Apply Gaussian Kernel Smoother

Convolves a numeric vector with a discrete Gaussian kernel.

Usage

filter_gaussian(x, sigma = 1, window_width = NULL)

Arguments

x

Numeric vector to filter.

sigma

Standard deviation of the Gaussian kernel, in samples (frames). Must be positive.

window_width

Integer kernel width in samples. Must be positive and is forced to be odd. Defaults to 2 * ceiling(3 * sigma) + 1, which truncates the kernel at ±3σ.

Value

Filtered numeric vector, same length as x.

Details

The kernel is symmetric and centered: weights[k] = dnorm(k, sd = sigma) for k in -half:half (where half = (window_width - 1) / 2), renormalised to sum to 1. The output is the kernel-weighted moving average of x.

At each position, weights for kernel taps that would fall outside x or that align with NA values are excluded, and the remaining weights are renormalised. This means edges and isolated NAs are handled gracefully without contaminating the result. A position whose entire window is NA returns NA.

Larger sigma gives heavier smoothing. For movement data, typical values range from 0.5 (very light smoothing) to 5 (heavy smoothing).

Examples

x <- c(1, 2, 3, 100, 5, 6, 7)
filter_gaussian(x, sigma = 1)
#> [1]  2.127792  7.635157 26.352299 42.308827 28.325582 11.377478  7.088955