rollapply

This question builds upon a previous one which was nicely answered for me here. R: Grouped rolling window linear regression with rollapply and ddply Wouldn't you know that the code doesn't quite work when extended to the real data rather than the example data? I have a somewhat large dataset with the following characteristics. str(T0_satData_reduced) 'data.frame': 45537 obs. of 5 variables: $ date : POSIXct, format: "2014-11-17 08:47:35" "2014-11-17 08:47:36" "2014-11-17 08:47:37" ... $ trial : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ... $ vial : Factor w/ 4 levels "1","2",

I'm trying to run rollapply from the bottom of my data.frame up to the top of my data.frame. Basically the last row in the data.frame (RBH) is the final measurement for a given subject in 2012. I then need to subtract each annual measurement in the previous years to calculate what the individual's size would have been each year prior. Sample data.frame: df1 <- structure(c(1.62, 3.96, 4.89, 6.61, 8.79, 57.15, 2.43, 5.58, 7.2, 9.3, 11.87, 66.6, 1.47, 3.49, 4.32, NA, NA, 60.75), .Dim = c(6L, 3L), .Dimnames = list(c("2008", "2009", "2010","2011", "2012","RBH"), c("Tree001", "Tree002", "Tree003")))

I want to calculate the rolling mean for all variables in column "sp". This is a sample of my data: the_date sp wins 01-06--2012 1 305 02-06--2012 1 276 03-06--2012 1 184 04-06--2012 1 248 05-06--2012 1 243 06-06--2012 1 363 07-06--2012 1 272 01-06--2012 2 432 02-06--2012 2 369 03-06--2012 2 302 04-06--2012 2 347 05-06--2012 2 357 06-06--2012 2 331 07-06--2012 2 380 01-06--2012 3 1 02-06--2012 3 2 03-06--2012 3 3 04-06--2012 3 2 05-06--2012 3 0 06-06--2012 3 2 07-06--2012 3 0 What I want, is to have a column added to data, that gives the moving average over 3 days for each sp. So the following

I have a data set with several grouping variables on which I want to run a rolling window linear regression. The ultimate goals is to extract the 10 linear regressions with the lowest slopes and average them together to provide a mean minimum rate of change. I have found examples using rollapply to calculate rolling window linear regressions, but I have the added complication that I would like to apply these linear regressions to groups within the data set. Here is a sample data set and my current code which is close and isn't quite working. dat<-data.frame(w=c(rep(1,27), rep(2,27),rep(3,27)),

I did the following for calculating Value at Risk (VaR) over 20 period rolling window: require(PerformanceAnalytics); require(zoo) data(edhec) class(edhec) # [1] "xts" "zoo" class(edhec$CTAGlobal) # "NULL" var1<-rollapply(edhec,width=20,FUN=function(edhec) VaR(R=edhec,p=.95,method="modified"),by.column=TRUE) It produces the desired output, and then I tried the same on another data: data(managers) class(managers) # [1] "xts" "zoo" class(managers$HAM4) # [1] "xts" "zoo" var2<-rollapply(managers,width=20,FUN=function(managers) VaR(R=managers,p=.95,method="modified"),by.column=TRUE) But I am

My imported data contains 7 variables: Y and X1 , X2 , X3 , X4 , X5 , X6 . I tried applying the rollapply function in zoo in order to run a rolling regression within an in-sample with a window of 262 obs. (work days in a year). date Y X1 X2 1 10/1/07 -0.0080321720 4.690734e-03 3.333770e-03 2 10/2/07 0.0000000000 -2.818413e-03 5.418223e-03 3 10/3/07 0.0023158650 -4.178744e-03 -3.821100e-04 4 10/4/07 -0.0057491710 -5.071030e-03 -8.321550e-04 5 10/5/07 0.0073570500 3.065045e-03 5.179574e-03 6 10/8/07 0.0127708010 -7.278513e-03 1.145395e-03 7 10/9/07 0.0032661980 9.692267e-03 6.514035e-03 8 10/10

I have a matrix in which each row is a sample from a distribution. I want to do a rolling comparison of the distributions using ks.test and save the test statistic in each case. The simplest way to implement this conceptually is with a loop: set.seed(1942) mt <- rbind(rnorm(5), rnorm(5), rnorm(5), rnorm(5)) results <- matrix(as.numeric(rep(NA, nrow(mt)))) for (i in 2 : nrow(mt)) { results[i] <- ks.test(x = mt[i - 1, ], y = mt[i, ])$statistic } However, my real data has ~400 columns and ~300,000 rows for a single example, and I have a lot of examples. So I'd like this to be fast. The Kolmogorov