Distance Binned Summary Statistics
Circular summary statistics over intervals of distances.
distance_binned_stats( azi, distance, n.breaks = 10, width.breaks = NULL, unc = NULL, prd = NULL, prd.error = NULL, kappa = 2, R = 1000, conf.level = 0.95, ... )
azi: numeric. Azimuth values in degrees.
distance: numeric. the independent variable along the values in azi
are sorted, e.g. the plate boundary distances
n.breaks: numeric. number (greater than or equal to 2) giving the number of equal-sized intervals into which distance is to be cut. Default is 10. Will be ignored if width.breaks is specified.
width.breaks: numeric. The width of the intervals into which distance
is to be cut.
unc: (optional) Uncertainties of azi (in degrees) acting as inverse weighting factors for statistics.
prd: (optional) numeric. A predicted orientation in degrees.
prd.error: (optional) numeric. The uncertainty of the predicted orientation in degrees.
kappa: numeric. Concentration parameter applied for the circular mode.
R: integer. Number of bootstrap iterates for estimating the error of the dispersion.
conf.level: The level of confidence for confidence interval and bootstrapped standard error of dispersion.
...: optional arguments passed to ggplot2::cut_interval() and [ggplot2::cut_width()
tibble containing the n values for aziin each bin, min/median/max distance of the bin, and the summary statistics for azi. If prd is specified, the normal Chi-squared statistic, dispersion and its standard error are returned as well.
data("plates") plate_boundary <- subset(plates, plates$pair == "na-pa") data("san_andreas") PoR <- subset(nuvel1, nuvel1$plate.rot == "na") san_andreas$distance <- distance_from_pb( x = san_andreas, PoR = PoR, pb = plate_boundary, tangential = TRUE ) dat <- san_andreas |> cbind(PoR_shmax(san_andreas, PoR, "right")) distance_binned_stats(dat$azi.PoR, distance = dat$distance, width.breaks = 1, unc = dat$unc, prd = 135 ) |> head()
circular_summary(), circular_dispersion(), and circular_dispersion_boot()