similar to: Replace zeroes in vector with nearest non-zero value

Folks, I have a matrix: set.seed(123) a <- matrix(rnorm(100), 10) And a vector: b <- rnorm(10) Now, I want to switch the signs of those rows of a corresponding to indices in b whose values exceed the 75 %-ile of b which(b > quantile(b)[4]) [1] 2 6 10 so I want, in effect: a[2, ] <- -a[2, ] a[6, ] <- -a[6, ] a[10, ] <- -a[10, ] I thought I could do a[which(b >

folks, if i have three matrices, a, b, cc with some colnames in common, and i want to create a matrix which consists of the common columns added up, and the other columns tacked on, what's a good way to do it? i've got the following roundabout code for two matrices, but if the number of matrices increases, then i'm a bit stymied. > a <- matrix(1:20,ncol=4); colnames(a) <-

Folks, If I have a series mm of, say, monthly observations, and a series dd of daily dates, what's a good way of expanding mm such that corresponding to each day in dd within the corresponding month in mm, the values of mm are repeated? So e.g., if I have mm: mm <- c(15, 10, 12, 13, 11) names(mm)<-c("Nov 2008", "Dec 2008", "Jan 2009", "Feb

Folks, I've got a matrix x as follows: > x <- matrix(c(1,2,3,5,3,4,3,2,1), ncol = 3, byrow = TRUE) > x [,1] [,2] [,3] [1,] 1 2 3 [2,] 5 3 4 [3,] 3 2 1 In each row of x, I want to replace the minimum value by -1, the maximum value by +1 and all other values by 0. So in the above case I want to end up as follows: [,1] [,2] [,3] [1,] -1 0

Folks, I put up a brief note describing my naive attempts to compute Goldbach partitions, starting with a brute-force approach and refining progressively. http://jostamon.blogspot.com/2009/02/goldbachs-comet.html I'd welcome your suggestions on improvements, alternatives, other optimisations, esp. to do with space vs time tradeoffs. Is this an example interesting enough for

folks, is there a clever way to compute the sum of the product of two vectors such that the common indices are not multiplied together? i.e. if i have vectors X, Y, how can i compute Sum (X[i] * Y[j]) i != j where i != j also, what if i wanted Sum (X[i] * Y[j] * R[i, j]) i != j where R is a matrix? thanks, murali

Sorry to plague the list, but I think I got the answer. The following would do: > signalList <- list(tradingRules$Signal[tradingRules$Enabled]) [[1]] > length(signalList) [1] 2 Now my problem is shifted: I have the Signal column in the original data frame referring to actual matrices previously created in R. That is, bar_signal and cif_signal are extant matrices. What I need is the

Folks, I have a 3000 x 4 matrix (y), which I need to regress row-by-row against a 4-vector (x) to create a matrix lm.y of intercepts and slopes. To illustrate: y <- matrix(rnorm(12000), ncol = 4) x <- c(1/12, 3/12, 6/12, 1) system.time(lm.y <- t(apply(y, 1, function(z) lm(z ~ x)$coefficient))) [1] 44.72 18.00 69.52 NA NA Takes more than a minute to do (and I need to do many

Folks, I have three dataframes storing some information about two currency pairs, as follows: R> a EUR-USD NOK-SEK 1.23 1.33 1.22 1.43 1.26 1.42 1.24 1.50 1.21 1.36 1.26 1.60 1.29 1.44 1.25 1.36 1.27 1.39 1.23 1.48 1.22 1.26 1.24 1.29 1.27 1.57 1.21 1.55 1.23 1.35 1.25 1.41 1.25 1.30 1.23 1.11 1.28 1.37 1.27 1.23 R> b EUR-USD NOK-SEK 1.23 1.22 1.21 1.36 1.28 1.61 1.23 1.34 1.21 1.22

Folks, I'm trying to get stats from a matrix for each transition from one state to another. I have a matrix x as below. structure(c(0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 0, 0, 2, 2, 0.21, -0.57, -0.59, 0.16, -1.62, 0.18, -0.81, -0.19, -0.76, 0.74, -1.51, 2.79, 0.41, 1.63, -0.86, -0.81, 0.39, -1.38, 0.06, 0.84, 0.51, -1, -1.29, 2.15, 0.39, 0.78, 0.85, 1.18, 1.66, 0.9, -0.94,

