Streaming Depth for Functional Data

Compute depth values using a streaming (pre-sorted reference) algorithm. This approach pre-sorts the reference data at each time point, enabling O(T log N) depth computation per curve instead of O(N^2 T).

Usage

streaming.depth(fdataobj, fdataori = NULL, method = c("FM", "MBD", "BD"), ...)

Arguments

fdataobj

An object of class 'fdata' to compute depth for.

fdataori

An object of class 'fdata' as reference sample. If NULL, uses fdataobj as reference.

method

Streaming depth method: "FM" (Fraiman-Muniz, default), "MBD" (modified band depth), or "BD" (band depth).

...

Additional arguments (ignored).

Value

Numeric vector of depth values, one per curve in fdataobj.

References

Fraiman, R. and Muniz, G. (2001). Trimmed means for functional data. Test, 10(2):419–440.

Lopez-Pintado, S. and Romo, J. (2009). On the concept of depth for functional data. Journal of the American Statistical Association, 104(486):718–734.

Examples

fd <- fdata(matrix(rnorm(200), 20, 10))
streaming.depth(fd, method = "FM")
#>  [1] 0.57 0.27 0.28 0.58 0.57 0.34 0.61 0.44 0.63 0.40 0.59 0.69 0.52 0.47 0.41
#> [16] 0.47 0.60 0.61 0.60 0.35