sample_dispersion | R Documentation |
Alternative versions of variance, dispersion a distance (Mardia and Jupp, 1999; pp. 19-20). These alternative dispersion has a minimum at the sample median.
sample_circular_variance(x, w = NULL, axial = TRUE, na.rm = TRUE)
sample_circular_distance(x, y, axial = TRUE, na.rm = TRUE)
sample_circular_dispersion(
x,
y = NULL,
w = NULL,
w.y = NULL,
axial = TRUE,
na.rm = TRUE
)
x , y |
vectors of numeric values in degrees. |
w , w.y |
(optional) Weights. A vector of positive numbers and of the same
length as |
axial |
logical. Whether the data are axial, i.e. pi-periodical
( |
na.rm |
logical. Whether |
N.I. Fisher (1993) Statistical Analysis of Circular Data, Cambridge University Press.
Mardia, K.V., and Jupp, P.E (1999). Directional Statistics, Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ, USA. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/9780470316979")}
a <- c(0, 2, 359, 6, 354)
sample_circular_distance(a, 10) # distance to single value
b <- a + 90
sample_circular_distance(a, b) # distance to multiple values
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
sample_circular_variance(sa.por$azi.PoR)
sample_circular_dispersion(sa.por$azi.PoR, y = 135)
sample_circular_dispersion(sa.por$azi.PoR, y = 135, w = 1 / san_andreas$unc)
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