Package 'SetMethods'

Description

This initiated as a package companion to the book by C. Q. Schneider and C. Wagemann "Set-Theoretic Methods for the Social Sciences", Cambridge University Press. It has now grown to include functions for performing set-theoretic multi-method research (Schneider 2023, Schneider and Rohlfing 2013), QCA for clustered data (Garcia-Castro and Arino 2013), theory evaluation, Enhanced Standard Analysis, QCA Radar Charts, indirect calibration, robustness tests (Oana and Schneider - 2021) etc.. Additionally it includes data to replicate the examples in the book by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", 2021, Cambridge University Press.

Details

The package contains functions to perform set-theoretic multi-method research, theory evaluation, QCA for clustered data, Enhanced Standard Analyses, indirect calibration, calculate parameters of fit and produce XY plots and QCA Radar Charts, perform robustness tests, etc.. Furthermore, it contains all the data used in the Schneider and Wagemann (2012) and Oana, Schneider, and Thomann (2021) books.

Author(s)

Ioana-Elena Oana [aut, cre], Juraj Medzihorsky [aut], Carsten Q. Schneider [aut], Mario Quaranta [aut]

Maintainer: Ioana-Elena Oana <[email protected]>

References

Schneider, C. Q. (2023). Set-Theoretic Multi-Method Research: A Guide to Combining QCA and Case Studies. Cambridge: Cambridge University Press.

Oana, I.E., Schneider, C. Q., Thomann, E. (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge: Cambridge University Press.

Oana, I.E., Schneider, C. Q. (2021). A Robustness Test Protocol for Applied QCA: Theory and R Software Application. Sociological Methods and Research. https://doi.org/10.1177/00491241211036158.

Oana, I.E., Schndeider, C.Q. (2018) SetMethods: An Add-on R Package for Advanced QCA.The R Journal 10(1): 507-33.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge

Schneider, C. Q., Rohlfing, I. (2013) Combining QCA and Process Tracing in Set-Theoretic Multi-Method Research. Sociological Methods Research 42(4): 559-597

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Garcia-Castro, A., Arino, M. A.. (2013) A General Approach to Panel Data Set-Theoretic Research. COMPASSS Working Paper 2013-76

Description

A function that identifies cases with 0.5 fuzzy-set values.

Usage

ambig.cases(data)

Arguments

Author(s)

Ioana-Elena Oana

Examples

# Import your data. For example:

data(SCHF)

# Get cases with 0.5 in the entire dataframe:

ambig.cases(SCHF)

# Get cases with 0.5 in the column "EMP" in the dataframe:

ambig.cases(SCHF$EMP)

# Get cases with 0.5 in the 7th column of the dataframe:

ambig.cases(SCHF[,7])

Description

The BCMV data frame has 18 rows and 5 variables

Usage

data(BCMV)

Format

A data frame with 18 observations on the following 5 variables.

GNP

a numeric vector. Condition, Gross National Product/Capita (ca. 1930). 0 if below 500$, 1 if between 550 and 850$, 2 above 850$.

URB

a numeric vector. Condition, urbanization (population in towns with 20000 and more inhabitants); 0 if below 50 per cent; 1 if above.

LIT

a numeric vector. Condition, literacy: 0 if below 75 per cent; 1 if above.

INDUS

a numeric vector. Condition, Industrial Labour Force (incl. mining); 0 if below 30 per cent of active population; 1 if above.

DEMOC

a numeric vector. Condition, stability of a democracy: 0 if not stable; 1 if stable.

Details

The data are used by Berg-Schlosser and Cronqvist (2005) to demostrate mvQCA. The original data are from Lipset (1963). Data are multi-value.

References

Berg-Schlosser, D. and Cronqvist, L. (2005) "Macro-Quantitative vs. Macro-Qualitative Methods in the Social Sciences - An Example from Empirical Democratic Theory", Historical Social Research 30, pp. 154-175.

Lipset, Seymour M. (1963) Political Man. The Social Bases of Politics. Doubleday: New York.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann

Examples

data(BCMV)

Description

Function returns pooled, within, and between consistencies for the relationship between two sets, for an object of class "qca", and for a Boolean expression.

Usage

cluster(data=NULL, results, outcome, 
      unit_id, cluster_id, sol = 1, 
      necessity = FALSE, wicons = FALSE)

Arguments

Author(s)

Ioana-Elena Oana

References

Garcia-Castro, Roberto, and Miguel A. Arino. 2016. "A General Approach to Panel Data Set-Theoretic Research."" Journal of Advances in Management Sciences & Information Systems 2: 6376.

See Also

minimize

Examples

# Import your clustered data in the long format. 
# For example:

data(SCHLF)

# Get the intermediate solution:

sol_yi <- minimize(SCHLF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))

# Get pooled, within, and between consistencies for the intermediate solution:

cluster(SCHLF, sol_yi, "EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR", sol = 1)
              
# or:

cluster(SCHLF, sol_yi, "EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR", sol = "c1p1i1")

# Get pooled, within, and between consistencies for EMP as necessary for EXPORT:

cluster(SCHLF, results="EMP", outcome="EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR", necessity=TRUE)
# or:

cluster(results=SCHLF$EMP, outcome=SCHLF$EXPORT, unit_id = SCHLF$COUNTRY, 
              cluster_id = SCHLF$YEAR, necessity=TRUE)
              
# Get pooled, within, and between consistencies for ~EMP as necessary for EXPORT:

cluster(SCHLF, results="~EMP", outcome="EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR", necessity=TRUE)
# or:

cluster(results=1-SCHLF$EMP, outcome=SCHLF$EXPORT, unit_id = SCHLF$COUNTRY, 
              cluster_id = SCHLF$YEAR, necessity=TRUE)                  
              
# Get pooled, within, and between consistencies for EMP*~MA*STOCK as sufficient for EXPORT:

cluster(SCHLF, "EMP*~MA*STOCK", "EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR") 
              
# Get pooled, within, and between consistencies for EMP*MA + ~STOCK as sufficient for ~EXPORT:

cluster(SCHLF, "EMP*MA + ~STOCK", "~EXPORT", unit_id = "COUNTRY", 
              cluster_id = "YEAR")

Description

Function for plotting pooled, between, and within consistencies for a cluster diagnostics. For a sufficient solution, the function returns plots for the entire solution and each sufficient term.

