Skip to contents

Generation of conservative or parsimonious solution for individual partitions

Source: R/partition_min.R
partition_min.Rd

partition_min decomposes clustered data into individual partitions. For panel data, for example, these can be cross sections, time series or both. The function derives an individual solution for each partition and the pooled data to assess the robustness of the solutions in a comparative perspective.

Usage

partition_min(
  dataset,
  units,
  time,
  cond,
  out,
  n_cut,
  incl_cut,
  solution,
  BE_cons,
  WI_cons,
  BE_ncut,
  WI_ncut
)

Arguments

dataset

Calibrated pooled dataset that is partitioned and minimized for deriving the pooled solution.

units

Units defining the within-dimension of data (time series). If no units are specified, the data is assumed to lack a dimension and be hierarchical.

time

Periods defining the between-dimension of data (cross sections). This should be specified because it does not make sense to partition a time series into individual data points.

cond

Conditions used for minimization

out

Outcome used for minimization

n_cut

Frequency cut-off for designating truth table rows as observed as opposed to designating them as remainders for the pooled data.

incl_cut

Inclusion (a.k.a. consistency) cut-off for designating truth table rows as consistent for the pooled data.

solution

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

BE_cons

Inclusion thresholds for creating an individual truth table for each cross section. They must be specified as a numeric vector. Its length should be equal the number of cross sections. The order of thresholds corresponds to the order of the cross sections in the data defined by the cross-section ID in the dataset (such as years in ascending order).

WI_cons

Inclusion thresholds for creating an individual truth table for each time series. They must be specified as a numeric vector. Its length should be equal the number of time series. The order of thresholds corresponds to the order of the of the time-series (unit) ID in the dataset (such as countries in alphabetical order).

BE_ncut

For cross sections, the minimum number of members needed for declaring a truth table row as relevant as opposed to designating it as a remainder. Must be specified as a numeric vector. Its length should be equal the number of cross sections. The order of thresholds corresponds to the order of the cross sections in the data defined by the cross-section ID in the dataset (such as years in ascending order).

WI_ncut

For time series, the minimum number of members needed for declaring a truth table row as relevant as opposed to designating it as a remainder. Must be specified as a numeric vector. Its length should be equal the number of time series. The order of thresholds corresponds to the order of the of the time-series (unit) ID in the dataset (such as countries in alphabetical order).

Value

A dataframe summarizing the partition-specific and pooled solutions with the following columns:

  • type: The type of the partition. pooled are rows with information on the pooled data; between is for cross-section partitions; within is for time-series partitions.

  • partition: Specific dimension of the partition at hand. For between-dimension, the unit identifiers are included here (argument units). For the within-dimension, the time identifier are listed (argument time). The entry is - for the pooled data without partitions.

  • solution: The solution derived for the partition or the pooled data. Absence of a condition is denoted by the ~ sign.

  • model: Running ID for models. In the presence of model ambiguity, each model has its own row with its individual solution and parameters. The rest of the information in the row is duplicated, for example by having two rows for the within-partition 1996. The column model highlights the presence of model ambiguity by numbering all models belonging to the same solution. For example, if three consecutive rows are numbered 1, 2 and 3, then these rows belong to the same solution and represent model ambiguity. If a 1 in a row is followed by another 1, then there is no model ambiguity.

  • consistency: The consistency score (a.k.a. inclusion score) for the partition of the data or the pooled data.

  • coverage: The coverage score for the partition of the data or the pooled data.

Examples

# \donttest{
# load data from Thiem (2011; see data documentation)

data(Thiem2011)

Thiem_pars <- partition_min(
  dataset = Thiem2011,
  units = "country", time = "year",
  cond = c("fedismfs", "homogtyfs", "powdifffs", "comptvnsfs", "pubsupfs", "ecodpcefs"),
  out = "memberfs",
  n_cut = 1, incl_cut = 0.8,
  solution = "P",
  BE_cons = c(0.9, 0.8, 0.7, 0.8, 0.6, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8),
  WI_cons = c(0.5, 0.8, 0.7, 0.8, 0.6, rep(0.8, 10)))
 # }