[Qca-commits] r38 - in pkg: . inst man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Nov 20 12:31:14 CET 2014
Author: alrikthiem
Date: 2014-11-20 12:31:14 +0100 (Thu, 20 Nov 2014)
New Revision: 38
Modified:
pkg/NAMESPACE
pkg/inst/CITATION
pkg/inst/ChangeLog
pkg/inst/TODO
pkg/man/QCA.package.Rd
pkg/man/calibrate.Rd
pkg/man/deMorgan.Rd
pkg/man/eqmcc.Rd
pkg/man/factorize.Rd
pkg/man/pof.Rd
pkg/man/superSubset.Rd
pkg/man/truthTable.Rd
Log:
Added function "retention" (previously "perturb" and created documentation; updated documentation; renamed data sets
Modified: pkg/NAMESPACE
===================================================================
--- pkg/NAMESPACE 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/NAMESPACE 2014-11-20 11:31:14 UTC (rev 38)
@@ -36,6 +36,7 @@
print.pic,
print.sS,
print.tt,
+ retention,
rowDominance,
solveChart,
sortMatrix,
Modified: pkg/inst/CITATION
===================================================================
--- pkg/inst/CITATION 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/inst/CITATION 2014-11-20 11:31:14 UTC (rev 38)
@@ -13,6 +13,6 @@
note = note,
url = "http://cran.r-project.org/package=QCA",
textVersion = paste("Dusa, Adrian, and Alrik Thiem", sprintf("%s", year),
- title, note, "URL: http://cran.r-project.org/package=QCA", sep = ". ")
+ title, note, "URL: http://cran.r-project.org/package=QCA.", sep = ". ")
)
Modified: pkg/inst/ChangeLog
===================================================================
--- pkg/inst/ChangeLog 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/inst/ChangeLog 2014-11-20 11:31:14 UTC (rev 38)
@@ -1,3 +1,9 @@
+Version 1.1-4
+ o Improved: documentation now includes Digital Object Identifier (DOI) links
+ to cited references or direct URL
+ o Renamed: all data sets renamed with more meaningful expressions (from author
+ abbreviation to topic keyword)
+
Version 1.1-3
o New: number of PIs processable by solveChart() now limited (maximum integer
number that can be treated by the machine)
@@ -3,6 +9,5 @@
o New: three data sets added (d.AS, d.HMN, d.Kil)
o Improved: documentation for various functions
- o Changed: argument "all.sol" in function eqmcc() renamed to "min.dis" and set
- to FALSE by default
+ o Changed: argument "all.sol" in function eqmcc() renamed to "min.dis"
o Changed: argument "inf.test" in eqmcc() placed before "use.tilde"
o Renamed: in output from various functions prefix "S" (solution) renamed to
@@ -14,8 +19,8 @@
o Fixed: case names assigned to incorrect PIs in function
demoChart() under some conditions (thanks to M. Vink)
o Fixed: very large pof() objects didn't generated an error on print
- o Fixed: more informative error message when the truth table didn't contain
- any explained values (thanks to Mattia Zulianello)
+ o Fixed: more informative error message when the truth table did not contain
+ any explained values (thanks to M. Zulianello)
Version 1.1-2
o Fixed: missing data triggered an error message in function truthTable()
Modified: pkg/inst/TODO
===================================================================
--- pkg/inst/TODO 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/inst/TODO 2014-11-20 11:31:14 UTC (rev 38)
@@ -1,8 +1,7 @@
- make pof() accept string expressions like "A*B + C => D" and calculate parameters of fit
- function deMorgan(): negate set-theoretic expressions with multivalent variables
-- correct error message "Error: All combinations have been included into the analysis. Please check the truth table."
- minimization for multiple outcomes that are not conditions
- inference-statistical tests for fuzzy sets (check paper by Bear Braumoeller and Eliason and Stryker)
- add argument to eqmcc for specifying temporal order
-- sensitivity diagnostics
-- reconstruct full truth tables from minimized solutions (call it "develop")
\ No newline at end of file
+- sensitivity diagnostics (include function "perturb")
+- reconstruct developed normal functions from normal functions (call it "develop")
\ No newline at end of file
Modified: pkg/man/QCA.package.Rd
===================================================================
--- pkg/man/QCA.package.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/QCA.package.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -23,7 +23,8 @@
Ragin and Strand 2008). The \pkg{QCA} package extends these basic variants in
different directions by implementing complementary functionality. For example,
intermediate solutions and summary statistics are also available in mvQCA (Thiem
-2014a).