Usage

cluster.plot(cluster.res,
           labs = TRUE,
           size = 5,
           angle = 0,
           wicons = FALSE,
           wiconslabs = FALSE,
           wiconssize = 5,
           wiconsangle = 90)

Arguments

Author(s)

Ioana-Elena Oana

References

Garcia-Castro, Roberto, and Miguel A. Arino. 2016. "A General Approach to Panel Data Set-Theoretic Research."" Journal of Advances in Management Sciences & Information Systems 2: 6376.

Examples

# Load the data:
data(PAYF)

# Create a sufficient solution using minimize:
PS <- minimize(data = PAYF,
                        outcome  = "HL",
                        conditions = c("HE","GG","AH","HI","HW"),
                        incl.cut = 0.9,
                        n.cut = 2,
                        include = "?",
                        details = TRUE, 
                        show.cases = TRUE)
PS

# Perform cluster diagnostics:

CB <- cluster(data = PAYF,
           results = PS,
           outcome = "HL",
           unit_id = "COUNTRY",
           cluster_id = "REGION",
           necessity=FALSE,
           wicons = FALSE)
CB

# Plot pooled, between, and within consistencies:

cluster.plot(cluster.res = CB,
             labs = TRUE,
             size = 8,
             angle = 6,
             wicons = TRUE)

Description

The EMMF data frame has 19 rows and 8 sets

Usage

data(EMMF)

Format

A data frame with 19 observations on the following 8 sets.

country

a factor with levels Australia Austria Belgium Canada Denmark Finland France Germany Ireland Italy Netherlands NewZealand Norway Portugal Spain Sweden Switzerland UK USA

s

a numeric vector. Condition, state-society relationships.

c

a numeric vector. Condition, non-market coordination.

l

a numeric vector. Condition, strength of the labour movement.

r

a numeric vector. Condition, religious denomination.

p

a numeric vector. Condition, strenght of religious parties.

v

a numeric vector. Condition, institutional veto points.

jsr

a numeric vector. Outcome, job-secturity regulations.

Details

Data are used by Emmenegger (2011) to analyze job-security regulations in Western democracies. The data are fuzzy-sets.

References

Emmenegger, P. (2011) "Job-security regulations in Western democracies", European Journal of Political Research 50, pp. 336-364.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(EMMF)

Description

Function that performs the Enhanced Standard Analysis.

Usage

esa(oldtt, nec_cond, untenable_LR, contrad_rows)

Arguments

Value

It returns a new truth table in which all truth table rows are set to outcome value 0 that would otherwise present untenable assumptions.

Author(s)

Ioana-Elena Oana

References

Schneider, C. Q., Wagemann, C. 2012. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 8.

See Also

minimize

Examples

# Import your data. For example:

data(SCHF)

# Get the truth table for the presence of the outcome:

TT_y <- truthTable(SCHF, outcome = "EXPORT",
                   conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                   incl.cut = .9,
                   complete = TRUE,
                   PRI = TRUE,
                   sort.by = c("out", "incl", "n"))

# Exclude condition STOCK + MA and condition EMP as necessary for EXPORT
# Exclude all remainder rows containing the combination BARGAIN*~OCCUP
# Exclude the rows "19", "14", "46", "51" as contradictory:

newtt <- esa(oldtt = TT_y, nec_cond = c("STOCK+MA", "EMP"), 
          untenable_LR = "BARGAIN*~OCCUP", contrad_rows = c("19", "14", "46", "51"))

# The truth table newly created can afterwards be used in further analyses

Description

The FakeCS data frame has 30 rows and 5 sets

Usage

data(FakeCS)

Format

A data frame with 30 observations on the following 5 sets.

y

a numeric vector. Outcome with 2 categories (crisp-set).

j

a numeric vector. Condition with 2 categories (crisp-set).

z

a numeric vector. Condition with 2 categories (crisp-set).

w

a numeric vector. Condition with 2 categories (crisp-set).

k

a numeric vector. Condition with 2 categories (crisp-set).

Details

The data frame has only exercise purpuses to let the user learn how to perform crisp-set QCA in R.

References

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(FakeCS)

Description

mvQCA data frame has 25 rows and 4 sets.

Usage

data(FakeMV)

Format

A data frame with 25 observations on the following 4 sets.

Y

a numeric vector. Outcome with 2 categories (crisp).

A

a numeric vector. Condition with 2 categories (crisp).

B

a numeric vector. Condition with 3 categories (multi-value).

C

a numeric vector. Condition with 3 categories (multi-value).

Details

The data frame has only exercise purpuses to let the user learn how to perform mvQCA in R.

References

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(FakeMV)

Description

The FSR data frame has 16 rows and 8 sets

Usage

data(FSR)

Format

A data frame with 16 observations on the following 8 sets.

integrated_comp_schools

a numeric vector. Condition, percentage of Pupils Enrolled in Integrated Comprehensive Schools,

coop_comp_schools

a numeric vector. Condition, percentage of Pupils Enrolled in Cooperative Comprehensive Schools.

full_day_schools

a numeric vector. Condition, percentage of Pupils Enrolled in All-Day Schools

child_care

a numeric vector. Condition, ratio of Number of Child Care Facilities to Total Population between 0 and 6 Years (percent).

pre_schools

a numeric vector. Condition, ratio of pupils Enrolled in Pre-School to Total 6-Year-Old Population (per cent)

early_tracking

a numeric vector. Condition, onset of Tracking, Legal Regulation.

outcome

a numeric vector. Outcome, high Degree of Social Inequality Cases in Education.

indep_hauptschule

a numeric vector. Condition, autonomy of the Hauptschule.

