compute_helpers: Computes rolling local values
In mnirs: Muscle Near-Infrared Spectroscopy Processing and Analysis

compute_local_windows

R Documentation

Computes rolling local values

Description

compute_local_windows(): Compute a list of rolling window indices along a time variable t.

compute_local_fun(): Compute a rolling function along x from a list of rolling sample windows.

compute_outliers(): Computes a vector of local medians and logicals indicating outliers of x within a list of rolling sample windows window_idx.

compute_valid_neighbours(): Compute a list of rolling window indices along x to either side of NAs.

Usage

compute_local_windows(
  t,
  idx = seq_along(t),
  width = NULL,
  span = NULL,
  align = c("centre", "left", "right")
)

compute_local_fun(x, window_idx, fn, ...)

compute_outliers(x, window_idx, outlier_cutoff)

compute_valid_neighbours(
  x,
  t = seq_along(x),
  width = NULL,
  span = NULL,
  verbose = TRUE
)

Arguments

`t`	An optional numeric vector of the predictor variable (e.g. time). Default is `seq_along(x)`.
`idx`	A numeric vector of indices of `t` at which to calculate local windows. All indices of `t` by default, or can be used to only calculate for known indices, such as invalid values of `x`.
`width`	An integer defining the local window in number of samples around `idx` in which to perform the operation, according to `align`.
`span`	A numeric value defining the local window time span around `idx` in which to perform the operation, according to `align`. In units of `time_channel` or `t`.
`align`	Window alignment as "centre"/"center" (the default), "left", or "right". Where "left" is forward looking, and "right" is backward looking from the current sample.
`x`	A numeric vector of the response variable.
`window_idx`	A list the same or shorter length as `x` with numeric vectors for the sample indices of local rolling windows.
`fn`	A function to pass through for local rolling calculation.
`...`	Additional arguments.
`outlier_cutoff`	A numeric value for the local outlier threshold, as the number of standard deviations from the local median. Default `NULL` will not replace outliers. Lower values are more sensitive and flag more outliers; higher values are more conservative. `outlier_cutoff = 3` Pearson's 3 sigma edit rule. `outlier_cutoff = 2` approximates a Tukey-style 1.5*IQR rule. `outlier_cutoff = 0` Tukey's median filter.
`verbose`	Logical. Default is `TRUE`. Display or silence (if `FALSE`) warnings and information messages helpful for troubleshooting. Ad global default can be set via `options(mnirs.verbose = FALSE)`.

Details

The local rolling window can be specified by either width as the number of samples, or span as the time span in units of t. Specifying width is often faster than span.

align defaults to "centre" the local window around idx between ⁠[idx - floor((width-1)/2),⁠ ⁠idx + floor(width/2)]⁠ when width is specified. Even width values will bias align to "left", with the unequal sample forward of idx, effectively returning NA at the last sample index. When span is specified, the local window is between ⁠[t - span/2, t + span/2]⁠.

Value

compute_local_windows(): A list the same length as idx and the same or shorter length as t with numeric vectors of sample indices of length width samples or span units of time t.

compute_local_fun(): A numeric vector the same length as x.

compute_outliers(): A list() with vectors the same length as x for with numeric local medians and logical identifying where is_outlier.

compute_valid_neighbours(): A list the same length as the NA values in x with numeric vectors of sample indices of length width samples or span units of time t for valid values neighbouring split to either side of the invalid NAs.