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Apply a moving average filter

Source: R/filter_mnirs.R
filter_moving_average.Rd

Apply a simple moving average smoothing filter to vector data

Usage

filter_moving_average(
  x,
  t = seq_along(x),
  width = NULL,
  span = NULL,
  partial = FALSE,
  na.rm = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x

A numeric vector of the response variable.

t

An optional numeric vector of the predictor variable; time or sample number. Defaults to indices of t = seq_along(x).

width

An integer defining the local window in number of samples around idx in which to perform the operation., between [idx - floor(width/2), idx + floor(width/2)].

span

A numeric value defining the local window timespan around idx in which to perform the operation. In units of time_channel or t, between [t - span/2, t + span/2].

partial

A logical specifying whether to perform the operation over a subset of available data within the local rolling window (TRUE), or requiring a complete window of valid samples (FALSE, by default). See Details.

na.rm

A logical indicating whether missing values should be preserved and passed through the filter (TRUE). Otherwise FALSE (the default) will throw an error if there are any NAs (see Details).

verbose

A logical to display (the default) or silence (FALSE) warnings and information messages used for troubleshooting.

...

Additional arguments.

Value

A numeric vector the same length as x.

Details

Applies a centred (symmetrical) moving average filter in a local window defined by either width as the number of samples around idx between [idx - floor(width/2), idx + floor(width/2)]. Or by span as the timespan in units of time_channel between [t - span/2, t + span/2].

Specifying width is often faster than span.

If there are no valid values within the calculation window, will return NA. A partial moving average will be calculated at the edges of the data.

See also

zoo::rollmean()

Examples

## basic moving average with sample width
x <- c(1, 3, 2, 5, 4, 6, 5, 7)
filter_moving_average(x, width = 3)
#> [1]       NA 2.000000 3.333333 3.666667 5.000000 5.000000 6.000000       NA

## with explicit time vector
t <- c(0, 1, 2, 3, 4, 5, 6, 7)
filter_moving_average(x, t, width = 2)
#> [1] 2.0 2.5 3.5 4.5 5.0 5.5 6.0  NA

## using timespan instead of sample width
filter_moving_average(x, span = 2)
#> [1] 2.000000 2.000000 3.333333 3.666667 5.000000 5.000000 6.000000 6.000000