Details

Data are used by Freitag and Schlicht (2009) to analyze social inequality in education. The data are raw scores.

References

Freitag, M, and Schlicht, R. (2009) "Educational Federalism in Germany: Foundations of Social Inequalities in Education", Governance 22(1), pp. 47-72.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(FSR)

Description

indirectCalibration is a function for the indirect calibration procedure as described by Ragin (2008). It uses a binomial or a beta regression for tranforming raw scores into calibrated scores. In our opinion, using a fractional polynomial may not be appropriate to this case. In fact, we do not deal with proportions. This function requires the package betareg.

Usage

indirectCalibration(x, x_cal, binom = TRUE)

Arguments

Value

It returns a vector of indirectly calibrated values.

Author(s)

Mario Quaranta

References

Ragin, C. C. (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond, The Chicago University Press: Chicago and London.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

# Generate fake data
set.seed(4)
x <- runif(20, 0, 1)

# Find quantiles
quant <- quantile(x, c(.2, .4, .5, .6, .8))

# Theoretical calibration
x_cal <- NA  
x_cal[x <= quant[1]] <- 0
x_cal[x > quant[1] & x <= quant[2]] <- .2
x_cal[x > quant[2] & x <= quant[3]] <- .4
x_cal[x > quant[3] & x <= quant[4]] <- .6
x_cal[x > quant[4] & x <= quant[5]] <- .8
x_cal[x > quant[5]] <- 1
x_cal

# Indirect calibration (binomial)
a <- indirectCalibration(x, x_cal, binom = TRUE)

# Indirect calibration (beta regression)
b <- indirectCalibration(x, x_cal, binom = FALSE)

# Correlation
cor(a, b)

# Plot
plot(x, a); points(x, b, col = "red")

Description

Function that intersects two boolean expressions.

Usage

intersectExp(expression1, expression2)

Arguments

Value

It returns the boolean expression representing the intersection of the two inputed expressions.

Author(s)

Ioana-Elena Oana

Examples

intersectExp("~EMP*MA", "MA+~STOCK*OCCUP")
intersectExp("~A*B + C*~D","A*B+~D")

Description

Fake fuzzy-set job motivation data used for Chapter 3 of the Oana et al. (forthcoming) book.

Usage

data(JOBF)

Format

A data frame with 17 observations on the following 3 variables.

HS

Condition: High salary

FS

Condition: Flexible schedule

LS

Condition: Lenient supervisor

LJS

Condition: Low job security

PP

Condition: High promotion potential

HM

Outcome: High motivation

FSorLS

Condition:

References

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

data(JOBF)

Description

The KAF data frame has 13 rows and 5 sets

Usage

data(KAF)

Format

A data frame with 13 observations on the following 5 sets.

supranat

a numeric vector. Government support for supranational CFSP.

identmass

a numeric vector. European identity of the general public.

conform

a numeric vector. Policy conformity.

region

a numeric vector. Regional governance.

capab

a numeric vector. Material capabilities.

Details

Data are used by Koenig-Archibugi (2004) to analyze government preferences for istitutional change in EU foreign and security policy. Data are fuzzy-sets.

References

Koenig-Archibugi, M. (2004) "Explaining Government Preferences for Istitutional Change in EU Foreign and Security Policy", International Organization 58, pp.137-174.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(KAF)

Description

The LIPC data frame has 18 rows and 6 sets

Usage

data(LIPC)

Format

A data frame with 18 observations on the following 6 sets.

DEVELOPED

a numeric vector. Condition, economically developed country.

URBAN

a numeric vector. Condition, urbanized countries.

LITERATE

a numeric vector. Condition, countries with high literacy rate.

INDUSTRIAL

a numeric vector. Condition, Industrialized countries.

GOVSTAB

a numeric vector. Condition, politically stable countries.

SURVIVED

a numeric vector. Outcome, survival of democracy during the inter-war period.

Details

Data used by Ragin (2009) to illustrates the variants of QCA. Originally by Lipset (1959). Data are crisp-sets.

References

Lipset, S. M. (1959) "Some Social Requisites of Democracy: Economic Development and Political Legitimacy", American Political Science Review 53, pp. 69-105.

Ragin, C. C. (2009) "Qualitative Comparative Analysis. Using Fuzzy Sets (fsQCA)." In Rihoux, B., and Ragin, C. C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques. Thousand Oaks, CA and London: Sage, pp. 87-121.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(LIPC)

Description

The LIPF data frame has 18 rows and 6 sets

Usage

data(LIPF)

Format

A data frame with 18 observations on the following 6 sets.

SURVIVED

a numeric vector. Outcome, survival of democracy during the inter-war period.

DEVELOPED

a numeric vector. Condition, economically developed countries.

URBAN

a numeric vector. Condition, urbanized countries.

LITERATE

a numeric vector. Condition, countries with high literacy rate.

INDUSTRIAL

a numeric vector. Condition, industrialized countries.

STABLE

a numeric vector. Condition, politically stable countries.

Details

Data used by Ragin (2009) to illustrates the variants of QCA. Originally by Lipset (1959). Data are fuzzy-sets.

References

Lipset, S. M. (1959) "Some Social Requisites of Democracy: Economic Development and Political Legitimacy", American Political Science Review 53, pp. 69-105.

Ragin, C. C. (2009) "Qualitative Comparative Analysis. Using Fuzzy Sets (fsQCA)." In Rihoux, B., and Ragin, C. C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques. Thousand Oaks, CA and London: Sage, pp. 87-121.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(LIPF)

Description

The LIPR data frame has 18 rows and 6 variables

Usage

data(LIPR)

Format

A data frame with 18 observations on the following 6 variables.

