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 83.202119 83.20212 -3.552714e-15
#> sd 8.764969 19.95980 1.993609e+01
#> min 71.506182 64.57246 -2.649411e+01
#> max 92.589225 109.64732 2.098342e+01
#>
#> $effects_summary$negative
#> received exerted Diff
#> mean 77.082125 77.08212 0.00000
#> sd 4.887857 19.58312 22.25668
#> min 71.124192 50.33220 -18.46447
#> max 82.707750 97.20834 32.37555
#>
#> $effects_summary$dark
#> received exerted Diff
#> mean 139.715757 139.715757 0.00000
#> sd 6.942656 7.968076 11.83639
#> min 134.139041 130.631078 -13.70569
#> max 149.749949 149.992272 11.53075
#>
#>
#> $n_components
#> [1] 4
#>
#> $strongest_effects
#> $strongest_effects[[1]]
#> $strongest_effects[[1]]$component
#> [1] "AAO"
#>
#> $strongest_effects[[1]]$effect
#> [1] -26.49411
#>
#>
#> $strongest_effects[[2]]
#> $strongest_effects[[2]]$component
#> [1] "AAO"
#>
#> $strongest_effects[[2]]$effect
#> [1] 32.37555
#>
#>
#> $strongest_effects[[3]]
#> $strongest_effects[[3]]$component
#> [1] "AO"
#>
#> $strongest_effects[[3]]$effect
#> [1] -13.70569
#>
#>
#>
#> attr(,"class")
#> [1] "summary.pc_effect"
# }