similar to: cumsum

I want to display cumulative summary functions with lattice. First I tried to get cumulated data: library(lattice) data(barley) d.cum <- with( barley, by( yield, INDICES=list(site=site,year=year), FUN=cumsum ) ) I got a list of vectors. I tried to get a dataframe which I could use in xyplot. But neither of the following functions led to the goal: d.cum.df1 <-

>>>>> 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]

Apologies for the empty email earlier! I have species abundance data sampled at a weekly frequency or sometimes monthly depending on the year. The goal is to identify the dates in an annual cycle in which the cumulative abundance of a species reaches some threshold. Here's an example of the data for 1 species over an annual period: "mc_pheno" is the object created from this data:

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'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 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! 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

Dear R users, I am using R version 2.0.1 (2004/11/15) on an i386-pc-mingw32 platform. I encounter the following problem while using cumsum: > a <- rep(0.01, 100) > b <- cumsum(a) > sum(a) == 1 [1] TRUE > b[100] == 1 [1] FALSE Am I missing something? Should cumsum have such an outcome? Thanks in advance for any clarifications any of you can offer. Regards, Makram Talih --

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,]

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

I have a question about using cumsum on subsets of a data frame. Suppose I have a frame that looks something like this > tmp f x y 1 left 1 0 2 left 2 0 3 left 3 9 4 left 4 10 5 left 5 23 6 left 6 45 7 left 7 13 8 left 8 2 9 left 9 6 10 right 1 10 11 right 2 26 12 right 3 9 13 right 4 50 14 right 5 78 15 right 6 20 16 right 7 7 17 right 8 20 18 right 9 19

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

When running a = runif(10) class(a) = "foo" Math.foo = function(x, ...) { NextMethod(.Generic) } signif(a, 3) cumsum(a) I don't understand why cumsum strips the class, but signif does not. Both claim in the documentation that "These are generic functions: methods can be defined for them individually or via the ?Math? group generic." -- Edzer Pebesma Institute for

Dear list, I have a problem with the cumsum function. I have a data frame like the following one variable Year value EC01 2005 5 EC01 2006 10 AAO1 2005 2 AAO1 2006 4 what I would like to obtain is variable Year value cumsum EC01 2005 5 5 EC01

I need to do a simple cumulative sum by group and add the result to the data. I found an earlier thread in the help files with a few suggestions. Somewhat, one of the suggestions does not work with "my data", and I don't really understand why ? The error am getting using the "my data" below is... Error in data.frame(..., check.names = FALSE) : arguments imply

Dear R-users, I have a dataset with categories and numbers. I would like to compute and add cumulative numbers to the dataset. I do not understand the structure of by(...) or tapply(...) output enough to handle it. Here a small example -------------- d<-expand.grid(a=1:5,b=1:3,c=1:2) d$n = 10 * d$a + d$b +0.1* d$c Sn<-by(d$n,list(d$a,d$c),cumsum) str(Sn) --------- List of 10 $ : num

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 useRs, I am trying to calculate the Sharpe ratio with "sharpe" of the library "tseries". The documentation requires the univariate time series to be a portfolio's cumulated returns. In this case, the example given data(EuStockMarkets) dax <- log(EuStockMarkets[,"FTSE"]) is however not the cumulated returns but rather the daily returns of the FTSE