rean_cna {frscore} | R Documentation |
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, quiet = TRUE, ...)
Arguments
x |
A |
attempt |
Numeric vector that contains the values from which combinations of consistency and coverage thresholds are formed, to be used in the analyses. |
ncsf |
|
output |
Character vector that determines whether csfs, asfs, or mscs are
returned; |
n.init |
Integer that determines the maximum number of csfs built in
the analyses. See |
quiet |
Logical. When |
... |
Any arguments to be passed to |
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"
.
As executing a reanalysis series involves repeated calls to various cna
functions, numerous warnings about the value of n.init
, and messages
about asf
s being returned separately, may be generated. The argument
quiet
is used to suppress or display these warnings/messages:
the default quiet = TRUE
suppresses all such warnings and messages.
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
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