Circular mean, standard deviation, variance, quasi-quantiles, 95% confidence angle, standardized skewness and kurtosis
Arguments
- x
numeric vector. Values in degrees.
- w
(optional) Weights. A vector of positive numbers and of the same length as
x
.- axial
logical. Whether the data are axial, i.e. pi-periodical (
TRUE
, the default) or directional, i.e. \(2 \pi\)-periodical (FALSE
).- na.rm
logical value indicating whether
NA
values inx
should be stripped before the computation proceeds.
Examples
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
circular_summary(sa.por$azi.PoR)
#> n mean sd var 25% quasi-median
#> 1126.0000000 140.8777709 23.4183183 0.2840287 124.8495460 136.8274701
#> 75% median 95%CI skewness kurtosis R
#> 150.2163335 138.9450918 2.6012976 -0.1758456 1.4538046 0.7159713
circular_summary(sa.por$azi.PoR, w = 1 / san_andreas$unc)
#> n mean sd var 25% quasi-median
#> 1126.0000000 138.9024850 18.5799867 0.1896731 124.8436061 136.8580836
#> 75% median 95%CI skewness kurtosis R
#> 150.2225367 138.9450918 2.0854956 -0.3377921 2.7863614 0.7159713