cna (version 2.2.3)

some: Randomly select configurations from a data frame or truthTab

Description

Randomly select configurations from a data frame or truthTab with or without replacement.

Usage

some(x, ...)
# S3 method for data.frame
some(x, n = 10, replace = TRUE, ...)
# S3 method for truthTab
some(x, n = 10, replace = TRUE, ...)

Arguments

x

Data frame or truthTab.

n

Sample size.

replace

Logical; if TRUE, configurations are sampled with replacement.

…

Not used.

Value

A data frame or truthTab.

Details

The function some randomly samples configurations from x, which is a data frame or truthTab. Such samples can, for instance, be used to simulate data fragmentation (limited diversity), i.e. the failure to observe/measure all configurations that are compatible with a data generating causal structure. They can also be used to simulate large-N data featuring multiple cases instantiating each configuration.

References

Krook, Mona Lena. 2010. “Women's Representation in Parliament: A Qualitative Comparative Analysis.” Political Studies 58(5):886-908.

See Also

truthTab, selectCases, allCombs, makeFuzzy, cna, d.women

Examples

# NOT RUN {
# Randomly sample configurations from the dataset analyzed by Krook (2010).
tt.women <- truthTab(d.women)
some(tt.women, 20)
some(tt.women, 5, replace = FALSE)
some(tt.women, 5, replace = TRUE)

# Simulate limited diversity in data generated by the causal structure 
# A=2*B=1 + C=3*D=4 <-> E=3.
dat1 <- allCombs(c(3, 3, 4, 4, 3))
dat2 <- selectCases("A=2*B=1 + C=3*D=4 <-> E=3", dat1, type = "mv")
(dat3 <- some(dat2, 150, replace = TRUE))
mvcna(dat3)

# Simulate large-N fuzzy-set data generated by the common-cause structure
# (A*b*C + B*c <-> D) * (A*B + a*C <-> E).
dat1 <- selectCases("(A*b*C + B*c <-> D) * (A*B + a*C <-> E)")
dat2 <- some(dat1, 250, replace = TRUE)
dat3 <- makeFuzzy(tt2df(dat2), fuzzvalues = seq(0, 0.45, 0.01))
fscna(dat3, ordering = list(c("D", "E")), strict = TRUE, con = .8, cov = .8)
# }