Detect outliers — detect_outliers • evprof

Detect outliers

Usage

detect_outliers(
  sessions,
  MinPts = NULL,
  eps = NULL,
  noise_th = 2,
  log = FALSE,
  start = getOption("evprof.start.hour")
)

Arguments

sessions: tibble, sessions data set in evprof standard format.
MinPts: MinPts parameter for DBSCAN clustering
eps: eps parameter for DBSCAN clustering
noise_th: noise threshold
log: logical, whether to transform ConnectionStartDateTime and ConnectionHours variables to natural logarithmic scale (base = exp(1)).
start: integer, start hour in the x axis of the plot.

Value

sessions tibble with extra boolean column Outlier

Examples

library(dplyr)
sessions_outliers <- california_ev_sessions %>%
  sample_frac(0.05) %>%
  detect_outliers(start = 3, noise_th = 5, eps = 2.5)
#> Trying with MinPts = 200 and eps = 2.5
#> Too much nosie (7.3 %). Consider a higher eps.
#> Trying with MinPts = 200 and eps = 3.75
#> Solution found: MinPts= 200 , eps = 2.594