Compute scagnostics on all possible scatter plots for the given data

Source: R/calculate.R

Compute scagnostics on all possible scatter plots for the given data

calc_scags_wide(
  all_data,
  scags = c("outlying", "stringy", "striated", "striated2", "clumpy", "clumpy2",
    "sparse", "skewed", "convex", "skinny", "monotonic", "splines", "dcor"),
  out.rm = TRUE,
  euclid = FALSE
)

Arguments

all_data

tibble of multivariate data on which to compute scagnostics

scags

collection of strings matching names of scagnostics to calculate: outlying, stringy, striated, striated2, striped, clumpy, clumpy2, sparse, skewed, convex, skinny, monotonic, splines, dcor

out.rm

logical indicator to indicate if outliers should be removed before calculating non outlying measures

euclid

logical indicator to use Euclidean distance

Value

A data frame that gives the data's scagnostic scores for each possible variable combination.

See also

calc_scags

Examples

# Calculate selected scagnostics
data(pk)
calc_scags_wide(pk[,2:5], scags=c("outlying","monotonic"))
#> # A tibble: 6 × 4
#>   Var1           Var2         outlying monotonic
#>   <fct>          <fct>           <dbl>     <dbl>
#> 1 MDVP:Fhi(Hz)   MDVP:Fo(Hz)     0.412    0.796 
#> 2 MDVP:Flo(Hz)   MDVP:Fo(Hz)     0.172    0.324 
#> 3 MDVP:Flo(Hz)   MDVP:Fhi(Hz)    0.336    0.0956
#> 4 MDVP:Jitter(%) MDVP:Fo(Hz)     0.289    0.270 
#> 5 MDVP:Jitter(%) MDVP:Fhi(Hz)    0.541    0.0978
#> 6 MDVP:Jitter(%) MDVP:Flo(Hz)    0.430    0.407