This function finds the optimal number of bins in both x and y direction which should be used to calculate the binned distance. The binned distance is calculated for each combination of provided choices of number of bins in x and y direction and finds the difference using calc_diff for each combination. The combination for which the difference is maximum should be used.

opt_bin_diff(lineup.dat, var, xlow, xhigh, ylow, yhigh, pos,
  plot = FALSE, m = 20)

Arguments

lineup.dat

lineup data to get the lineup

var

a list of names of the variables to be used to calculate the difference

xlow

the lowest value of number of bins on the x-direction

xhigh

the highest value of number of bins on the x-direction

ylow

the lowest value of number of bins on the y-direction

yhigh

the highest value of number of bins on the y-direction

pos

position of the true plot in the lineup

plot

LOGICAL; if true, returns a tile plot for the combinations of number of bins with the differences as weights

m

number of plots in the lineup, by default m = 20

Value

a dataframe with the number of bins and differences the maximum mean distance of the null plots

Examples

if(require('dplyr')){ opt_bin_diff(lineup(null_permute('mpg'), mtcars, pos = 1), var = c('mpg', 'wt'), 2, 5, 4, 8, pos = 1, plot = TRUE, m = 8) }
#> $dat #> # A tibble: 20 x 3 #> # Groups: xbins [4] #> xbins ybins Diff #> <int> <int> <dbl> #> 1 2 4 -0.236 #> 2 2 5 -0.236 #> 3 2 6 -0.432 #> 4 2 7 -0.236 #> 5 2 8 -0.236 #> 6 3 4 -0.416 #> 7 3 5 -0.416 #> 8 3 6 -0.365 #> 9 3 7 -0.416 #> 10 3 8 -0.416 #> 11 4 4 -0.556 #> 12 4 5 -0.556 #> 13 4 6 -0.544 #> 14 4 7 -0.556 #> 15 4 8 -0.556 #> 16 5 4 0.149 #> 17 5 5 0.149 #> 18 5 6 -0.00404 #> 19 5 7 0.149 #> 20 5 8 0.149 #> #> $p
#>