Description Usage Arguments Value Examples
A function for computing the rolling and expanding products 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
products.
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 products with complete windows
roll_prod(x, width = 5)
# rolling products with partial windows
roll_prod(x, width = 5, min_obs = 1)
# expanding products with partial windows
roll_prod(x, width = n, min_obs = 1)
# expanding products with partial windows and weights
roll_prod(x, width = n, min_obs = 1, weights = weights)
|
[1] NA NA NA NA -0.021808468
[6] 0.158026328 -0.048749341 -0.015620696 -0.006469362 -0.187674764
[11] -0.052786934 0.050418075 0.033093285 0.067464126 0.005949360
[1] 0.139780481 -0.263427397 -0.507157189 0.512125227 -0.021808468
[6] 0.158026328 -0.048749341 -0.015620696 -0.006469362 -0.187674764
[11] -0.052786934 0.050418075 0.033093285 0.067464126 0.005949360
[1] 1.397805e-01 -2.634274e-01 -5.071572e-01 5.121252e-01 -2.180847e-02
[6] 2.208900e-02 1.284191e-02 7.922148e-03 -3.313123e-03 4.092899e-03
[11] -1.166010e-03 6.474645e-04 2.621699e-04 -2.235170e-04 2.435013e-05
[1] 1.258024e-01 -1.920386e-01 -2.695241e-01 1.785670e-01 -4.490170e-03
[6] 2.416956e-03 6.720784e-04 1.784733e-04 -2.891682e-05 1.245573e-05
[11] -1.113546e-06 1.746352e-07 1.797427e-08 -3.505694e-09 7.863256e-11
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