+2014a). Note that functionality for gsQCA (Thiem, 2014b) is in development but
+not yet implemented.
As of version 0.4-5, \pkg{QCA} has offered a new function called \code{\link{eqmcc}}
(\dfn{enhanced Quine-McCluskey}; \emph{e}QMC) that finds exact solutions much
@@ -41,15 +42,15 @@
Several data sets from various areas of the social science are integrated in
\pkg{QCA} so as to facilitate users' familiarization with the package's functionality.
-Areas currently covered include business, management & organization (\code{\link{d.CZH}}),
-education (\code{\link{d.SS}}), environmental sciences (\code{\link{d.Bas}}),
-evaluation (\code{\link{d.SA}}), legal studies (\code{\link{d.AS}}), political
-science (\code{\link{d.Emm}}, \code{\link{d.HK}}, \code{\link{d.Kro}}), public
-health (\code{\link{d.BWB}}), urban affairs (\code{\link{d.Kil}}), and sociology
-(\code{\link{d.CS}}, \code{\link{d.HMN}}). For more details, see the data sets'
-documentation files. Please also note that many more data sets for QCA in both raw
-and calibrated set format are available on the COMPASSS website at
-\url{http://www.compasss.org/bibdata.htm}.
+Areas currently covered include business, management & organization (\code{\link{d.stakeholder}}),
+education (\code{\link{d.education}}), environmental sciences (\code{\link{d.biodiversity}}),
+evaluation (\code{\link{d.transport}}), legal studies (\code{\link{d.napoleon}}),
+political science (\code{\link{d.jobsecurity}}, \code{\link{d.partybans}}, \code{\link{d.represent}}),
+public health (\code{\link{d.health}}), urban affairs (\code{\link{d.urban}}),
+and sociology (\code{\link{d.homeless}}, \code{\link{d.socialsecurity}}). For more
+details, see the data sets' documentation files. Please also note that many more
+data sets for QCA in both raw and calibrated format are available on the COMPASSS
+website at \url{http://www.compasss.org/bibdata.htm}.
How to make the most of the package's capabilities is demonstrated in detail by
Thiem and Dusa (2013c) with examples from political science (note that data sets
@@ -63,7 +64,8 @@
not least for reasons of scientific transparency. The software citation for \pkg{QCA}
loads on attaching the package or by using the command \code{citation("QCA")} after
having loaded the package. The aforesaid command also provides a suitable BibTeX
-entry.
+entry. In addition, please either cite Thiem and Dusa (2013b) or/and Thiem and
+Dusa (2013c).
}
\details{
Modified: pkg/man/calibrate.Rd
===================================================================
--- pkg/man/calibrate.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/calibrate.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -43,8 +43,8 @@
For \code{type = "fuzzy"}, this function can generate fuzzy sets by linear,
s-shaped, inverted s-shaped and logistic transformation for end-point concepts.
It can generate fuzzy sets by trapezoidal, triangular and bell-shaped transformation
-for mid-point concepts (Bojadziev and Bojadziev 2007; Clark et al. 2008; Thiem
-and Dusa 2013).
+for mid-point concepts (Bojadziev and Bojadziev 2007; Clark et al. 2008; Thiem 2014;
+Thiem and Dusa 2013).
For calibrating fuzzy sets based on end-point concepts, \code{thresholds} should
be specified as a numeric vector \code{c(thEX, thCR, thIN)}, where
@@ -98,6 +98,11 @@
Mark J. Wierman. 2008. \emph{Applying Fuzzy Mathematics to Formal Models in Comparative
Politics}. Berlin: Springer.
+Thiem, Alrik. 2014. \dQuote{Membership Function Sensitivity of Descriptive Statistics
+in Fuzzy-Set Relations.} \emph{International Journal of Social Research Methodology}
+17 (6):625-42.
+DOI: \href{http://dx.doi.org/10.1080/13645579.2013.806118}{10.1080/13645579.2013.806118}.