SURVIVED

a numeric vector. Outcome, survival of democracy during the inter-war period.

DEVELOPED

a numeric vector. Condition, level of economic development.

URBAN

a numeric vector. Condition, level of urbanization.

LITERATE

a numeric vector. Condition, level of literacy.

INDUSTRIAL

a numeric vector. Condition, level of industrialization.

UNSTABLE

a numeric vector. Condition, politically stable countries.

Details

Data used by Ragin (2009) to illustrates the variants of QCA. Originally by Lipset (1959). Data are raw-scores.

References

Lipset, S. M. (1959) "Some Social Requisites of Democracy: Economic Development and Political Legitimacy", American Political Science Review 53, pp. 69-105.

Ragin, C. C. (2009) "Qualitative Comparative Analysis. Using Fuzzy Sets (fsQCA)." In Rihoux, B., and Ragin, C. C. (eds.) Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques. Thousand Oaks, CA and London: Sage, pp. 87-121.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(LIPR)

Description

A function for identifying contradictory simplifying assumptions or easy counterfactuals by intersecting the SAs or ECs of two solutions.

Usage

LR.intersect(results1, results2, sol1 = 1, sol2 = 1)

Arguments

Author(s)

Ioana-Elena Oana

See Also

minimize

Examples

# Import your data. For example:

data(SCHF)

# Get the parsimonious solution:

sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)

# Get the parsimonious solution for the absence of the outcome:

sol_nyp <- minimize(SCHF, outcome = "EXPORT", neg.out = TRUE,
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)


# Get the contradictory simplofying assumptions:

LR.intersect(sol_yp, sol_nyp)

Description

Function that negates a boolean expression.

Usage

negateExp(expression)

Arguments

Value

It returns a negated boolean expression.

Author(s)

Ioana-Elena Oana

Examples

negateExp("~EMP*MA")
negateExp("~A*B + C*~D")

Description

The PAYF data frame has 131 rows and 9 sets. The data is calibrated into fuzzy sets.

Usage

data(PAYF)

Format

A data frame with 131 observations on the following 9 sets.

COUNTRY

Country

REGION

Region the country belongs to.

HE

Condition: healthy education system

GG

Condition: good governance

AH

Condition: affluent health system

HI

Condition: high income inequality

HW

Condition: high wealth

HL

Outcome: high life expectancy

LL

Negated Outcome: low life expectancy

References

Paykani, Toktam, Rafiey, Hassan, and Sajjadi, Homeira. 2018. A fuzzy set qualitative comparative analysis of 131 countries: which configuration of the structural conditions can explain health better? International journal for equity in health, 17(1), 10.

Examples

data(PAYF)

Description

The PAYR data frame has 131 rows and 9 variables. The data is raw, uncalibrated.

Usage

data(PAYR)

Format

A data frame with 131 observations on the following 9 sets.

COUNTRY

Country

REGION

Region the country belongs to.

LIFEX

Life expectancy, raw data.

EDUC

Education, raw data.

GOV

Governance, raw data.

HEAL

Health system, raw data.

INCEQ

Income inequality, raw data.

WEAL

Wealth, raw data.

References

Paykani, Toktam, Rafiey, Hassan, and Sajjadi, Homeira. 2018. A fuzzy set qualitative comparative analysis of 131 countries: which configuration of the structural conditions can explain health better? International journal for equity in health, 17(1), 10.

Examples

data(PAYR)

Description

The PENF data frame has 45 rows and 5 sets

Usage

data(PENF)

Format

A data frame with 45 observations on the following 5 sets.

K

a numeric vector. Outcome, constitutional control.

C

a numeric vector. Condition, consensus democracy.

P

a numeric vector. Condition, presidentialism.

N

a numeric vector. Condition, new democracy.

R

a numeric vector. Condition, rigid constritution.

Details

Data used by Pennings (2009) to explain constitutional control. Data are fuzzy-sets.

References

Pennings, P. (2009) "Beyond Dichotomous Explanations: Explaining Constitutional control of the Executive with Fuzzy-sets", European Journal of Political Research 42, pp. 541-567.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(PENF)

Description

A function that displays each case's set membership scores in each sufficient term, the solution formula, and the outcome from an object of class "qca".

Usage

pimdata(results, outcome, sol = 1, ...)

Arguments

Value

A table with set memberships.

Author(s)

Ioana-Elena Oana and Juraj Medzihorsky

See Also

minimize pimplot

Examples

# Import your data. For example:

data(SCHF)

# Get the parsimonious solution:


sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)

# Get the intermediate solution:

sol_yi <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))


# Get the prime implicants table for the parsimonious solution:

pimdata(results = sol_yp, outcome = "EXPORT")

# Get the prime implicants table for the first intermediate solution:

pimdata(results = sol_yi, outcome = "EXPORT", sol = 1)

Description

A function that displays XY plots for each sufficient term and the solution formula plotted against the outcome from an object of class "qca" (obtained by using the minimize function in package QCA). The function can also plot truth table rows against the outcome. Additionally, the function can plot results obtained from necessity analyses using an object of class "sS" (obtained by using the superSubset function in package QCA).

Usage

pimplot(data = NULL,
           results,
           outcome,
           incl.tt=NULL,
           ttrows= c(),
           necessity=FALSE,
           sol=1,
           all_labels=FALSE,
           markers = TRUE,
           labcol="black", 
           jitter = FALSE,
           font = "sans",
           fontface = "italic", 
           fontsize = 3,
           crisp = FALSE,
           consH = FALSE,
           ...)

Arguments

Value

XY plots.

