similar to: trouble with cumsum?

Dear R-users, Is there a preferred method for testing whether a real symmetric matrix is positive definite? [modulo machine rounding errors.] The obvious way of computing eigenvalues via "E <- eigen(A, symmetric=T, only.values=T)$values" and returning the result of "!any(E <= 0)" seems less efficient than going through the LU decomposition invoked in

Dear R users, I get the following (I think puzzling) result when doing the following: > a <- list(3,4,5) > a[[2]] <- NULL > a [[1]] [1] 3 [[2]] [1] 5 I would have expected the result to be: [[1]] [1] 3 [[2]] NULL [[3]] [1] 4 as in the outcome of: > list(3, NULL, 4) Is this a desired effect? If so, could it be built in a 'help(NULL)' file? If you think it is

Hello all, I've put together a quick and dirty menubar + dialogs + spreadsheet GUI for R using the RGtk package. Performance is not great (OOP is a real memory hog?), the design may be worse, but the hope is that it will be useful in an introductory stats course while we await the arrival of a real gui with ObveRsive and SciViews. The package can be found at

Hello all, I've put together a quick and dirty menubar + dialogs + spreadsheet GUI for R using the RGtk package. Performance is not great (OOP is a real memory hog?), the design may be worse, but the hope is that it will be useful in an introductory stats course while we await the arrival of a real gui with ObveRsive and SciViews. The package can be found at

Hello! Is it posible to apply /cumsum()/ along the 3rd dimension of 3D array? Something like matrlab function - /cumsum (*A*,dim)/ which returns the cumulative sum of the elements along the dimension of *A* specified by scalar dim. Thanks in advance ?eljka -- View this message in context: http://r.789695.n4.nabble.com/cumsum-in-3d-arrays-tp4110470p4110470.html Sent from the R help mailing

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

Hi I noticed a small inconsistency when using sum() vs cumsum() I have a char-based series > tryjpy$long [1] "0.0022" "-0.0002" "-0.0149" "-0.0023" "-0.0342" "-0.0245" "-0.0022" [8] "0.0003" "-0.0001" "-0.0004" "-0.0036" "-0.001" "-0.0011"

I've a matrix like this: 1985 1.38 1.27 1.84 2.10 0.59 3.47 1986 1.05 1.13 1.21 1.54 0.21 2.14 1987 1.33 1.21 1.77 1.44 0.27 2.85 1988 1.86 1.06 2.33 2.14 0.55 1.40 1989 2.10 0.65 2.74 2.43 1.19 1.45 1990 1.55 0.00 1.59 1.94 0.99 2.14 1991 0.92

Hi, my problem belongs to the basic ones. I want to get cumulated sum over the matrix columns by one command (if such exists). Ordinary R's cumsum(x) when x is: [,1] [,2] [1,] 1 5 [2,] 2 6 [3,] 3 7 [4,] 4 8 yields: 1 3 6 10 15 21 28 36 I want: [,1] [,2] [1,] 1 5 [2,] 3 11 [3,] 6 18 [4,] 10 26 Is there any command to do so?? best,

>>>>> Tomas Kalibera >>>>> on Tue, 25 Aug 2020 09:29:05 +0200 writes: > On 8/23/20 5:02 PM, Rory Winston wrote: >> Hi >> >> I noticed a small inconsistency when using sum() vs cumsum() >> >> I have a char-based series >> >> > tryjpy$long >> >> [1]

I'm trying to create an xyplot with a "groups" argument where the y-variable is the cumsum of the values stored in the input data frame. I almost have it, but I can't get it to automatically adjust the y-axis scale. How do I get the y-axis to automatically scale as it would have if the cumsum values had been stored in the data frame? Here is the code I have so far:

I would like to cumulatively sum rows in a matrix, in which each row has 1 NA value. The usual "na.rm=TRUE" does not seem to work with the command cumsum. Is there another way to ignore the NAs or do I need to figure out a different way to do this? Here's an example matrix of title "proportion": Ntrail Strail NFJD Baldy Onion Crane [1,]

Hi all, I have a frequency data that looks like this. 3 2 1 5 What I want to get is the "decreasing" cumulative of this data yielding 11 8 6 5 0 Is there any? I am aware of cumsum(), which will yield 3 5 6 11. But it is not what I want. - G.V.

This code gives unexpected result. library(data.table) library(lattice) set.seed(123) mydt <- data.table(date = seq.Date(as.IDate("2024-01-01"), by = 1, length.out = 50), xgroup = "A", x = runif(50, 0, 1)) mydt <- rbindlist(list(mydt, data.table(date = mydt$date, xgroup = "B", x = runif(50, 0, 3)))) mydt[, `:=`(xcumsum = cumsum(x)), by = .(xgroup)] mydt[,

I would like to cumulatively sum rows in a matrix, in which each row has 1 NA value, which I do NOT want to treat as 0s. The NAs are placeholders where there is actually no data, which is not the same as a 0. The usual "na.rm=TRUE" does not seem to work with the command cumsum. Is there another way to ignore the NAs or do I need to figure out a different way to do this? Here's an

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

Hi, First post here. Grateful for any help you can give. I have data which looks like this: id time x 1 12:01 5 1 12:02 14 1 12:03 6 1 12:04 3 2 12:01 98 2 12:02 23 2 12:03 1 2 12:04 4 3 12:01 5 3 12:02 65 3 12:03 23 3 12:04 23 But I want to add a column which is the cumulative sum of X, but only by id. I've used cumsum before, but not

Is there anything like cumsum and cumprod but which allows you to apply an arbitrary function instead of sum and product? In other words, I want a function cumfunc(x, f) that returns a vector, so that for all n up to the length of x cumapply(x,f)[n] = f(x[1:n]) This would give cumsum and cumprod as special cases when f=sum or f=prod. I could write such a function, but I can't see

Hi All, I have a vector n, and for each n[i] I want to extract n[i], n[i+1], n[i+2]..., until the cumulative sum of n[i] and subsequent elements exceeds a CheckValue, whereupon I move to the next index and repeat. I am trying to find a Vectorized approach, and have seen similar posts where filter{stat} and rollmean{zoo} were suggested, but, I haven't been able to figure a way to use them

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