I think this is really a statistical issue, not a general r
programming issue, which is what r-help is about.
I think a much better target for this post would be the r-sig-geo
list, where you likely would find the expertise you need.
r-sig-ecology might also work, so you should probably check this out
also.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Dec 28, 2016 at 12:11 PM, Sidoti, Salvatore A.
<sidoti...@buckeyemail.osu.edu> wrote:
> I am attempting to smooth the jagged paths of animal tracks to determine
> their distances with greater accuracy. The data is in the form of (x,y) 2D
> coordinates. My end goal is to produce a set of interpolating points whereby
> their Cartesian distances are equal to each other. So far, I have been able
> to produce a path with a specified number of interpolating points via
> spline(). However, these points are not equidistant.
>
> An example data set and my code thus far:
>
> df <- structure(list(x = c(329L, 329L, 329L, 329L, 330L, 330L, 330L,
> 330L, 330L, 330L, 330L, 330L, 330L, 330L, 330L, 330L, 330L, 330L,
> 330L, 330L, 330L, 330L, 331L, 331L, 331L, 332L, 332L, 333L, 333L,
> 333L, 333L, 333L, 333L, 333L, 333L, 333L, 333L, 333L, 334L, 334L,
> 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L,
> 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L,
> 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L, 334L,
> 334L, 334L, 334L, 334L, 334L, 334L, 334L, 333L, 333L, 332L, 332L,
> 332L, 332L, 332L, 332L, 333L, 333L, 333L, 332L, 333L, 331L, 331L,
> 330L, 330L, 330L, 330L, 330L, 330L, 330L, 330L, 329L, 329L, 329L,
> 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L,
> 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 329L, 328L,
> 327L, 327L, 327L, 327L, 327L, 326L, 326L, 325L, 325L, 325L, 325L,
> 325L, 323L, 322L, 321L, 320L, 319L, 319L, 319L, 319L, 319L, 319L
> ), y = c(255L, 256L, 256L, 256L, 257L, 257L, 257L, 257L, 257L,
> 257L, 257L, 257L, 257L, 257L, 258L, 259L, 259L, 259L, 261L, 261L,
> 262L, 263L, 263L, 264L, 265L, 266L, 266L, 267L, 268L, 269L, 270L,
> 272L, 272L, 273L, 274L, 275L, 275L, 275L, 275L, 275L, 275L, 275L,
> 275L, 275L, 275L, 275L, 275L, 275L, 275L, 275L, 275L, 275L, 275L,
> 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L,
> 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L, 276L,
> 276L, 276L, 276L, 276L, 277L, 278L, 278L, 279L, 280L, 281L, 283L,
> 284L, 285L, 287L, 288L, 290L, 291L, 291L, 294L, 295L, 297L, 298L,
> 299L, 300L, 301L, 302L, 302L, 304L, 305L, 306L, 306L, 308L, 308L,
> 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L,
> 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 308L, 309L,
> 310L, 311L, 311L, 312L, 313L, 314L, 315L, 318L, 319L, 320L, 322L,
> 323L, 324L, 325L, 325L, 325L, 325L, 326L, 326L, 327L)), .Names = c("x",
> "y"), row.names = c(NA, -150L), class = "data.frame")
>
> require(Momocs)
>
> cumdist <- coo_perimcum(df)
> sx <- spline(cumdist, df[, 1], method = "natural", n = 10)
> sy <- spline(cumdist, df[, 2], method = "natural", n = 10)
> splines <- cbind.data.frame(x = sx$y, y = sy$y)
>
> par(pty = "s")
> with(df, plot(x, y, main = "Example Locomotor Path - Cubic Spline Smoothing",
> axes = FALSE, frame.plot = TRUE, type = "l", col = "light
> gray", lwd = 3))
> with(splines, lines(x, y, type = "b", col = "red", lwd = 3))
>
> Thank you!
>
> Salvatore A. Sidoti
> PhD Student
> Behavioral Ecology
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
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