Author(s)

Ioana-Elena Oana

References

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Schneider, C. Q., Rohlfing, I. 2013. Combining QCA and Process Tracing in Set-Theoretic Multi-Method Research. Sociological Methods Research 42(4): 559-597

See Also

minimize pimdata

Examples

# Import your data. For example:

data(SCHF)

# Get the parsimonious solution:


sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)

# Plot the prime implicants of the parsimonious solution:

pimplot(data = SCHF, results = sol_yp, outcome = "EXPORT")

# Plot a two-by-two table:

pimplot(data = SCHF, results = sol_yp, outcome = "EXPORT", crisp = TRUE)

# Plot all truth table rows with a consistency higher than 0.95:

pimplot(data=SCHF, results = sol_yp, incl.tt=0.97, outcome = "EXPORT", sol = 1)

# Plot truth table row "60":

pimplot(data=SCHF, results = sol_yp, ttrows =c("60"), 
        outcome = "EXPORT", sol = 1)

# For plotting results of necessity analyses using superSubset,
# the first stept is to obtain an "sS" object:

SUPSUB <- superSubset(SCHF, outcome="EXPORT", 
                      conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"), 
                      relation = "necessity", incl.cut = 0.996)
SUPSUB

# This can be imputed as result and necessity should be set to \code{TRUE}:

pimplot(data = SCHF, results = SUPSUB, outcome = "EXPORT", necessity = TRUE)

Description

A function for visualizing 3D property spaces as a 3D scatter plot using the scatterplot3d package.

Usage

property.cube(data, labs = FALSE, 
                          main = "3D Property Space", 
                          xlab=NULL, 
                          ylab=NULL, 
                          zlab=NULL,
                          highlight.3d=TRUE,
                          dot.cex=0.5,
                          dot.col="black",
                          dot.srt=15,
                          dot.pos=3,
                          dot.offset = 1)

Arguments

Value

It returns an enhanced 3d scatter plot using the scatterplot3d package.

Author(s)

Ioana-Elena Oana

Examples

# Load the Schneider data:

data(SCHF)

# Create a property space for conditions "EMP","BARGAIN", and outcome "EXPORT":

property.cube(SCHF[,c("EMP","BARGAIN","EXPORT")])

# Create a property space for conditions 1,2, and 3 in the data together with case labels:

property.cube(SCHF[,1:3], labs=TRUE)

Description

QCAfit is a function calculating parameters of fit useful in QCA and fsQCA that are consistency, coverage, PRI, Haesebrouck's consistency, RoN and PRODUCT. It works with both single and multiple conditions.

Usage

QCAfit(x, y, cond.lab = NULL, necessity = TRUE, neg.out = FALSE, 
              product = FALSE, sol=1, ttrows= c(), consH = FALSE)

Arguments

Value

It returns a matrix containing the parameters of fit for each condition.

Author(s)

Mario Quaranta and Ioana-Elena Oana

References

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Ragin, C. C. 2006. Set Relations in Social Research: Evaluating Their Consistency and Coverage. Political Analysis 14(3): 291-310.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

See Also

minimize

Examples

# Generate fake data
set.seed(1234)

a <- runif(100, 0, 1)
b <- runif(100, 0, 1)
c <- runif(100, 0, 1)
y <- runif(100, 0, 1)

# Only one condition, for necessity
QCAfit(a, y, cond.lab = "A")

# With three conditions and their negation, for necessity
QCAfit(cbind(a, b, c), y)

# Only one condition, for sufficiency
QCAfit(a, y, cond.lab = "A", necessity = FALSE)

# With three conditions, their negation and negated output, for necessity
QCAfit(cbind(a, b, c), y, neg.out = TRUE)

# Load the Schneider data:

data(SCHF)

# Get parameters of fit for condition EMP as necessary for outcome EXPORT:

QCAfit(SCHF$EMP, SCHF$EXPORT, cond.lab = "EMP")

# Get parameters of fit for condition ~EMP as necessary for outcome ~EXPORT:

QCAfit(1-SCHF$EMP, SCHF$EXPORT, neg.out=TRUE, cond.lab = "~EMP")

# Get parameters of fit for all conditions and their negation as necessary for outcome EXPORT:

QCAfit(SCHF[,1:6], SCHF$EXPORT)

# Obtain the parsimonious solution for outcome "EXPORT":

sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)
                
# Get parameters of fit for the parsimonious solution:

QCAfit(x = sol_yp, y = "EXPORT", necessity = FALSE)

# Get parameters of fit for truth table rows 2,8, and 10:

QCAfit(x = sol_yp, y = "EXPORT", ttrows=c("2","8","10"), necessity = FALSE)

Description

Function displays a radar chart for an object of class "qca" or for a boolean expression.

Usage

QCAradar(results, outcome= NULL, fit= FALSE, sol = 1)

Arguments

Author(s)

Ioana-Elena Oana

References

Maerz, F. Seraphine. 2017. "Pathways of Authoritarian Persistence." Paper presented at the CEU Annual Doctoral Conference

See Also

minimize

Examples

# Import data. 
# For example:

data(SCHF)

# Get the intermediate solution:

sol_yi <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))

# Display radar chart for the intermediate solution:

QCAradar(results = sol_yi, outcome = "EXPORT", fit=TRUE, sol = 1)

# Show a radar chart for the following boolean expression "A + ~B*Z*~C"

QCAradar(results = "A + ~B*Z*~C")

Description

Function for identifying the calibration threshold ranges for a conditio within which the Boolean formula for the solution does not change. The function gradually increases and, then, decreases each of the three (0, 0,5, 1) calibration thresholds of a condition by the value specifyied in the step argument and checks whether the solution formula changes. The function performs this iteration for the number of times specified in the max.runs argument. If the solution formula does not change given the number of runs specified, it will return an NA, meaning that it could not find a limit to the range.

