similar to: moving average and NA values

Dear List, I'd like to make boxplots of a large number of observations (+/- 20.000), which are distributed log-normal and right skewed. The problem is that with standard boxplots a too large number of observations are displayed as outliers. I also tried to display the log of the observations, but even then there are to may outliers to my taste. So I'd like to change the standard IQR box

Hello, I was hoping for some advice to resolve a problem I am having trouble with. The data consists of a series of pre and post variables, in a dataframe called 'offend'. I am interested in graphically depicting the pre & post values for a factor variable called 'decision' which has 4 values : nusm, fit, unsound & unfit. An example of a pre and post variable is:

I have 12 binary (raster) files https://echange-fichiers.inra.fr/get?k=k3M2jatJyHy65Cs99G4 . I would like to calculate the moving average for the 12 values for each pixel in the 12 files. For a simple vector we can get a moving average by using this : x <- c(1,2,3,NA,NA,4,6,5,6,4,2,5) movingmean <- rollapply(x, 3, FUN = mean, na.rm = T,fill=NA) now

Dear all, I have weekly data by city (variable citycode). I would like to take the average of the previous two, three, four weeks (without the current week) of the variable called value. This is what I have tried to compute the average of the two previous weeks; df = df %>% mutate(value.lag1 = lag(value, n = 1)) %>% mutate(value .2.previous = rollapply(data = value.lag1,

# 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 all, I wonder if there is any way to calculate a moving average on an irregular time series, or use the rollapply function in zoo? I have a set of dates where I want to check if there has been an event 14 days prior to each time point in order to mark these timepoints for removal, and can't figure out a good way to do it. Many thanks in advance! Gustaf Example data:

Dear List, Trying to explain my situation as simply as possible for me: I am running a series of iteration on coxph model on simulated data (newly generated data on each iteration to run under coxph; in my example below- sim.fr is the generated data). However, sometimes i get warning messages like "Ran out of iterations and did not converge" or "Error in var(x, na.rm = na.rm) :

#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,

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, 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, I'm trying to figure out common approach on calculating MA on a dataset that contains column "time". After digging around, I believe functions rollmean and rollaply should be used. However I don't quite understand the requirements for the underlying data. Should it be zoo object type? Formatted in a special way? As an example, I'm looking to get calculated

I am sure that this sort of thing has been asked and answered before, so in case my suggestions don't work for you, just search the archives a bit more. I am also sure that it can be handled directly by numerous functions in numerous packages, e.g. via time series methods or by calculating running means of suitably shifted series. However, as it seems to be a straightforward task, I'll

Hello everyone, I have a dataframe with 1 column and I'd like to replace that column with a moving average. Example: > library('zoo') > mydat <- seq_len(10) > mydat [1] 1 2 3 4 5 6 7 8 9 10 > df <- data.frame("V1" = mydat) > df V1 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 > df[df$V1 <- rollapply(df$V1, 3, mean)]

Hi I would like to understand how to extend the function (FUN) I am using in rollapply below. ###################################### With the following simplified data, test1 yields parameters for a rolling regression data = data.frame(Xvar=c(70.67,70.54,69.87,69.51,70.69,72.66,72.65,73.36), Yvar =c(78.01,77.07,77.35,76.72,77.49,78.70,77.78,79.58)) data.z = zoo(d) test1 =

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)

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()

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

Dear Bert, Thank you very much.This works. I was wondering if the fact that I want to create new variables (sorry for not stating that fact) makes any difference? Thank you again. Sincerely, Milu On Sun, Mar 25, 2018 at 10:05 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > I am sure that this sort of thing has been asked and answered before, > so in case my suggestions

Hi, I need to perform calculations on subsets of a data frame: DF = data.frame(read.table(textConnection(" A B C D E F 1 a 1995 0 4 1 2 a 1997 1 1 3 3 b 1995 3 7 0 4 b 1996 1 2 3 5 b 1997 1 2 3 6 b 1998 6 0 0 7 b 1999 3 7 0 8 c 1997 1 2 3 9 c 1998 1 2 3 10 c 1999 6 0 0 11 d 1999 3 7 0 12 e 1995 1 2 3 13 e 1998 1 2 3 14 e 1999 6

I am looking for some help at removing low-frequency components from a signal, through Moving Average on a sliding window. I understand thiis is a smoothing procedure that I never done in my life before .. sigh. I searched R archives and found "rollmean", "MovingAverages {TTR}", "SymmetricMA". None of the above mantioned functions seems to accept the smoothing