Provides a summary of the pattern causality effect analysis results. This function displays the summary statistics for the effects, including the number of components and the strongest effects.
Usage
# S3 method for class 'pc_effect'
summary(object, ...)Examples
# \donttest{
data(climate_indices)
dataset <- climate_indices[, -1]
pc_matrix_obj <- pcMatrix(dataset, E = 3, tau = 1,
metric = "euclidean", h = 1, weighted = TRUE,
verbose = FALSE)
effects <- pcEffect(pc_matrix_obj)
summary(effects)
#> $effects_summary
#> $effects_summary$positive
#> received exerted Diff
#> mean 124.27885 124.27885 -3.552714e-15
#> sd 14.37416 14.29813 2.839047e+01
#> min 111.68984 110.79557 -2.893929e+01
#> max 140.90400 140.62913 3.010843e+01
#>
#> $effects_summary$negative
#> received exerted Diff
#> mean 34.694733 34.694733 8.881784e-16
#> sd 8.461353 2.545728 1.073526e+01
#> min 27.064962 31.936029 -1.063687e+01
#> max 44.049820 37.701829 1.065365e+01
#>
#> $effects_summary$dark
#> received exerted Diff
#> mean 141.026422 141.02642 0.00000
#> sd 6.677246 12.19215 17.70408
#> min 132.031033 125.97470 -19.47157
#> max 147.497723 151.50260 18.28564
#>
#>
#> $n_components
#> [1] 4
#>
#> $strongest_effects
#> $strongest_effects[[1]]
#> $strongest_effects[[1]]$component
#> [1] "PNA"
#>
#> $strongest_effects[[1]]$effect
#> [1] 30.10843
#>
#>
#> $strongest_effects[[2]]
#> $strongest_effects[[2]]$component
#> [1] "AAO"
#>
#> $strongest_effects[[2]]$effect
#> [1] 10.65365
#>
#>
#> $strongest_effects[[3]]
#> $strongest_effects[[3]]$component
#> [1] "PNA"
#>
#> $strongest_effects[[3]]$effect
#> [1] -19.47157
#>
#>
#>
#> attr(,"class")
#> [1] "summary.pc_effect"
# }