This function computes the gradient of f using finite differences.
gradient(f, binsize, multidimensional = FALSE)A numeric array of the same shape as the input array f storing the
gradient of f obtained via finite differences.
Either a numeric vector of a numeric matrix or a numeric array specifying the curve(s) that need to be differentiated.
If a vector, it must be of shape \(M\) and it is interpreted as a single \(1\)-dimensional curve observed on a grid of size \(M\).
If a matrix and multidimensional == FALSE, it must be of shape
\(M \times N\). In this case, it is interpreted as a sample of \(N\)
curves observed on a grid of size \(M\), unless \(M = 1\) in which case
it is interpreted as a single \(1\)-dimensional curve observed on a grid
of size \(M\).
If a matrix and multidimensional == TRUE,it must be of shape
\(L \times M\) and it is interpreted as a single \(L\)-dimensional
curve observed on a grid of size \(M\).
If a 3D array, it must be of shape \(L \times M \times N\) and it is interpreted as a sample of \(N\) \(L\)-dimensional curves observed on a grid of size \(M\).
A numeric value specifying the size of the bins for computing finite differences.
A boolean specifying if the curves are
multi-dimensional. This is useful when f is provided as a matrix to
determine whether it is a single multi-dimensional curve or a collection of
uni-dimensional curves. Defaults to FALSE.
out <- gradient(simu_data$f[, 1], mean(diff(simu_data$time)))
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