similar to: simultaneous estimation

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,

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

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

Hi folks, I want to sort a matrix row-by-row and create a new matrix that contains the corresponding colnames of the original matrix. E.g. > set.seed(123) > a <- matrix(rnorm(20), ncol=4); colnames(a) <- c("A","B","C","D") > a A B C D [1,] -0.56047565 1.7150650 1.2240818 1.7869131 [2,]

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",

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, If I have a vector such as the following: x <- c(0, -1, -1, -1, 0, 0, 1, -1, 1, 0) and I want to replace the zeroes by the nearest non-zero number to the left, is there a more elegant way to do this than the following loop? y <- x for (i in 2 : length(x)) { if (y[i] == 0) { y[i] <- y[i - 1] } } > y [1] 0 -1 -1 -1 -1 -1 1 -1 1 1 You can see the

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 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'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?

Hi Eric I did not see any answer and frankly speaking I cannot provide you with canned help. AFAIK if a function is defined within another function (which is your case) it cannot be called directly so it is necessary to define it in global environment. > fff <- function(x) { + myf <- function(a) a+2 + myf(x)^2} > > fff(5) [1] 49 > myf(5) Error in myf(5) : could not find

Hi Petr, Many thanks for your response. Basically I want to create a probability matrix to be used in a trinomial tree going forward. This is the reason why I thought to build the matrix around 0 would be much more efficient. I need to loop through because the probabilities will depend on my node and is not always the same per row (e.g. if N> jmax, jmax being defined in another function) I

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) <-

I want to populate the matrix prb through the function HWMProb <- function (a,j,dt) that encapsulates different functions (please see code below), using j= 0:2 for each j. It only populates prb if I specify each function independently in the global environment and then run the loop with the iF statement, as per below. for (j in 0:2) { if (j==0) { prb["0","1"] <-

Hi Well, I am not an expert in this field so I cannot comment your approach. I wanted only to point out that building matrix your way is like scratching your left ear with right hand, especially in R. What if you want increase size of your matrix? E.g. you use function ProbUP once for row "0" and than for rows different from jmax (if I correctly understand your code). Use of any

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'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