retention(data, outcome = "", conditions = "", type = "corruption", dep = TRUE, n.cut = 1, incl.cut = 1, p.pert = 0.5, n.pert = 1)TRUE indicating DPA - Dependent Perturbations
Assumption and if FALSE indicating IPA - Independent
Perturbations Assumption.The argument data requires a suitable data set, in the form of a data frame.
with the following structure: values of 0 and 1 for bivalent crisp-set variables.
The argument outcome specifies the outcome to be explained, in upper-case
notation (e.g. X).
The argument conditions specifies the names of the condition variables.
If omitted, all variables in data are used except outcome.
The argument type controls which type of perturbations should be simulated
to calculate the retention probability.
When type = "corruption", it simulates changes of values in the conditions
(values of 0 become 1, and values of 1 become 0). When type = "deletion",
it calculates the probability of retaining the same solution if a number of cases are
deleted from the original data.
The argument dep is a logical which choses between two categories of assumptions.
If dep = TRUE (the default) it indicates DPA - Dependent Perturbations Assumption,
when perturbations depend on each other and are tied to a fixed number of cases, ex-ante
(see Thiem, Spohel and Dusa, 2016).
If dep = FALSE, it indicates IPA - Independent Perturbations Assumption, when
perturbations are assumed to occur independently of each other.
The argument n.cut is one of the factors that decide which configurations
are coded as logical remainders or not, in conjunction with argument incl.cut.
Those configurations that contain fewer than n.cut cases with membership scores
above 0.5 are coded as logical remainders (OUT = "?"). If the number of such
cases is at least n.cut, configurations with an inclusion score of at least
incl.cut are coded positive (OUT = "1"), while configurations with an
inclusion score below incl.cut are coded negative (OUT = "0").
The argument p.pert specifies the probability of perturbation under the
type = "IPA" independent perturbations assumption.
The argument n.pert specifies the number of perturbations under the
type = "DPA" dependent perturbations assumption. At least one perturbation is
needed to possibly change a csQCA solution, otherwise the solution will remain the
same (retention equal to 100%) if zero perturbations occur under this argument.
Thiem, A.; Spoehel, R.; Dusa, A. (2016) “Enhancing Sensitivity Diagnostics for Qualitative Comparative Analysis: A Combinatorial Approach.” Political Analysis vol.24, no.1, pp.104-120.
if (require("QCA")) {
# the replication data, see Thiem, Spohel and Dusa (2015)
dat <- data.frame(matrix(c(
rep(1,25), rep(0,20), rep(c(0,0,1,0,0),3),
0,0,0,1,0,0,1,0,0,0,0, rep(1,7),0,1),
nrow = 16, byrow = TRUE, dimnames = list(c(
"AT","DK","FI","NO","SE","AU","CA","FR",
"US","DE","NL","CH","JP","NZ","IE","BE"),
c("P", "U", "C", "S", "W"))
))
# calculate the retention probability, for 2.5% probability of data corruption
# under the IPA - independent perturbation assuption
retention(dat, outcome = "W", type = "corruption", dep = FALSE,
p.pert = 0.025, incl.cut = 1)
# the probability that a csQCA solution will change
1 - retention(dat, outcome = "W", type = "corruption", dep = FALSE,
p.pert = 0.025, incl.cut = 1)
}Run the code above in your browser using DataLab