dispersion | R Documentation |
Circular distance between two angles and circular dispersion of angles about a specified angle.
circular_distance(x, y, axial = TRUE, na.rm = TRUE)
circular_dispersion(
x,
y = NULL,
w = NULL,
w.y = NULL,
axial = TRUE,
na.rm = TRUE
)
circular_sd2(x, y, w = NULL, axial = TRUE, na.rm = TRUE)
x , y |
vectors of numeric values in degrees. |
axial |
logical. Whether the data are axial, i.e. pi-periodical
( |
na.rm |
logical. Whether |
w , w.y |
(optional) Weights. A vector of positive numbers and of the same
length as |
Circular dispersion is a measure for the spread of data like the variance.
Dispersion measures the spread about a given angles, whereas
the variance measures the spread about the mean (Mardia and Jupp, 1999). When
y = NULL
the dispersion is identical to the variance.
Circular standard deviation in circular_sd2()
is the transformed dispersion
instead of the variance as for circular_sd()
.
circular_distance
returns a numeric vector of positive numbers,
circular_dispersion
and circular_sd2()
return a positive number.
If y
is NULL
, than the circular variance is returned.
Mardia, K.V. (1972). Statistics of Directional Data: Probability and Mathematical Statistics. London: Academic 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")}
circular_mean()
, circular_var()
.
a <- c(0, 2, 359, 6, 354)
circular_distance(a, 10) # distance to single value
b <- a + 90
circular_distance(a, b) # distance to multiple values
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
circular_dispersion(sa.por$azi.PoR, y = 135)
circular_dispersion(sa.por$azi.PoR, y = 135, w = 1 / san_andreas$unc)
circular_sd2(sa.por$azi.PoR, y = 135, w = 1 / san_andreas$unc)
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