+
Thiem, Alrik, and Adrian Dusa. 2013. \emph{Qualitative Comparative Analysis with R:
A User's Guide}. New York: Springer.
}
Modified: pkg/man/deMorgan.Rd
===================================================================
--- pkg/man/deMorgan.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/deMorgan.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -62,8 +62,8 @@
# use solution object of class "qca" returned by eqmcc() function;
# even with multiple models
-data(d.Kro)
-Kro.sol <- eqmcc(d.Kro, outcome = "WNP", include = "?")
+data(d.represent)
+Kro.sol <- eqmcc(d.represent, outcome = "WNP", include = "?")
deMorgan(Kro.sol)
}
Modified: pkg/man/eqmcc.Rd
===================================================================
--- pkg/man/eqmcc.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/eqmcc.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -144,7 +144,7 @@
\code{min.dis} may lead to the identification of so many models that they cannot
be returned. Users should also be aware that \emph{for purposes of causal data
analysis, neither} \code{row.dom} \emph{nor} \code{min.dis} \emph{should be
-operative}.
+operative} (Thiem 2014b).
The argument \code{omit} can be used to omit any configuration (positive,
negative or remainder) from the minimization process. It accepts a vector of row
@@ -162,7 +162,7 @@
so. For multivalent variables, multiple values have to be enclosed by double
quotes and separated by a semicolon (see mvQCA example using Hartmann and Kemmerzell
(2010) below). In some situations, directional expectations in mvQCA generate easy
-counterfactuals that do not contribute to parsimony (Thiem 2014). These so-called
+counterfactuals that do not contribute to parsimony (Thiem 2014a). These so-called
non-simplifying easy counterfactuals will not be part of the solution (see mvQCA
example using Sager and Andereggen (2012) below).
@@ -242,9 +242,13 @@
Analysis to Study Causal Order: Comment on Caren and Panofsky (2005).}
\emph{Sociological Methods & Research} 36 (4):431-41.
-Thiem, Alrik. 2014. \dQuote{Parameters of Fit and Intermediate Solutions in Multi-Value
+Thiem, Alrik. 2014a. \dQuote{Parameters of Fit and Intermediate Solutions in Multi-Value
Qualitative Comparative Analysis.} \emph{Quality & Quantity}.
DOI: \href{http://dx.doi.org/10.1007/s11135-014-0015-x}{10.1007/s11135-014-0015-x}.
+
+Thiem, Alrik. 2014b. \dQuote{Navigating the Complexities of Qualitative Comparative
+Analysis: Case Numbers, Necessity Relations, and Model Ambiguities.} \emph{Evaluation Review}.
+DOI: \href{http://dx.doi.org/10.1177/0193841x14550863}{10.1177/0193841x14550863}.
}
\seealso{\code{\link{truthTable}}, \code{\link{factorize}}}
@@ -252,17 +256,17 @@
\examples{
# csQCA using Krook (2010)
#-------------------------
-data(d.Kro)
-head(d.Kro)
+data(d.represent)
+head(d.represent)
# conservative solution
-eqmcc(d.Kro, outcome = "WNP")
+eqmcc(d.represent, outcome = "WNP")
# negated outcome, conservative solution
-eqmcc(d.Kro, outcome = "WNP", neg.out = TRUE)
+eqmcc(d.represent, outcome = "WNP", neg.out = TRUE)
# parsimonious solution with details and case names
-Kro.sp <- eqmcc(d.Kro, outcome = "WNP", include = "?",
+Kro.sp <- eqmcc(d.represent, outcome = "WNP", include = "?",
details = TRUE, show.cases = TRUE)
Kro.sp
@@ -278,26 +282,26 @@
}
# conservative solution with truth table object
-Kro.tt <- truthTable(d.Kro, outcome = "WNP")
+Kro.tt <- truthTable(d.represent, outcome = "WNP")
Kro.sc <- eqmcc(Kro.tt)
Kro.sc
# fsQCA using Emmenegger (2011)
#------------------------------
-data(d.Emm)
-head(d.Emm)
+data(d.jobsecurity)
+head(d.jobsecurity)
# parsimonious solution with details
-eqmcc(d.Emm, outcome = "JSR", incl.cut1 = 0.9, include = "?",
+eqmcc(d.jobsecurity, outcome = "JSR", incl.cut1 = 0.9, include = "?",
details = TRUE)
# intermediate solution
-Emm.si <- eqmcc(d.Emm, outcome = "JSR", incl.cut1 = 0.9,
+Emm.si <- eqmcc(d.jobsecurity, outcome = "JSR", incl.cut1 = 0.9,
include = "?", dir.exp = c(1,1,1,1,1,0), details = TRUE)
