similar to: Moving Averages in ggplot2

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

Hi all R users, Can anyone please let me know how to do the moving average with R? With regards, Abu _________________________________________________________________ Free games, great prizes - get gaming at Gamesbox.

Env: Win XP, R 2.9.2] It's a minor annoyance, but I find that after I run example(), the console state of prompting to press Enter for each new graph remains as it is under example. It's more annoying that it seems difficult to turn this behavior off! In a fresh session: > getOption("example.ask") [1] "default" Run an example: > example(plot) plot>

Good morning all! I am attempting to superimpose a moving-average smoother onto a graph of daily plots. These plots (in table[,2] below) span about 350 days and looks very noisy. I'd like for this smoother to plot the average of each group of 7 consecutive days (weekly) and show a line which joins these series of averages. Given the definition of MA, the first and last points will

Hi lattice users, I would like to add 5-year moving averages to my double y-plot. I have three factors needs to be plotted with moving averages in the same plot. One of these reads off y-axis 1 and two from y-axis 2. I have tried to use the rollmean function from the zoo-packages, but I fail in insering this into lattice (I am not an experienced lattice user). I want to keep the data points in

Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

Dear list, My name is Juan Fernandez-Tajes and I´m trying to use MALDIquant for analysing MS data from ABI 4700 series, I´ve exported a spot set to T2Dextractor in order to convert in mzXML format. I´ve imported this into R with mqReadMzXml function and have followed the suggested flowchart, however when I´m in the step for peak detections I obtained the following error: peaks <-

Dear List, I have a data frame with approximately 500000 rows that looks like this: ?Date??? time??? value ? 19.07.1956????????? 12:00:00?????????????? 4.84 19.07.1956????????? 13:00:00?????????????? 4.85 19.07.1956????????? 14:00:00?????????????? 4.89 19.07.1956????????? 15:00:00?????????????? 4.94 19.07.1956????????? 16:00:00?????????????? 4.99 19.07.1956????????? 17:00:00?????????????? 5.01

hello I'm learning mgcv and would like to obtain numerical output corresponding to plot.gam. I can do so when seWithMean=FALSE (the default) but only approximately when seWithMean=TRUE. Can anyone show how to obtain the exact values? Alternatively, can you clarify the explanation in the manual "Note that, if seWithMean=TRUE, the confidence bands include the uncertainty about the

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

Hi, First, I do some calculation, then a plot. Add some lines and texts to the plot. Second, do some further calculation, then a plot. Add again some lines and texts to the plot. Third, do some further calculation, then.... Fourth, ..... After a plot is complete (means the plot itself, lines and texts) I would like to click 'enter' to see the next complete plot (again the plot itself,

Hello, I am trying to calculate a 31 day running mean in some temperature data along ROWS. Rollmean() works great along columns, but how do I perform this same action on my rows? The data is a matrix of 365 columns (days of the year) by 5,000 rows (lat/long coordinates). I would like to perform a 31 day running mean along the 365 days. I am new to R so any help would be greatly appreciated!

Hello All: If I do have: x = (2, 4, 5, 5, 6, 4, 5, 2, 1) y = (9, 11.5, 12.5, 13, 14, 19, 20, 21, 22) I wanted to find a simple function in R which calculates the averages of X and Y for every 4 unit increase of Y. The results should look like: x = (4, 6, 3) #where (2+ 4+ 5+ 5)/ 4 = 4 and ... y = (12, 14, 20.5) #where (9+11.5+12.5+13)/ 4 = 12 and ... Thanks, Mohsen

Hi, I just wanted to know if anyone else is encountering error when compiling Evolution on FreeBSD 4.8 or 4.9. I have problems compiling e- util section of this port.

Hello, I'm new to R and would like to know how to create a vector of "rolling sums". (I have seen the Rmetrics package and the rollMean function and I would like to do the same thing except Sum instead of Mean.) I imagine someone has done this, I just can't find it anywhere. Example: x <- somevector #where x is 'n' entries long #what I would like to do is: x1

Hello useRs, I'd like to perform a moving average on the dataset, xx. I've tried combining the functions Reduce and rollmean but it didn't work. > r <- function(n) rollmean(n, 2) # where 2 = averaging interval > output < - Reduce("r", x) Error in f(init, x[[i]]) : unused argument(s) (x[[i]]) Is there anything wrong with the code in the first place? where

Dear all, With normal plotting, one can size a set of points in a plot using a vector argument to cex in the points() function. This works whether you are using one of the standard R symbols (i.e. 19+) or some ascii symbol, such as '/' eg: plot(1:10, 1:10, type='n'); points(1:10, 1:10, cex = 1:10, pch = '/') Trying to make the transition to ggplot2, I find that the

Dear all, I am having a vector of around 300.000 elements and I Want to slide fast a window from the first element until the last-Windowsize what I have so far is the following for statement:  for (i in 1:(length(data[,1]) - windowSize)) {         out[i] <- mean(data[i:(i + windowSize - 1), ])         elements[i]<-length(i:(i + windowSize - 1))       } but this of course takes ages to

Hi R-devel. I've noticed a couple of quirks in the current time/date axis functions (axis.Date, axis.POSIXct, and the equivalents in lattice). Looking at the code, it seems like a fairly ad-hoc approach, often using pretty() on components of the time. This is not always ideal - for example a one-hour interval gets cut into 10-minute chunks rather than the more natural 15-minute chunks (since