similar to: Rollapply

zoo's rollapply() function appears to be extremely useful for plugging in a function on-the-fly to run over a window. With inline, there is a lot more coding and room for error, and the code is less portable because the user has to have R compiling set up or it won't work. However, rollapply() seems to be really slow. Several orders of magnitude slower than inline, in fact. I don't

Hi everyone, I have a rollapply statement that applies a function, in steps, over a data matrix as follows: #Code start testm<-rollapply(mat, 100, by=100, min, na.rm=F) #Code end This moves down matrix 'mat' and calculates the minimum value over a 100 row range, every 100 rows (i.e. no overlaps). NAs are not removed. I want to modify this statement somehow so that the rollapply()

Hi. I'm comparing output from rollapply.zoo, as produced by two versions of R and package zoo. I'm illustrating with an example from a R-help posting 'Zoo - bug ???' dated 2010-07-13. My question is not about the first version, or the questions raised in that posting, because the behaviour is as documented. I'm puzzled as to why na.rm no longer is passed to mean, i.e. why

Hi, Sorry ahead of time for not including data with this question. Using rollapply to calculate mean values for 5 day blocks, I'd use this: Roll5mean <- rollapply(data, 5, mean, by=5, align = c("left")) My question is, can someone tell me how to fill in the days between each of these means with the previously calculated mean? If this doesn't make sense, I will clarify and

# Hi there, # I am trying to apply a function over a moving-window for a large number of multivariate time-series that are grouped in a nested set of factors. I have spent a few days searching for solutions with no luck, so any suggestions are much appreciated. # The data I have are for the abundance dynamics of multiple species observed in multiple fixed plots at multiple sites. (I total I

Hi all- Thank you for reading my post. Please bear in mind that I'm very much a newbie with R! My question is this: I'm trying to use rollapply() on an irregular time series so I can't simply use the width parameter (I don't think). Rather than last 5 entries, I'd like to rollapply on last 6 months (for example). What would be the proper course of action for this? Thanks!

Hi, I faced with problem when start using function - rollapply(returns, 3 , mean) Error in UseMethod("rollapply") : No suitable Method for "rollaply" How can I fix the problem? Thank you for help. -- Best regards, Andy Fetsun [[alternative HTML version deleted]]

#using "rollapply" to calculate a moving sum or running sum? #I am tryign to use rollapply to calcualte a moving sum? #I tried rollapply and get the error message #"Error in seq.default(start.at, NROW(data), by = by) : # wrong sign in 'by' argument" #example: mymatrix <- ( matrix(data=1:100, nrow=5, ncol=20) ) mymatrix_cumsum <- ( matrix(data=NA, nrow=5,

Here is a relatively simple script (with comments as to the logic interspersed): # Some of these libraries are probably not needed here, but leaving them in place harms nothing: library(tseries) library(xts) library(quantmod) library(fGarch) library(fTrading) library(ggplot2) # Set the working directory, where the data file is located, and read the raw data

Dear Colleagues, I have faced with the problem that function rollaply with rolling window for calculation of volatility doesn't give the all results of calculations. I have run the rolling window for calculation in Excel and obtained that the number of outputs for Excel is 36 and for R is 18. The total number of observations is 37. In the attachment you can find pdf of the Excel and Excel

Hi, That's what the DecoderMethod is for. Similarly ParserMatchClass for the asm parser and PrintMethod for the asm printer: def CondCodeOperand : AsmOperandClass { let Name = "CondCode"; } def pred : PredicateOperand<OtherVT, (ops i32imm, i32imm), (ops (i32 14), (i32 zero_reg))> { let PrintMethod = "printPredicateOperand";

Dear R users. I have zoo object "size_june" containing market-capital values: > dim(size_june) # market-cap data of 625 firms for 20 years [1] 20 625 > class(size_june) [1] "zoo" > size_june # colnames = "size.firmcode" size.34020 size.4710 size.11050 size.10660 size.9540 size.8060 size.16160 size.8080 size.9280 1988-06-30 NA

I just updated rpart to the latest version (3.1.0). There are a number of changes between this and previous versions, and some of the code I've been using with earlier versions (e.g. 3.0.2) no longer work. Here is a simple illustration of a problem I'm having with xpred.rpart. iris.test.rpart<-rpart(iris$Species~., data=iris[,1:4], parms=list(prior=c(0.5,0.25, 0.25))) + ) >

Hello James, that was also what I've planned to do but just wasn't sure. Thanks for that. On Mon, Dec 14, 2015 at 11:52 AM, James Molloy <james at jamesmolloy.co.uk> wrote: > Hi, > > You can't nest operands like that - it must be a flattened list. So: > > def *Xpred* : PredicateOperand<OtherVT, (ops *i32imm, i32imm*, i32imm), > (ops (i32 14), (i32

Hello I have a vector a =(-2,0,0,0,1,0,0,3,0,0,-4) I want to replace all zeros into previous non-zero state. So for instance the above vector should be converted into: a= (-2,-2,-2,-2,1,1,1,3,3,3,-4) I tried many things and finally concluded that probably(?) rollapply may be the best way? I tried f= function(x){ ifelse(x==0,Lag(x),x) } And then, rollappy(a,1,f) and that

Hi All, In ARMInstFormats.td predicate is defined this way: *def pred : PredicateOperand<OtherVT, (ops i32imm, i32imm),* *(ops (i32 14), (i32 zero_reg))> {...}* I use the same definition in my code. But I have another version of predicate which is exactly the same but it is a condition code plus a quantifier! (e.g. Xpred = (pred + i32imm)). I was wondering how we can define a sub sub

Dear List members I want to do the sliding window analysis of some specific values. Here is my code: require(zoo) dat <- read.table("chr1.txt", header = TRUE, sep="\t") dat2 <- cbind(dat[1,3]) #The first column is also important. It represents the position of the site on the chromosome. TS <- zoo(c(dat2)) a <- rollapply(TS, width=1000000, by=200000, FUN=mean,

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

# need zoo to use rollapply() # your data (I called df) df <- structure(list(a = 1:2, b = 2:3, c = c(5L, 9L), d = c(9L, 6L), e = c(1L, 5L), f = c(4, 7)), .Names = c("a", "b", "c", "d", "e", "f"), class = "data.frame", row.names = c(NA, -2L)) # transpose and make a zoo object df2 <- zoo(t(df)) #rollapply to get

Hi folks, I am confused whether the following is a bug or it is fine Here is the explanation a <- zoo(c(NA,1:9),1:10) Now If I do rollapply(a,FUN=mean,width=3,align="right") I get > rollapply(a,FUN=mean,width=3,align="right") 3 4 5 6 7 8 9 10 NA NA NA NA NA NA NA NA But I shouldn't be getting NA right ? i.e for index 10 I should get (1/3)*(9+8+7)