Description Usage Arguments Value Examples
A function for computing the rolling and expanding sums of time-series data.
1 2 |
x |
vector or matrix. Rows are observations and columns are variables. |
width |
integer. Window size. |
weights |
vector. Weights for each observation within a window. |
min_obs |
integer. Minimum number of observations required to have a value within a window,
otherwise result is |
complete_obs |
logical. If |
na_restore |
logical. Should missing values be restored? |
online |
logical. Process observations using an online algorithm. |
An object of the same class and dimension as x
with the rolling and expanding
sums.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | n <- 15
x <- rnorm(n)
weights <- 0.9 ^ (n:1)
# rolling sums with complete windows
roll_sum(x, width = 5)
# rolling sums with partial windows
roll_sum(x, width = 5, min_obs = 1)
# expanding sums with partial windows
roll_sum(x, width = n, min_obs = 1)
# expanding sums with partial windows and weights
roll_sum(x, width = n, min_obs = 1, weights = weights)
|
[1] NA NA NA NA -1.07429503 -3.08441543
[7] -2.20722899 -1.35045526 -1.15847871 0.94197323 1.54884019 2.63020157
[13] 0.31663308 0.39906049 -0.08741767
[1] 1.43401919 0.66252949 0.83950107 -0.02768074 -1.07429503 -3.08441543
[7] -2.20722899 -1.35045526 -1.15847871 0.94197323 1.54884019 2.63020157
[13] 0.31663308 0.39906049 -0.08741767
[1] 1.43401919 0.66252949 0.83950107 -0.02768074 -1.07429503 -1.65039624
[7] -1.54469949 -0.51095419 -1.18615945 -0.13232180 -0.10155605 1.08550208
[13] -0.19432111 -0.78709896 -0.21973947
[1] 1.290617271 0.467214816 0.579767753 -0.258672647 -1.174758243
[6] -1.575773511 -1.323069089 -0.260391405 -0.842036995 0.190620586
[11] 0.199247705 1.247675246 -0.028933151 -0.559539898 0.007037635
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