1 NaN NaN [1] 1 2 NaN NaN The reason is that cumsum (in cum.c:33) contains an explicit check for ISNAN. Is that intentional? IMHO, ISNA would be better

I recently traced a bug of mine to the fact that cumsum(s)[length(s)] is not always exactly equal to sum(s). For example, x<-1/(12:14) sum(x) - cumsum(x)[3] => 2.8e-17 Floating-point addition is of course not exact, and in particular is not associative, so there are various possible reasons for this. Perhaps sum uses clever summing tricks to get more accurate results? In some

Hello, everyone I wonder if there is in R somewhere a function similar to cumsum(). The function calculates a statistic (say mean or standard deviation) buy adding consequtively one more data point. So, say I have a timeseries of 100 observations. I start by calculating mean of first 30 observations Then I add one observation and calculate mean of 31 observations Then I add one more observation

Hello, I am searching for a function to calculate "partial" cumsums. For example it should calculate the cumulative sums until a NA appears, and restart the cumsum calculation after the NA. this: x <- c(1, 2, 3, NA, 5, 6, 7, 8, 9, 10) should become this: 1 3 6 NA 5 11 18 26 35 45 any ideas? thank you and best regards, stefan

I spent a lot of time searching and came up empty handed on the following query. Is there an equivalent to rowSums that does product or cumulative product and avoids use of apply or looping? I found a rowProd in a package but it was a convenience function for apply. As part of a likelihood calculation called from optim, I?m computing products and cumulative products of rows of matrices with

Full_Name: J. Sisk Version: 1.6.1 OS: Linux (RedHat 8) Submission from: (NULL) (67.119.41.66) Suppose you make a data-frame like so: xxx <- data.frame(a=10,b=20,c=30,d=40) Then cumprod(xxx[1,]) returns > cumprod(xxx[1,]) a b c d 1 10 20 30 40 The documentation for cumprod says that it should work on "numerical objects", and this is a data-frame, but it

Dear R-help, I have been trying really hard to generate the following vector given the data (x) and parameter (alpha) efficiently. Let y be the output list, the aim is to produce the the following vector(y) with at least half the time used by the loop example below. y[1] = alpha * x[1] y[2] = alpha^2 * x[1] + alpha * x[2] y[3] = alpha^3 * x[1] + alpha^2 * x[2] + alpha * x[3] ..... below are

Imagine that I have a large number of points (given by coordinates x and y) that vary in density per space. For the purpose of demonstration it could be generated like this: s <- data.frame(x=runif(10000,0,900),y=runif(10000,0,900)); plot(s) I want to create polygons around the points where point density is greater than a selected threshold (for example, by using krieging or equivalent

I have a dataframe of numeric values with 30 ?rows? and 7 ?columns?. For each column, beginning at ?row? 12 and down to ?row? 30, I wish to calculate the ?rolling 12 row product?. I.e., within each column, I wish to multiply all the values in row 1:12, 2:13, 19:30. I wish to save the results as a new dataframe, which will have 19 rows and 7 columns.

Just when I think I'm starting to learn .... Statement z1 works, statement z doesn't. Why doesn't z work and what do I do to fix it ? Clearly the problem is with the first NA, but I would think it's handled through the loop vectorization. y1 <- rnorm(20, 0, .013) y1 [1] -0.0068630836 -0.0101106230 -0.0169663344 -0.0066314769 0.0075063818 [6] -0.0033548024 0.0015647863

I thought the "apply" functions are faster than for loops, but my most recent test shows that apply actually takes a significantly longer than a for loop. Am I missing something? It doesn't matter much if I do column wise calculations rather than row wise ## Example of how apply is SLOWER than for loop: #rm(list=ls()) ## DEFINE VARIABLES mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ;

Hi all, I was wondering whether it is possible to use the lapply() function to alter the value of the input, something in the spirit of : a1<-runif(100) a2<-function(i){ a1[i]<-a1[i-1]*a1[i];a1[i] } a3<-lapply(2:100,a2) Something akin to a for() loop, but using the lapply() infrastructure. I haven't been able to get rapply() to do this. The reason is that the "real"

What is the R way of computing cumulative growth rates given a series of discrete values indexed . For instance, given a matrix of 20 observations for each of 5 series (zz), what is the most straight forward technique in R for computing cumulative growth (zzcum) ? It seems for the solution I'm after might be imbedding the following cum growth rate calc as a function into a function call

This is my code i don't understand the error message: library(rgenoud) rm(list=ls()) set.seed(666) ######################################################### # As a first step, it is assumed that all input parameters are independent of ageing : ######################################################### InputDim <-20 # Max number of ageings in the inputs CPIRate <- rep(0.02 , InputDim ) #

Hello, I need to build certain interpolation logic using R. Unfortunately, I just started using R, and I'm not familiar with lots of advanced or just convenient features of the language to make this simpler. So I struggled for few days and pretty much reduced the whole exercise to the following problem, which I cannot resolve: Assume we have a vector of some values with NA: a <- c(1,

Hello all, I want to define a vector like w[k+1]=w[k]*a/(b+k) for k=1,...,N-1 without use loop. Is it posible to do in R? Regards khazaei