Usage

rob.calibrange(raw.data,
    calib.data,
    test.cond.raw,
    test.cond.calib,
    test.thresholds,
    type = "fuzzy",
    step = 1,
    max.runs = 20,
    outcome,
    conditions,
    incl.cut = 1,
    n.cut = 1,
    include = "",
    ...)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the calibrated data:
data(LIPF)

# Load the raw data:
data(LIPR)

# Get calibration ranges for condition DEVELOPED:

rob.calibrange(
  raw.data = LIPR,
  calib.data = LIPF,
  test.cond.raw = "DEVELOPED",
  test.cond.calib = "DEVELOPED",
  test.thresholds = c(400, 550, 900),
  step = 500,
  max.runs = 2,
  outcome  = "SURVIVED",
  conditions = c("URBAN","LITERATE","INDUSTRIAL","STABLE"),
  incl.cut = 0.8,
  n.cut = 1,
  include = "?"
)

Description

Function for identifying cases between the various intersections between an inital solution and test solutions. The function also returns case ratio paramaters.

Usage

rob.cases(test_sol, 
           initial_sol, 
           outcome)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

## Not run: 
# Load the data:
data(PAYF)

# Store the name of the conditions in one vector:
conds <-  c("HE","GG","AH","HI","HW")

# Create several solutions:

# The initial solution
IS <- minimize(data = PAYF,
                outcome  = "HL",
                conditions = conds,
                incl.cut = 0.87,
                n.cut = 2,
                include = "?",
                details = TRUE, 
                show.cases = TRUE)

# altering consistency
TS1 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.7,
                 n.cut = 2,
                 include = "?",
                 details = TRUE, show.cases = TRUE)

#altering n.cut
TS2 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.87,
                 n.cut = 1,
                 include = "?",
                 details = TRUE, show.cases = TRUE)
                 
# Create the test set in a list:
TS <- list(TS1, TS2)

# Looking at cases against the test set:

rob.cases(test_sol = TS, 
                 initial_sol = IS, 
                 outcome = "HL")
## End(Not run)

Description

Function returning parameters of fit for the robust core between an initial solution and various alternative test solutions.

Usage

rob.corefit(test_sol, 
          initial_sol, 
          outcome)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the data:
data(PAYF)

# Store the name of the conditions in one vector:
conds <-  c("HE","GG","AH","HI","HW")

# Create several solutions:

# The initial solution
IS <- minimize(data = PAYF,
                outcome  = "HL",
                conditions = conds,
                incl.cut = 0.87,
                n.cut = 2,
                include = "?",
                details = TRUE, 
                show.cases = TRUE)

# altering consistency
TS1 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.7,
                 n.cut = 2,
                 include = "?",
                 details = TRUE, show.cases = TRUE)

#altering n.cut
TS2 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.87,
                 n.cut = 1,
                 include = "?",
                 details = TRUE, show.cases = TRUE)
                 
# Create the test set in a list:
TS <- list(TS1, TS2)

# Calculate robustness parameters, i.e. the ratio of the parameters
# of fit for the core vis-a-vis for the initial solution:

CF <- rob.corefit(test_sol = TS, 
           initial_sol = IS,  
           outcome = "HL")
CF

Description

Function returning robustness parameters of fit of the intersections between an initial sufficient solution and various alternative test solutions.

Usage

rob.fit(test_sol, 
              initial_sol, 
              outcome)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the data:
data(PAYF)

# Store the name of the conditions in one vector:
conds <-  c("HE","GG","AH","HI","HW")

# Create several solutions:

# The initial solution
IS <- minimize(data = PAYF,
                outcome  = "HL",
                conditions = conds,
                incl.cut = 0.87,
                n.cut = 2,
                include = "?",
                details = TRUE, 
                show.cases = TRUE)

# altering consistency
TS1 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.7,
                 n.cut = 2,
                 include = "?",
                 details = TRUE, show.cases = TRUE)

#altering n.cut
TS2 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.87,
                 n.cut = 1,
                 include = "?",
                 details = TRUE, show.cases = TRUE)
                 
# Create the test set in a list:
TS <- list(TS1, TS2)

# Calculate robustness parameters, i.e. the ratio of the parameters
# of fit for the core vis-a-vis for the initial solution:

RF <- rob.fit(test_sol = TS, 
           initial_sol = IS,  
           outcome = "HL")
RF

Description

Function for identifying the raw consistency threshold range for a truth table within which the Boolean formula for the solution does not change. The function gradually increases and, then, decreases an inital selected threshold by the value specifyied in the step argument and checks whether the solution formula changes for finding the lower and upper ranges for the raw consistency threshold. The function performs this iteration for the number of times specified in the max.runs argument. If the solution formula does not change given the number of runs specified, it will return an NA, meaning that it could not find a limit to the range.

Usage

rob.inclrange(data,
    step = 0.1,
    max.runs = 20,
    outcome,
    conditions,
    incl.cut = 1,
    n.cut = 1,
    include = "",
    ...)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the calibrated data:
data(PAYF)

# Check raw consistency ranges:

rob.inclrange(
  data = PAYF,
  step = 0.01,
  max.runs = 10,
  outcome  = "HL",
  conditions = c("HE","GG","AH","HI","HW"),
  incl.cut = 0.87,
  n.cut = 2,
  include = "?"
)

Description

Function for identifying the frequency threshold range for a truth table within which the Boolean formula for the solution does not change. The function gradually increases and, then, decreases an inital selected threshold by the value specifyied in the step argument and checks whether the solution formula changes for finding the lower and upper ranges for the frequency threshold. The function performs this iteration for the number of times specified in the max.runs argument. If the solution formula does not change given the number of runs specified, it will return an NA, meaning that it could not find a limit to the range.

