Compute selected scagnostics on subsets
calc_scags(
x,
y,
scags = c("outlying", "stringy", "striated", "clumpy", "sparse", "skewed", "convex",
"skinny", "monotonic", "splines", "dcor")
)sc_pairwise
# Calculate selected scagnostics on a single pair
calc_scags(anscombe$x1, anscombe$y1,
scags=c("monotonic", "outlying","convex"))
#> Registered S3 method overwritten by 'cli':
#> method from
#> print.boxx spatstat.geom
#> # A tibble: 1 × 3
#> outlying convex monotonic
#> <dbl> <dbl> <dbl>
#> 1 0 0.736 0.818
# Calculate all large number of scagnostics together
calc_scags(anscombe$x1, anscombe$y1)
#> # A tibble: 1 × 9
#> outlying stringy striated clumpy sparse skewed monotonic splines dcor
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0.714 0 0.407 0.271 0.270 0.818 0.667 0.667
# Compute on long form data, or subsets
# defined by a categorical variable
require(dplyr)
#> Loading required package: dplyr
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
datasaurus_dozen %>%
group_by(dataset) %>%
summarise(calc_scags(x,y,
scags=c("monotonic", "outlying", "convex")))
#> # A tibble: 13 × 4
#> dataset outlying convex monotonic
#> <chr> <dbl> <dbl> <dbl>
#> 1 away 0.0535 0.795 0.0573
#> 2 bullseye 0.113 0.884 0.0787
#> 3 circle 0.273 0.000574 0.0773
#> 4 dino 0 0.895 0.0651
#> 5 dots 0.330 0.000465 0.126
#> 6 h_lines 0.177 0.923 0.0520
#> 7 high_lines 0.0726 0.359 0.00287
#> 8 slant_down 0.0444 0.921 0.0669
#> 9 slant_up 0.0427 0.914 0.0861
#> 10 star 0.404 0.535 0.0514
#> 11 v_lines 0.190 0.935 0.0566
#> 12 wide_lines 0.0180 0.311 0.0522
#> 13 x_shape 0.186 0.159 0.0205