Description Usage Arguments Details Value Examples
A function for computing the rolling and expanding correlations of time-series data.
1 2 3 |
x |
vector or matrix. Rows are observations and columns are variables. |
y |
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 |
scale |
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 covariance,
so if the weights are the default then the divisor n - 1
is obtained.
A cube with each slice the rolling and expanding correlations.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | n <- 15
x <- rnorm(n)
y <- rnorm(n)
weights <- 0.9 ^ (n:1)
# rolling correlations with complete windows
roll_cor(x, y, width = 5)
# rolling correlations with partial windows
roll_cor(x, y, width = 5, min_obs = 1)
# expanding correlations with partial windows
roll_cor(x, y, width = n, min_obs = 1)
# expanding correlations with partial windows and weights
roll_cor(x, y, width = n, min_obs = 1, weights = weights)
|
[1] NA NA NA NA 0.33724365 0.28865402
[7] 0.04487921 0.16373152 0.64587810 0.50501936 0.42060940 0.32096086
[13] 0.41331235 0.38439764 -0.39328160
[1] NA 1.00000000 0.98403738 0.96306904 0.33724365 0.28865402
[7] 0.04487921 0.16373152 0.64587810 0.50501936 0.42060940 0.32096086
[13] 0.41331235 0.38439764 -0.39328160
[1] NA 1.00000000 0.98403738 0.96306904 0.33724365 0.31060262
[7] 0.31094543 0.26501712 0.27940252 0.29583840 0.30046228 0.29792730
[13] 0.30018483 0.32551590 -0.02714091
[1] NA 1.0000000 0.9858100 0.9611817 0.2637626 0.2783842
[7] 0.2827871 0.2430799 0.2628369 0.2779203 0.2923160 0.2883164
[13] 0.2914858 0.3328589 -0.2241375
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