Usage

rob.ncutrange(data,
    step = 1,
    max.runs = 20,
    outcome,
    conditions,
    incl.cut = 1,
    n.cut = 1,
    include = "",
    ...)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the calibrated data:
data(PAYF)

# Check frequency ranges:
rob.ncutrange(
  data = PAYF,
  step = 1,
  max.runs = 10,
  outcome  = "HL",
  conditions = c("HE","GG","AH","HI","HW"),
  incl.cut = 0.87,
  n.cut = 2,
  include = "?"
)

Description

Function returning robustness parameters for each single test solution in TS. The function returns the set-coincidence and RC_Rank between each alternative solution and the initial solution.

Usage

rob.singletest(test_sol, 
              initial_sol, 
              outcome)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2021. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the data:
data(PAYF)

# Store the name of the conditions in one vector:
conds <-  c("HE","GG","AH","HI","HW")

# Create several solutions:

# The initial solution
IS <- minimize(data = PAYF,
                outcome  = "HL",
                conditions = conds,
                incl.cut = 0.87,
                n.cut = 2,
                include = "?",
                details = TRUE, 
                show.cases = TRUE)

# altering consistency
TS1 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.7,
                 n.cut = 2,
                 include = "?",
                 details = TRUE, show.cases = TRUE)

#altering n.cut
TS2 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.87,
                 n.cut = 1,
                 include = "?",
                 details = TRUE, show.cases = TRUE)
                 
# Create the test set in a list:
TS <- list(TS1, TS2)

# Calculate robustness parameters, i.e. the ratio of the parameters
# of fit for the core vis-a-vis for the initial solution:

RST <- rob.singletest(test_sol = TS, 
           initial_sol = IS,  
           outcome = "HL")
RST

Description

Function for plotting an initial solution against the test set.

Usage

rob.xyplot(test_sol, 
            initial_sol, 
            outcome,
            all_labels = FALSE,
            jitter = TRUE,
            fontsize = 3,
            labs = TRUE,
            area_lab = TRUE)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, and Carsten Q. Schneider. 2020. Robustness tests in QCA: A fit-oriented and case-oriented perspective using R. Unpublished Manuscript.

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Load the data:
data(PAYF)

# Store the name of the conditions in one vector:
conds <-  c("HE","GG","AH","HI","HW")

# Create several solutions:

# The initial solution
IS <- minimize(data = PAYF,
                outcome  = "HL",
                conditions = conds,
                incl.cut = 0.87,
                n.cut = 2,
                include = "?",
                details = TRUE, 
                show.cases = TRUE)

# altering consistency
TS1 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.7,
                 n.cut = 2,
                 include = "?",
                 details = TRUE, show.cases = TRUE)

#altering n.cut
TS2 <- minimize(data = PAYF,
                 outcome  = "HL",
                 conditions = conds,
                 incl.cut = 0.87,
                 n.cut = 1,
                 include = "?",
                 details = TRUE, show.cases = TRUE)
                 
# Create the test set in a list:
TS <- list(TS1, TS2)

# Plotting the initial solution against the test set:

rob.xyplot(test_sol = TS, 
                  initial_sol = IS, 
                  outcome = "HL", 
                  fontsize = 2.5, 
                  jitter=TRUE)

Description

The SAMF data frame has 61 rows and 4 sets

Usage

data(SAMF)

Format

A data frame with 61 observations on the following 4 sets.

Y

a numeric vector. Outcome, trade liberalization.

G

a numeric vector. Condition, lack of weak growth.

H

a numeric vector. Condition, lack of hyper-inflation

HorG

a numeric vector. Condition, H or G.

Details

Data are used by Samford (2010) to analyze rapid trade liberalization in Latin America. Data are fuzzy-sets.

References

Samford, S. (2010) "Averting 'Disruption and Reversal': Reassessing the Logic of Rapid Trade Reform in Latin America", Politics and Policy 38(3), pp. 373-407.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(SAMF)

Description

The SC data frame has 130 rows and 4 sets

Usage

data(SC)

Format

A data frame with 130 observations on the following 4 sets.

A

a numeric vector. Condition, crisp-set.

B

a numeric vector. Condition, crisp-set.

C

a numeric vector. Condition, crisp-set.

Y

a numeric vector. Condition, crisp-set.

Details

The authors of the data are Carsten Schnieder and Claudius Wagemann. The data are used in the on-line appendix of "Set-Theoretic Methods for the Social Sciences" to practice the truth table algorithm. Data are crisp-sets.

References

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann

Examples

data(SC)

Description

The SCHF data frame has 76 observations and 7 sets

Usage

data(SCHF)

Format

A data frame with 76 observations on the following 7 sets.

EMP

a numeric vector. Condition, employment protection.

BARGAIN

a numeric vector. Condition, collective bargaining.

UNI

a numeric vector. Condition, university training.

OCCUP

a numeric vector. Condition, occupational training.

STOCK

a numeric vector. Condition, stock market size.

MA

a numeric vector. Condition, mergers and acquisitions.

EXPORT

a numeric vector. Outcome, export performance in high-tech industries.

Details

Data used by Schneider et. al. (2010) to explain capitalist variety and export performance in high-tech industries. Data are fuzzy-sets.

References

Schneider, M. R., Schulze-Bentrop, C., Paunescu, M. (2010) "Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance", Journal of International Business Studies 41, pp. 246-266.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(SCHF)

Description

The SCHLF data frame has 76 observations and 9 variables.

Usage

data(SCHLF)

Format

A data frame with 76 observations on the following 9 variables.

EMP

a numeric vector. Condition, employment protection.

BARGAIN

a numeric vector. Condition, collective bargaining.

UNI

a numeric vector. Condition, university training.

OCCUP

a numeric vector. Condition, occupational training.

STOCK

a numeric vector. Condition, stock market size.

MA

a numeric vector. Condition, mergers and acquisitions.

EXPORT

a numeric vector. Outcome, export performance in high-tech industries.

COUNTRY

a string vector. Name of the country in which the observation was made.

