standard-deviation

问题 I am trying to implement a vectorized exponentially weighted moving standard deviation using R. Is this the correct approach? ewma <- function (x, alpha) { c(stats::filter(x * ratio, 1 - ratio, "recursive", init = x[1])) } ewmsd <- function(x, alpha) { sqerror <- na.omit((x - lag(ewma(x, ratio)))^2) ewmvar <- c(stats::filter(sqerror * ratio, 1 - ratio, "recursive", init = 0)) c(NA, sqrt(ewmvar)) } I'm guessing it's not, since its output is different from Python's pandas.Series.ewm.std()

问题 I am trying to implement a vectorized exponentially weighted moving standard deviation using R. Is this the correct approach? ewma <- function (x, alpha) { c(stats::filter(x * ratio, 1 - ratio, "recursive", init = x[1])) } ewmsd <- function(x, alpha) { sqerror <- na.omit((x - lag(ewma(x, ratio)))^2) ewmvar <- c(stats::filter(sqerror * ratio, 1 - ratio, "recursive", init = 0)) c(NA, sqrt(ewmvar)) } I'm guessing it's not, since its output is different from Python's pandas.Series.ewm.std()

问题 I'm trying to write a Java program to calculate the maximum, minimum, mean and standard deviation after reading a text file full of floating point numbers. As you can see, I've calculated the max, min, mean, but for the standard deviation, I'm confused. Any ideas how I should implement this? Also, I'm fairly new to programming, so sorry if things aren't structured correctly. Here's my code: /* * Create a Java program to read a file of floating point numbers and compute * the following

问题 I'm trying to write a Java program to calculate the maximum, minimum, mean and standard deviation after reading a text file full of floating point numbers. As you can see, I've calculated the max, min, mean, but for the standard deviation, I'm confused. Any ideas how I should implement this? Also, I'm fairly new to programming, so sorry if things aren't structured correctly. Here's my code: /* * Create a Java program to read a file of floating point numbers and compute * the following

问题 I got the following dataset: data <- read.table(text=" wake_time sleep_time 08:38:00 23:05:00 09:30:00 00:50:00 06:45:00 22:15:00 07:27:00 23:34:00 09:00:00 23:00:00 09:05:00 00:10:00 06:40:00 23:28:00 10:00:00 23:30:00 08:10:00 00:10:00 08:07:00 00:38:00", header=T) I used the chron-package to calculate the average wake_time: > mean(times(data$wake_time)) [1] 08:20:12 But when I do the same for the variable sleep_time, this happens: > mean(times(data$sleep_time)) [1] 14:04:00 I guess the

来源： https://stackoverflow.com/questions/51609299/python-np-lognormal-gives-infinite-results-for-big-average-and-st-dev

来源： https://stackoverflow.com/questions/51609299/python-np-lognormal-gives-infinite-results-for-big-average-and-st-dev

来源： https://stackoverflow.com/questions/49961165/how-can-compute-rolling-window-for-standard-deviation-in-r

来源： https://stackoverflow.com/questions/12310075/awk-search-and-calculate-standard-deviation-different-results

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r