wop {QCAcluster}

Weight of partitions for pooled solution parameters for conservative or parsimonious solution

Description

wop calculates the contribution or weight of partitions for the pooled solution parameters of consistency and coverage for the conservative or parsimonious solution.

Usage

wop(dataset, units, time, cond, out, n_cut, incl_cut, solution, amb_selector)

Arguments

dataset

Calibrated pooled dataset for partitioning and minimization of pooled solution.

units

Units that define the within-dimension of data (time series).

time

Periods that define 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 in the pooled analysis.

incl_cut

Inclusion cut-off for designating truth table rows as consistent in the pooled analysis.

solution

A character specifying the type of solution that should be derived. C produces the conservative (or complex) solution, P the parsimonious solution. See wop_inter for deriving intermediate solution.

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 with 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

data(Thiem2011)
wop_pars <- wop(
  dataset = Thiem2011,
  units = "country", time = "year",
  cond = c("fedismfs", "homogtyfs", "powdifffs", "comptvnsfs", "pubsupfs", "ecodpcefs"),
  out = "memberfs",
  n_cut = 6, incl_cut = 0.8,
  solution = "P",
  amb_selector = 1)
wop_pars