Hi, With a vector like: x = c (22, 23, 22.5, 0,0,24, 0, 23.2, 23.5, 0,0,0, 26) How can I replace the 0's with the previous last value without looping through the vector ? Something tells me I am missing the obvious. Thanks, Chris -- View this message in context: http://r.789695.n4.nabble.com/Vector-replace-0-elements-without-using-a-loop-tp2966191p2966191.html Sent from the R help

Folks, I'm a bit puzzled by the fact that if I generate 100,000 standard normal variates using rnorm() and perform the Jarque-Bera on the resulting vector, I get p-values that vary drastically from run to run. Is this expected? Surely the p-val should be close to 1 for each test? Are 100,000 variates sufficient for this test? Or is it that rnorm() is not a robust random number generator?

Folks, I have a 'matching' matrix between variables A, X, L, O: > a <- structure(c(1, 0.41, 0.58, 0.75, 0.41, 1, 0.6, 0.86, 0.58, 0.6, 1, 0.83, 0.75, 0.86, 0.83, 1), .Dim = c(4L, 4L), .Dimnames = list( c("A", "X", "L", "O"), c("A", "X", "L", "O"))) > a A X L O A 1.00 0.41

Hi folks, Not sure what this sort of estimation is called. I have a 2-column time-series x(i,t) [with (i=1,2; t=1,...T)], and I want to do the following 'simultaneous' regressions: x(1,t) = (d - 1)(x(1, t-1) - mu(1)) x(2,t) = (d - 1)(x(2, t-1) - mu(2)) And I want to determine the coefficients d, mu(1), mu(2). Note that the d should be the same for both estimations, whereas the

Hi all, I have a vector like this: x<- c(0.7, 0.1, 0, 0.2, 0.2, 0, 0, 0 , 0, 0.4, 0, 0.8, 1.8) I would like to replace the zero values with the first previous non zero value. my returning vector should look like this: y<-c( 0.7, 0.1, 0.1,0.2,0.2,0.2,0.2,0.2, 0.4, 0.4, 0.8, 1.8) How can I do this in R without using for loop? Thank you

Folks, I'm battling the layout() functionality in graphics, and getting a bit mixed up. I'd like to create subscreens like so: _________ _________ | | | | 1 | 2 | |_________|________ | | | | | 3 | 4 | |_________|_________| | |____6____| | 5 |____7____| |_________|____8____| Note that subscreens 1:5 are the same

NOTE: I will provide data if necessary, but I didn't want clutter everyones mailbox All: I have a time series with level and temperature data for 11 sites for each of three bases. I will have to do this more than once is what I am saying here. OK, The time series are zoo objects with index values in chron format. The problem is that the date and times should be at even 15 min intervals,

Folks, I have a matrix of historicalReturns, where entry (i, j) is the daily return corresponding to date i and equity j. I also have a matrix startOffset, where entry (1, k) is the row offset in historicalReturns where I entered into equity k. So we have that NCOL(startOffset) = NCOL(historicalReturns). Now I would like compute for each column in historicalReturns, the cumulative return

Folks, I have a dataframe comprising a column of dates and a column of signals (-1, 0, 1) that looks something like this: 30/01/2007 0 31/01/2007 -1 01/02/2007 -1 02/02/2007 -1 03/02/2007 1 04/02/2007 1 05/02/2007 1 06/02/2007 1 07/02/2007 1 08/02/2007 1 09/02/2007 0 10/02/2007 0 11/02/2007 0 12/02/2007 1 13/02/2007 1 14/02/2007 1 15/02/2007 0 16/02/2007 0 What I need to do is for each signal

Folks, I have the following dataframe: > x <- structure(list(name = c("EU B", "EU B", "EU B", "EU B", "EU B", "EU B", "AU A", "AU A", "AU A", "AU A", "AU A", "AU A"), date = c("2010-10-11", "2010-10-12", "2010-10-13",