QCAcluster (version 0.1.0)

wop_inter: Calculation of weight of partitions in pooled solution parameters for intermediate solution

Description

wop_inter calculates the weight of partitions in the pooled solution parameters (consistency, coverage) for the intermediate solution.

Usage

wop_inter(
  dataset,
  units,
  time,
  cond,
  out,
  n_cut,
  incl_cut,
  intermediate,
  amb_selector
)

Arguments

dataset

Calibrated pooled dataset for partitioning and minimization

units

Units defining the within-dimension of data (time series)

time

Periods defining the between-dimension of data (cross sections)

cond

Conditions used for the pooled analysis

out

Outcome used for the pooled analysis

n_cut

Frequency cut-off for designating truth table rows as observed

incl_cut

Inclusion cut-off for designating truth table rows as consistent

intermediate

A vector of directional expectations to derive the intermediate solutions

amb_selector

Numerical value for selecting a single model in the presence of model ambiguity. Models are numbered according to their order produced by minimize by the QCA package.

Value

A dataframe with information about the weight of the partitions for pooled consistency and coverage scores and the following columns:

• type: The type of the partition. between stands for cross-sections; within stands for time series. pooled stands information about the pooled data.

• partition: Type of partition. For between-dimension, the unit identifiers are listed (argument units). For the within-dimension, the time identifiers are listed (argument time). The entry is - for the pooled data.

• denom_cons: Denominator of the consistency formula. It is the sum over the cases' membership in the solution.

• num_cons: Numerator of the consistency formula. It is the sum over the minimum of the cases' membership in the solution and the outcome.

• denom_cov: Denominator of the coverage formula. It is the sum over the cases' membership in the outcome.

• num_cov: Numerator of the coverage formula. It is the sum over the minimum of the cases' membership in the solution and the outcome. (identical to num_cons)

Examples

# NOT RUN {
data(Schwarz2016)

# }