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Distance Binned Summary Statistics

Source: R/roll_statistics.R
distance_binned_stats.Rd

[Experimental] Circular summary statistics over intervals of distances.

Usage

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,
  ...
)

Arguments

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()

Value

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.

See also

circular_summary(), circular_dispersion(), and circular_dispersion_boot()

Examples

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()
#> # A tibble: 6 × 19
#>   bins      n distance_min distance_median distance_max  mean    sd    var    lq
#>   <fct> <int>        <dbl>           <dbl>        <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1 [-9.…     2        -9.23           -9.09        -8.95   NA   NA   NA      NA  
#> 2 (-8.…     8        -8.26           -7.91        -7.56  102.  44.7  0.703  71.4
#> 3 (-7.…     9        -7.47           -6.84        -6.60  134.  36.2  0.550  92.2
#> 4 (-6.…    11        -6.44           -6.18        -5.87  150.  29.4  0.410 126. 
#> 5 (-5.…    10        -5.32           -5.06        -4.64  149.  37.9  0.583  65.5
#> 6 (-4.…    23        -4.41           -3.91        -3.57  159.  33.7  0.500  80.8
#> # ℹ 10 more variables: quasimedian <dbl>, uq <dbl>, median <dbl>, mode <dbl>,
#> #   CI <dbl>, skewness <dbl>, kurtosis <dbl>, nchisq <dbl>, dispersion <dbl>,
#> #   dispersion_sde <dbl>