Emm.si
# are the prime implicants also sufficient for the negation of the outcome?
-pof(Emm.si$i.sol$C1P1$pims, outcome = "JSR", d.Emm, neg.out = TRUE,
+pof(Emm.si$i.sol$C1P1$pims, outcome = "JSR", d.jobsecurity, neg.out = TRUE,
relation = "suf")
# check PI chart for intermediate solution;
@@ -319,7 +323,7 @@
PIsc <- Emm.si$i.sol$C1P1$pims
par(mfrow = c(2, 2))
for(i in 1:4){
- plot(PIsc[, i], d.Emm$JSR, pch = 19, ylab = "JSR",
+ plot(PIsc[, i], d.jobsecurity$JSR, pch = 19, ylab = "JSR",
xlab = names(PIsc)[i], xlim = c(0, 1), ylim = c(0, 1),
main = paste("Prime Implicant", print(i)))
mtext(paste(
@@ -333,14 +337,14 @@
# mvQCA using Hartmann and Kemmerzell (2010)
#-------------------------------------------
-data(d.HK)
-head(d.HK)
+data(d.partybans)
+head(d.partybans)
# create vector of condition variables
conds <- c("C", "F", "T", "V")
# parsimonious solution, with contradictions included
-HK.sp <- eqmcc(d.HK, outcome = "PB{1}", conditions = conds,
+HK.sp <- eqmcc(d.partybans, outcome = "PB{1}", conditions = conds,
incl.cut0 = 0.4, include = c("?", "C"), details = TRUE)
HK.sp
@@ -351,7 +355,7 @@
require(VennDiagram)
vennHK.suf <- venn.diagram(
x = list(
- "PB{1}" = which(d.HK$PB == 1),
+ "PB{1}" = which(d.partybans$PB == 1),
"C{0,1}" = which(PIms[, 1] == 1 | PIms[, 2] == 1),
"T{2}" = which(PIms[, 4] == 1),
"T{1}*V{0}" = which(PIms[, 5] == 1)),
@@ -363,26 +367,26 @@
grid.draw(vennHK.suf)
# which are the two countries in T{2} but not PB{1}?
-rownames(d.HK[d.HK$T == 2 & d.HK$PB != 1, ])
+rownames(d.partybans[d.partybans$T == 2 & d.partybans$PB != 1, ])
# minimize contradictions (only one contradiction)
-eqmcc(d.HK, outcome = "PB{1}", conditions = conds, incl.cut0 = 0.4,
+eqmcc(d.partybans, outcome = "PB{1}", conditions = conds, incl.cut0 = 0.4,
explain = "C")
# intermediate solution with directional expectations:
# C{1}, F{1,2}, T{2}, V contribute to OUT = 1
-HK.si <- eqmcc(d.HK, outcome = "PB{1}", conditions = conds,
+HK.si <- eqmcc(d.partybans, outcome = "PB{1}", conditions = conds,
include = "?", dir.exp = c(1, "1;2", 2, 1), details = TRUE)
HK.si
# mvQCA using Sager and Andereggen (2012)
#----------------------------------------
-data(d.SA)
-head(d.SA)
+data(d.transport)
+head(d.transport)
# directional expectation of FED{0} leads to non-simplifying
# easy counterfactual (see Thiem 2014 for more details)
-SA.si <- eqmcc(d.SA, outcome = "ACC{1}", conditions = names(d.SA)[1:5],
+SA.si <- eqmcc(d.transport, outcome = "ACC{1}", conditions = names(d.transport)[1:5],
include = "?", dir.exp = c(0,1,0,1,1), details = TRUE)
SA.si
@@ -390,13 +394,13 @@
# tQCA using Ragin and Strand (2008)
#-----------------------------------
-data(d.RS)
-head(d.RS)
+data(d.graduate)
+head(d.graduate)
# conservative solution with details and case names;
# auxiliary temporal order condition "EBA" automatically excluded
# from parameters of fit
-eqmcc(d.RS, outcome = "REC", details = TRUE, show.cases = TRUE)
+eqmcc(d.graduate, outcome = "REC", details = TRUE, show.cases = TRUE)
# QCA path models ("causal chain" in CNA); data from Baumgartner (2009);
# note that CNA and QCA results are not always equal because CNA applies a
Modified: pkg/man/factorize.Rd
===================================================================
--- pkg/man/factorize.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/factorize.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -58,13 +58,13 @@
factorize("one&TWO&four + one&THREE + THREE&four", prod.split = "&")
# factorize solution objects directly
-data(d.HK)
-HK.sol <- eqmcc(d.HK, outcome = "PB", conditions = c("C", "F", "T", "V"),
+data(d.partybans)
+HK.sol <- eqmcc(d.partybans, outcome = "PB", conditions = c("C", "F", "T", "V"),
include = c("?"))
