cluster.eqmcc: Diagnostic tool for clustered data

Description Usage Arguments Author(s) References See Also Examples

Description

Function returns pooled, within, and between consistencies for an object of class "qca".

Usage

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cluster.eqmcc(results, data, outcome, unit_id, cluster_id, intermed=FALSE, sol = 1)

Arguments

results

An object of class "qca". For performing cluster diagnostics of the sufficient solution for the negated outcome one must only use the eqmcc() result from the sufficiency analysis of the negated outcome.

data

A data frame.

outcome

A character string with the name of the outcome in capital letters. When performing cluster diagnostics of the sufficient solution for the negated outcome one must only use the eqmcc() result from the sufficiency analysis of the negated outcome in the argument results. Changing the name in the argument outcome or using a tilde is not necessary.

unit_id

A character string with the name of the vector containing the units (i.e. countries).

cluster_id

A character string with the name of the vector containing the clustering units (i.e. years).

intermed

Logical. Use the intermediate solution?

sol

A numeric vector where the first number indicates the number of the solution according to the order in the "qca" object.

Author(s)

Ioana-Elena Oana and Juraj Medzihorsky

References

Garcia-Castro, A., Arino, M. A.. 2013. A General Approach to Panel Data Set-Theoretic Research. COMPASSS Working Paper 2013-76

See Also

eqmcc

Examples

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# Import your clustered data in the long format. 
# For example:

data(SchneiderLong)

# Get the intermediate solution:

sol_yi <- eqmcc(SchneiderLong, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut1 = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))

# Get pooled, within, and between consistencies for the second intermediate solution:

cluster.eqmcc(results = sol_yi, data = SchneiderLong, outcome = "EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR", intermed = TRUE, sol = 2)


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