circle_stats | R Documentation |
Calculate the (weighted median) and standard deviation of orientation data.
circular_mean(x, w = NULL, axial = TRUE, na.rm = TRUE)
circular_var(x, w = NULL, axial = TRUE, na.rm = TRUE)
circular_sd(x, w = NULL, axial = TRUE, na.rm = TRUE)
circular_median(x, w = NULL, axial = TRUE, na.rm = TRUE)
circular_quantiles(x, w = NULL, axial = TRUE, na.rm = TRUE)
circular_IQR(x, w = NULL, axial = TRUE, na.rm = TRUE)
sample_circular_dispersion(x, w = NULL, axial = TRUE, na.rm = TRUE)
x |
numeric vector. Values in degrees. |
w |
(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 value indicating whether |
numeric vector
Weighting may be the reciprocal of the data uncertainties.
Weightings have no effect on quasi-median and quasi-quantiles if
length(x) %% 2 != 1
and length(x) %% 4 == 0
, respectively.
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")}
Ziegler, M. O.; Heidbach O. (2019). Manual of the Matlab Script Stress2Grid v1.1. WSM Technical Report 19-02, GFZ German Research Centre for Geosciences. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2312/wsm.2019.002")}
Heidbach, O., Tingay, M., Barth, A., Reinecker, J., Kurfess, D., & Mueller, B. (2010). Global crustal stress pattern based on the World Stress Map database release 2008. Tectonophysics 482, 3<U+2013>15, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.tecto.2009.07.023")}
x <- rvm(10, 0, 100) %% 180
unc <- stats::runif(100, 0, 10)
circular_mean(x, 1 / unc)
circular_var(x, 1 / unc)
circular_sd(x, 1 / unc)
circular_median(x, 1 / unc)
circular_quantiles(x, 1 / unc)
circular_IQR(x, 1 / unc)
data("san_andreas")
circular_mean(san_andreas$azi)
circular_mean(san_andreas$azi, 1 / san_andreas$unc)
circular_median(san_andreas$azi)
circular_median(san_andreas$azi, 1 / san_andreas$unc)
circular_quantiles(san_andreas$azi)
circular_quantiles(san_andreas$azi, 1 / san_andreas$unc)
circular_var(san_andreas$azi)
circular_var(san_andreas$azi, 1 / san_andreas$unc)
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
circular_mean(sa.por$azi.PoR, 1 / san_andreas$unc)
circular_median(sa.por$azi.PoR, 1 / san_andreas$unc)
circular_var(sa.por$azi.PoR, 1 / san_andreas$unc)
circular_quantiles(sa.por$azi.PoR, 1 / san_andreas$unc)
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