rean_cna {frscore}

rean_cna

Description

Perform a reanalysis series on a data set with cna() using all combinations of consistency and coverage threshold values in a given range of values

Usage

rean_cna(x, attempt = seq(1, 0.7, -0.1), ncsf = deprecated(),
output = c("csf", "asf", "msc"),
n.init = 1000, ...)

Arguments

x

A data.frame or configTable to be analyzed with cna(). In case of multi-value or fuzzy-set data, the data type must be indicated by type = "mv" and type = "fs", respectively.

attempt

Numeric vector that contains the values from which combinations of consistency and coverage thresholds are formed, to be used in the analyses.

ncsf

Allowed for backward compatibility, due to be dropped in future versions. Please use n.init instead.

output

Character vector that determines whether csfs, asfs, or mscs are returned; "csf" (default) returns csfs, "asf" asfs, and "msc" mscs.

n.init

Integer that determines the maximum number of csfs built in the analyses. See csf()

...

Any arguments to be passed to cna() except con, cov or con.msc. The effect of argument what is overriden by output.

Details

rean_cna() performs a reanalysis series of a data set x, which constitutes the first computational phase of fit-robustness scoring as introduced in Parkkinen and Baumgartner (2021). The series consists of cna() calls at all combinatorially possible consistency and coverage settings drawn from the vector attempt. If the output argument is set to its default value "csf", rean_cna() returns complex solutions formulas (csf), in case of "asf" only atomic solution formulas ("asf") are built, which is faster. The argument n.init allows for controlling the number of csf to be built, if output = "csf".

Value

A list where each element is a data frame containing the results of a single analysis of the input data set with cna(), each using a different combination of consistency and coverage threshold values. These values are added to the output as extra columns 'cnacon' and 'cnacov'.

References

V.P. Parkkinen and M. Baumgartner (2021), “Robustness and Model Selection in Configurational Causal Modeling,” Sociological Methods and Research, doi:10.1177/0049124120986200.

See Also

frscore(), cna()

Examples

# Crisp-set data
sols1 <- rean_cna(d.error, attempt = seq(1, 0.8, -0.1))
sols1 <- do.call(rbind, sols1)
sols1

# Multi-value data
sols2 <- rean_cna(d.pban, type = "mv", attempt = seq(0.9, 0.7, -0.1),
                  ordering = list("T", "PB"), strict = TRUE)
sols2 <- do.call(rbind, sols2)
sols2

# Fuzzy-set data
sols3 <- rean_cna(d.jobsecurity, type = "fs", attempt = seq(0.9, 0.7, -0.1),
                  ordering = list("JSR"), strict = TRUE) # execution takes a couple of seconds
sols3 <- do.call(rbind, sols2)
sols3