Description Usage Arguments Details Value Examples
A function for computing the rolling and expanding variances of time-series data.
1 2 3 |
x |
vector or matrix. Rows are observations and columns are variables. |
width |
integer. Window size. |
weights |
vector. Weights for each observation within a window. |
center |
logical. If |
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. |
The denominator used gives an unbiased estimate of the variance,
so if the weights are the default then the divisor n - 1
is obtained.
An object of the same class and dimension as x
with the rolling and expanding
variances.
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 variances with complete windows
roll_var(x, width = 5)
# rolling variances with partial windows
roll_var(x, width = 5, min_obs = 1)
# expanding variances with partial windows
roll_var(x, width = n, min_obs = 1)
# expanding variances with partial windows and weights
roll_var(x, width = n, min_obs = 1, weights = weights)
|
[1] NA NA NA NA 0.9055892 0.4613841 0.4474906
[8] 0.5142274 0.1290636 0.1637473 0.1607914 0.3390854 0.3580176 0.5441532
[15] 0.4990635
[1] NA 1.6340998 0.8756766 1.1136935 0.9055892 0.4613841 0.4474906
[8] 0.5142274 0.1290636 0.1637473 0.1607914 0.3390854 0.3580176 0.5441532
[15] 0.4990635
[1] NA 1.6340998 0.8756766 1.1136935 0.9055892 0.7385328 0.6466820
[8] 0.5750886 0.5032321 0.4855500 0.4372868 0.4782188 0.4598313 0.4771764
[15] 0.4780957
[1] NA 1.6340998 0.8225078 1.0689141 0.8667009 0.6775731 0.5694387
[8] 0.4935989 0.4070521 0.3979721 0.3370032 0.4226713 0.3949314 0.4458724
[15] 0.4505977
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