loop_break: Find smallest subset to exclude from sample for...

In hannesrosenbusch/StatBreak: Examine robustness of sample statistics to case deletions

Description

The function iteratively learns which observations should at least be excluded from the data to reach a conservative 'goal value' for the statistic of interest. It does so by relying on a genetic algorithm, which efficiently explores the (usually vast) space of possible subsets. The result can uncover impactful subsamples and fuel discussions of robustness. Necessary arguments include the dataframe, a function to compute the statistic of interest ('statistic_computation' see examples), and the goal value of interest.

Usage

loop_break(
  data = NA,
  goal_value = NA,
  statistic_computation = NA,
  max_exclusions = 3,
  random_seed = 42
)

Arguments

data	A data.frame containing the observations as rows.
goal_value	This conservative value (e.g., small effect size) is targeted.
statistic_computation	A formula which has 'data' as input and returns the statistic of interest.
max_exclusions	maximum number of cases to be excluded
random_seed	Seed for replicability.

Value

Vector of row indeces to be excluded

Examples

coefficient_computation <- function(data){
statistic <- cor(data$Sepal.Length, data$Petal.Width)
return(statistic)}

filter <- loop_break(data = iris, statistic_computation = coefficient_computation, goal_value = 0.2, max_exclusions = 2)
print(filter)

hannesrosenbusch/StatBreak documentation built on Feb. 12, 2020, 10:35 a.m.