multilevel_break: Find smallest subset to exclude from clustered sample for...

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

Description

The function iteratively learns which groups (see argument grouping_var) 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), the column with the grouping variable ('grouping_var'), and the goal value of interest.

Usage

multilevel_break(
  data = NULL,
  goal_value = NULL,
  statistic_computation = NULL,
  grouping_var = NULL,
  max_exclusions = NULL,
  pop = 200,
  max_generations = 300,
  exclusion_cost = 0.01,
  prop_included_cases = 0.9,
  chance_of_mutation = 0.05,
  stop_search = 100,
  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 groups to be excluded
pop	Number of 'individuals' in each generation of the genetic algorithm.
max_generations	Maximum number of generations that the algorithm generates.
exclusion_cost	Used to calibrate fitness function.
prop_included_cases	Initial proportion of included groups (e.g. .90).
chance_of_mutation	Chance that a gene mutates, higher is slower but more accurate (e.g. .02).
stop_search	After how many generations without change is the 'converged' result returned.
random_seed	Seed for replicability.

Value

Vector of zeros and ones with length equal to number of observations in data. Ones indicate exclusion.

Examples

set.seed(42)
groups = c(0,0,0,0,0,1,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8)
v1 = rnorm(length(groups))+0.7
v2 = rnorm(length(groups))

df = as.data.frame(cbind(groups, v1,v2))

st = function(data){
t.test(data$v1, data$v2)$p.value
}

multilevel_break(df, statistic_computation = st, goal_value = 0.05, grouping_var = 'groups', max_exclusions = 8)

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