roll_scale: Rolling Scaling and Centering
In roll: Rolling and Expanding Statistics

Description Usage Arguments Details Value Examples

A function for computing the rolling and expanding scaling and centering of time-series data.

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roll_scale(x, width, weights = rep(1, width), center = TRUE,
  scale = TRUE, min_obs = width, complete_obs = FALSE,
  na_restore = FALSE, online = TRUE)

`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 `TRUE` then the weighted mean of each variable is used, if `FALSE` then zero is used.
`scale`	logical. If `TRUE` then the weighted standard deviation of each variable is used, if `FALSE` then no scaling is done.
`min_obs`	integer. Minimum number of observations required to have a value within a window, otherwise result is `NA`.
`complete_obs`	logical. If `TRUE` then rows containing any missing values are removed, if `FALSE` then each value is used.
`na_restore`	logical. Should missing values be restored?
`online`	logical. Process observations using an online algorithm.

If center is TRUE then centering is done by subtracting the weighted mean from each variable, if FALSE then zero is used. After centering, if scale is TRUE then scaling is done by dividing by the weighted standard deviation for each variable if center is TRUE, and the root mean square otherwise. If scale is FALSE then no scaling is done.

The denominator used gives an unbiased estimate of the standard deviation, 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 scaling and centering.

n <- 15
x <- rnorm(n)
weights <- 0.9 ^ (n:1)

# rolling z-scores with complete windows
roll_scale(x, width = 5)

# rolling z-scores with partial windows
roll_scale(x, width = 5, min_obs = 1)

# expanding z-scores with partial windows
roll_scale(x, width = n, min_obs = 1)

# expanding z-scores with partial windows and weights
roll_scale(x, width = n, min_obs = 1, weights = weights)

 [1]         NA         NA         NA         NA -0.8539589 -1.2334460
 [7]  1.6838749  0.3967620 -0.6937601 -0.8960314  0.4494436  0.9738593
[13]  0.6034998  0.7887685 -1.6834170
 [1]         NA  0.7071068 -1.1225621 -0.4960434 -0.8539589 -1.2334460
 [7]  1.6838749  0.3967620 -0.6937601 -0.8960314  0.4494436  0.9738593
[13]  0.6034998  0.7887685 -1.6834170
 [1]         NA  0.7071068 -1.1225621 -0.4960434 -0.8539589 -1.4179348
 [7]  1.7097645  0.4303983 -0.9881007 -1.3593633  0.7793457  1.0397202
[13]  0.6364000  1.1954772 -0.8812297
 [1]         NA  0.6698906 -1.0392796 -0.4275413 -0.7703709 -1.2747856
 [7]  1.5424086  0.3677354 -0.9025327 -1.1745598  0.7713885  0.9502773
[13]  0.5503598  1.0261921 -0.8998317