factorize(HK.sol)
-data(d.Emm)
-Emm.sol <- eqmcc(d.Emm, outcome = "JSR", incl.cut1 = 0.9)
+data(d.jobsecurity)
+Emm.sol <- eqmcc(d.jobsecurity, outcome = "JSR", incl.cut1 = 0.9)
factorize(Emm.sol)
# sort by the largest number of factoring sets
Modified: pkg/man/pof.Rd
===================================================================
--- pkg/man/pof.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/pof.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -79,54 +79,54 @@
\examples{
# csQCA using Krook (2010)
#-------------------------
-data(d.Kro)
-head(d.Kro)
+data(d.represent)
+head(d.represent)
# first also compute negations
-x.1 <- d.Kro[, 1:5]
+x.1 <- d.represent[, 1:5]
x.2 <- 1 - x.1
names(x.2) <- tolower(names(x.1))
x <- cbind(x.1, x.2)
# necessity parameters of fit for all conditions
-pof(x, outcome = "WNP", d.Kro)
+pof(x, outcome = "WNP", d.represent)
# for the negated outcome
-pof(x, outcome = "WNP", d.Kro, neg.out = TRUE)
+pof(x, outcome = "WNP", d.represent, neg.out = TRUE)
# sufficiency parameters of fit
-pof(x, outcome = "WNP", d.Kro, relation = "suf")
+pof(x, outcome = "WNP", d.represent, relation = "suf")
# for the negated outcome
-pof(x, outcome = "WNP", d.Kro, neg.out = TRUE, relation = "suf")
+pof(x, outcome = "WNP", d.represent, neg.out = TRUE, relation = "suf")
# exact binomial tests of sufficiency and necessity inclusion
-pof(x.1, outcome = "WNP", d.Kro, relation = "suf", inf.test = "binom",
+pof(x.1, outcome = "WNP", d.represent, relation = "suf", inf.test = "binom",
incl.cut1 = 0.75, incl.cut0 = 0.5)
-pof(x.1, outcome = "WNP", d.Kro, inf.test = "binom", incl.cut1 = 0.75,
+pof(x.1, outcome = "WNP", d.represent, inf.test = "binom", incl.cut1 = 0.75,
incl.cut0 = 0.5)
# fsQCA using Emmenegger (2011)
#------------------------------
-data(d.Emm)
-head(d.Emm)
+data(d.jobsecurity)
+head(d.jobsecurity)
# first test for minimally necessary combinations with superSubset(),
# then check whether these combinations are also necessary for the
# negation of the outcome
-Emm.nr <- superSubset(d.Emm, outcome = "JSR", incl.cut = 0.965,
+Emm.nr <- superSubset(d.jobsecurity, outcome = "JSR", incl.cut = 0.965,
cov.cut = 0.6)
Emm.nr
-pof(Emm.nr$coms, outcome = "JSR", d.Emm, neg.out = TRUE)
+pof(Emm.nr$coms, outcome = "JSR", d.jobsecurity, neg.out = TRUE)
# first derive the conservative solution, then check whether the
# negations of the prime implicants are also sufficient for the outcome
-Emm.sc <- eqmcc(d.Emm, outcome = "JSR", incl.cut1 = 0.9, details = TRUE)
+Emm.sc <- eqmcc(d.jobsecurity, outcome = "JSR", incl.cut1 = 0.9, details = TRUE)
Emm.sc
-pof(1 - Emm.sc$pims, outcome = "JSR", d.Emm, relation = "suf")
+pof(1 - Emm.sc$pims, outcome = "JSR", d.jobsecurity, relation = "suf")
# parameters of fit for matrix of implicants;
# "-1" is the placeholder for an eliminated variable;
@@ -138,10 +138,10 @@
confs <- matrix(c(-1,-1,-1, 1, 0, 1,
1, 0, 1,-1, 1, 0), nrow = 2, byrow = TRUE)
-pof(confs, outcome = "JSR", d.Emm, relation = "suf")
+pof(confs, outcome = "JSR", d.jobsecurity, relation = "suf")
# or even vectors of line numbers from the implicant matrix
-pof(c(43, 57), "JSR", d.Emm, relation = "suf")
+pof(c(43, 57), "JSR", d.jobsecurity, relation = "suf")
# parameters of fit for a data frame
x <- data.frame(A = c(1,1,1,0,1), B = c(1,1,1,0,1),
Modified: pkg/man/superSubset.Rd
===================================================================
