knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

library(QCAcluster) library(knitr) # nicer html tables

We use the data from Thiem (2011) for illustrating how the function `wop()`

calculates the weights of partitions. The weight of a partition is defined on the level of individual models and can be calculated for the *consistency* and *coverage* value of a model that has been derived from the pooled data. The weight of a partition for the consistency value of the pooled solution is calculated by applying the consistency formula only to the cases that belong to a partition. The weight of partition is calculated in *absolute* terms by calculating separately its contribution to the numerator and denominator of the formula. When one divides the partition-specific absolute contribution to the numerator by the contribution to the denominator, then one receives the partition-specific consistency or coverage score (depending on the type of formula).

The arguments of the functions are:

`n_cut`

: Frequency threshold for pooled data`incl_cut`

: Inclusion threshold (a.k.a. consistency threshold) for pooled data`solution`

: Either`C`

for conservative solution (a.k.a. complex solution) or`P`

for parsimonious 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.

# load data (see data description for details) data("Thiem2011") # calculate weight of partitions 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) kable(wop_pars)

When one aggregates the partition-specific absolute weights for the between-dimension or within-dimension, one gets the absolute value for the pooled solution. We illustrate this with the following chunk

# sum over all cross-sections for consistency denominator sum(wop_pars[wop_pars$type == "between", ]$denom_cons) # sum over all time series for coverage numerator sum(wop_pars[wop_pars$type == "within", ]$num_cov)

On the basis of the absolute weights, one can calculate the *relative weight* of a partition by dividing its absolute contribution by the corresponding value for the pooled solution.

# relative contribution of cross sections to denominator for consistency wop_between <- wop_pars[wop_pars$type == "between", ] wop_between$rel_denom_cons <- round(wop_between$denom_cons / sum(wop_between$denom_cons), digits = 2) kable(wop_between)

The weight of partitions for intermediate solutions is produced with `wop_inter()`

. We use data from Schwarz 2016 to illustrate the function.

# load data (see data description for details) data("Schwarz2016") # calculating weight of partitions Schwarz_wop_inter <- partition_min_inter( Schwarz2016, units = "country", time = "year", cond = c("poltrans", "ecotrans", "reform", "conflict", "attention"), out = "enlarge", n_cut = 1, incl_cut = 0.8, intermediate = c("1", "1", "1", "1", "1")) kable(Schwarz_wop_inter)

Yihui Xie (2021): *knitr: A General-Purpose Package for Dynamic Report Generation in
R.* R package version 1.33.

Yihui Xie (2015): *Dynamic Documents with R and knitr.* 2nd edition. Chapman and
Hall/CRC. ISBN 978-1498716963

Yihui Xie (2014): *knitr: A Comprehensive Tool for Reproducible Research in R.* In
Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing
Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595

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