--- pkg/man/superSubset.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/superSubset.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -127,12 +127,12 @@
\examples{
# csQCA using Krook (2010)
#-------------------------
-data(d.Kro)
-head(d.Kro)
+data(d.represent)
+head(d.represent)
# find all minimally necessary combinations with an inclusion score
# of at least 0.9 and coverage score of at least 0.65
-Kro.ss <- superSubset(d.Kro, outcome = "WNP", incl.cut = 0.9,
+Kro.ss <- superSubset(d.represent, outcome = "WNP", incl.cut = 0.9,
cov.cut = 0.65)
Kro.ss
@@ -143,7 +143,7 @@
require(VennDiagram)
vn.Kro.nec <- venn.diagram(
x = list(
- "WNP" = which(d.Kro$WNP == 1),
+ "WNP" = which(d.represent$WNP == 1),
"wm+LP" = which(Kro.coms[, 1] == 1),
"WS+LP" = which(Kro.coms[, 2] == 1),
"ES+LP" = which(Kro.coms[, 3] == 1)),
@@ -156,11 +156,11 @@
# mvQCA using Hartmann and Kemmerzell (2010)
#-------------------------------------------
-data(d.HK)
-head(d.HK)
+data(d.partybans)
+head(d.partybans)
# find all minimally necessary combinations with perfect inclusion
-HK.ss <- superSubset(d.HK, outcome = "PB",
+HK.ss <- superSubset(d.partybans, outcome = "PB",
conditions = c("C", "F", "T", "V"))
HK.ss
@@ -169,18 +169,18 @@
# fsQCA using Emmenegger (2011)
#------------------------------
-data(d.Emm)
-head(d.Emm)
+data(d.jobsecurity)
+head(d.jobsecurity)
# find all minimally sufficient combinations with an inclusion score
# of at least 0.9 and coverage score of at least 0.4; also return
# PRI (proportional reduction in inconsistency) scores
-Emm.ss <- superSubset(d.Emm, outcome = "JSR", relation = "suf",
+Emm.ss <- superSubset(d.jobsecurity, outcome = "JSR", relation = "suf",
incl.cut = 0.9, cov.cut = 0.4, PRI = TRUE)
Emm.ss
# same criteria, but for the negation of the outcome
-Emm.ss.n <- superSubset(d.Emm, outcome = "JSR", neg.out = TRUE,
+Emm.ss.n <- superSubset(d.jobsecurity, outcome = "JSR", neg.out = TRUE,
relation = "suf", incl.cut = 0.9, cov.cut = 0.4, use.tilde = TRUE)
Emm.ss.n
@@ -188,7 +188,7 @@
head(Emm.coms <- Emm.ss.n$coms)
par(mfrow = c(2, 2))
for(i in 1:4){
- plot(Emm.coms[, i], 1 - d.Emm$JSR, pch = 19, ylab = "~JSR",
+ plot(Emm.coms[, i], 1 - d.jobsecurity$JSR, pch = 19, ylab = "~JSR",
xlab = names(Emm.coms)[i], xlim = c(0, 1), ylim = c(0, 1),
main = paste("Combination", print(i)))
mtext(paste(
Modified: pkg/man/truthTable.Rd
===================================================================
--- pkg/man/truthTable.Rd 2014-09-09 05:28:32 UTC (rev 37)
+++ pkg/man/truthTable.Rd 2014-11-20 11:31:14 UTC (rev 38)
@@ -48,9 +48,9 @@
\details{
The argument \code{data} requires a suitable data set. Suitable data sets have
-the following structure: values of 0 and 1 for bivalent crisp set variables,
-values between 0 and 1 for bivalent fuzzy set variables, and values beginning with
-0 at increments of 1 for multivalent crisp set variables. The placeholder "-"
+the following structure: values of 0 and 1 for bivalent crisp-set variables,
+values between 0 and 1 for bivalent fuzzy-set variables, and values beginning with
+0 at increments of 1 for multivalent crisp-set variables. The placeholder "-"
indicates a "don't care" value in auxiliary conditions that specify temporal order
between other substantive conditions in tQCA. These values lead to the exclusion
of the auxiliary condition from the computation of parameters of fit.
@@ -146,19 +146,19 @@
\examples{
# csQCA using Krook (2010)
#-------------------------
-data(d.Kro)
-head(d.Kro)
+data(d.represent)
+head(d.represent)
# print truth table
-truthTable(d.Kro, outcome = "WNP")
+truthTable(d.represent, outcome = "WNP")
# print complete truth table, show cases, and first sort by
# inclusion scores, then by number of cases
-truthTable(d.Kro, outcome = "WNP", complete = TRUE,
+truthTable(d.represent, outcome = "WNP", complete = TRUE,
show.cases = TRUE, sort.by = c("incl", "n"))
# code configurations with single case as remainders
-Kro.tt <- truthTable(d.Kro, outcome = "WNP", n.cut = 2,
+Kro.tt <- truthTable(d.represent, outcome = "WNP", n.cut = 2,
show.cases = TRUE)
Kro.tt
@@ -167,28 +167,28 @@
# fsQCA using Emmenegger (2011)
#------------------------------
-data(d.Emm)
-head(d.Emm)
+data(d.jobsecurity)
+head(d.jobsecurity)
# code non-remainder configurations with inclusion scores
# between 0.5 and 0.9 as contradictions
-Emm.tt <- truthTable(d.Emm, outcome = "JSR", incl.cut1 = 0.9,
+Emm.tt <- truthTable(d.jobsecurity, outcome = "JSR", incl.cut1 = 0.9,
incl.cut0 = 0.5)
Emm.tt
# truth table based on negated outcome set
-Emm.tt <- truthTable(d.Emm, outcome = "JSR", neg.out = TRUE,
+Emm.tt <- truthTable(d.jobsecurity, outcome = "JSR", neg.out = TRUE,
incl.cut1 = 0.9, incl.cut0 = 0.5)
Emm.tt
# mvQCA using Hartmann and Kemmerzell (2010)
#-------------------------------------------
-data(d.HK)
-head(d.HK)
+data(d.partybans)
+head(d.partybans)
# code non-remainder configurations with inclusion scores below 1
# but above 0.4 as contradictions
-HK.tt <- truthTable(d.HK, outcome = "PB",
+HK.tt <- truthTable(d.partybans, outcome = "PB",
conditions = c("C","F","T","V"), incl.cut0 = 0.4)
HK.tt
@@ -200,18 +200,18 @@
# code output function values in truth table based on
# exact binomial test (condition variable V dropped)
-HK.tt <- truthTable(d.HK, outcome = "PB",
+HK.tt <- truthTable(d.partybans, outcome = "PB",
conditions = c("C","F","T"), incl.cut1 = 0.9, incl.cut0 = 0.4,
show.cases = TRUE, inf.test = c("binom", 0.1))
HK.tt
# tQCA using Ragin and Strand (2008)
#-----------------------------------
-data(d.RS)
-head(d.RS)
+data(d.graduate)
+head(d.graduate)
# tQCA truth table with "don't care" values
-truthTable(d.RS, outcome = "REC")
+truthTable(d.graduate, outcome = "REC")
}
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