YEAR

a numeric vector. Year in which the observation was made.

Details

Data used by Schneider et. al. (2010) to explain capitalist variety and export performance in high-tech industries. Data is saved in the long format and the first 7 variables are fuzzy-sets.

References

Schneider, M. R., Schulze-Bentrop, C., Paunescu, M. (2010) "Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance", Journal of International Business Studies 41, pp. 246:266;

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(SCHLF)

Description

The SDC data frame has 98 rows and 4 sets

Usage

data(SDC)

Format

A data frame with 98 observations on the following 4 sets.

A

a numeric vector. Condition, crisp-set.

B

a numeric vector. Condition, crisp-set.

C

a numeric vector. Condition, crisp-set.

Y

a numeric vector. Outcome, crisp-set.

Details

The authors of the data are Carsten Schnieder and Claudius Wagemann. The data are used in the on-line appendix of "Set-Theoretic Methods for the Social Sciences" to show the disappearance of a necessary condition. Data are crisp-sets.

References

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann

Examples

data(SDC)

Description

A function that identifies how skewed sets are by returning the number and percentage of cases with higher than 0.5 fuzzy-set values. The function can also return histograms of the calibrated sets.

Usage

skew.check(data, hist =  FALSE, main = NULL)

Arguments

Author(s)

Ioana-Elena Oana

Examples

# Import your data. For example:

data(SCHF)

# Check skewness for the entire dataframe:

skew.check(SCHF)

# Check skewness for the column "EMP" in the dataframe:

skew.check(SCHF$EMP)

# Check skewness for the 5th column of the dataframe:

skew.check(SCHF[,5])

Description

A function that selects best available cases for single case studies and best pairs of matching cases for comparative case studies.

Usage

smmr(results, outcome, sol = 1, match = NULL, 
      cases = NULL, max_pairs = 5, term = 1, nec.cond =NULL, necessity = FALSE, ...)

Arguments

Author(s)

Ioana-Elena Oana

References

Schneider, C. Q., Rohlfing, I. 2013. Combining QCA and Process Tracing in Set-Theoretic Multi-Method Research. Sociological Methods and Research 42(4): 559-97

See Also

minimize

Examples

# Import your data. For example:

data(SCHF)

## SUFFICIENCY SMMR examples:

# Get the parsimonious solution:

sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)

# Get typical cases for each focal conjunct in the third term of the parsimonious solution:

smmr(results = sol_yp, outcome = "EXPORT", match=FALSE, cases=2, term = 3)

# Get typical cases for each focal conjunct in the third term of the parsimonious solution
# and specifying STOCK as a necessary condition:

smmr(results = sol_yp, outcome = "EXPORT", 
      match=FALSE, cases=2, term = 3, nec.cond="STOCK")

# Get matching typical-typical cases for the second term of the parsimonious solution:

smmr(results = sol_yp, outcome = "EXPORT", match=TRUE, cases=1, term = 2)

# Get matching typical-DCN cases:

smmr(results = sol_yp, outcome = "EXPORT", match=TRUE, cases=3)

## NECESSITY SMMR examples:

# Imagine you found condition "STOCK + MA" is nececssary for outcome "EXPORT".

# Get typical cases for each disjunct of the necessary condition:

smmr(results = sol_yp, outcome = "EXPORT", match=FALSE, cases=1, 
     nec.cond = "STOCK+MA", necessity = TRUE)

# Get typical - deviant relevance cases for each disjunct of the necessary condition:

smmr(results = sol_yp, outcome = "EXPORT", match=TRUE, cases=1, 
     nec.cond = "STOCK+MA", necessity = TRUE)

Description

Function for exporting a sufficienct solution from minimize in latex, html, or text format.

Usage

stargazerSol(results,
           outcome,
           sol = 1,
           show.cases = FALSE,
           type = "latex", 
           title = "",
           out = NULL,
           digits = 3)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Import your data. For example:

data(SCHF)

# Get the parsimonious solution:


sol_yp <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE)
                
# Print in latex format:

stargazerSol(sol_yp, "EXPORT")

Description

Function for exporting a sufficienct solution from minimize in latex, html, or text format.

Usage

stargazerTT(truthtable,
           show.cases = FALSE,
           type = "latex", 
           title = "",
           out = NULL,
           digits = 3)

Arguments

Author(s)

Ioana-Elena Oana

References

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

# Import your data. For example:

data(SCHF)

# Get the truth table:

mytt <- truthTable(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, complete = TRUE)

# Export as latex:
stargazerTT(mytt)

# Export as text:
stargazerTT(mytt, type = "text")

Description

Fake fuzzy-set football practice data used for Chapter 4 of the Oana et al. (forthcoming) book.

Usage

data(STUF)

Format

A data frame with 17 observations on the following 3 variables. Conditions for arriving in time for football practice.

A

Condition: Live close by

B

Condition: Arrive by bike

C

Condition: Come from punctual familie

D

Condition: Are seasoned players

E

Condition: Goalkeepers

Y

Outcome: Arrive on time for football practice

References

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

data(STUF)

Description

Fake raw, student data used for Chapter 2 of the Oana et al. (forthcoming) book.

Usage

data(STUR)

Format

A data frame with 17 observations on the following 3 variables.

MARK

Cond.: student mark

PARTICIPATION

Cond.: quality and correctness of the student's comments in class,

PEERS

Cond.: extent to which the student engages with and helps peers during interactive in-class teaching exercises

References

Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (forthcoming). Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge: Cambridge University Press.

Examples

data(STUR)

Description

Function that returns membership of cases in the intersections between theory and the empirical solution in the form of a data frame, the names of cases in the intersections between theory and the empirical solution, and the parameters of fit for these intersections.

Usage

theory.evaluation(theory, empirics, outcome, sol = 1, print.fit=FALSE, 
                  print.data=FALSE, consH = FALSE, ...)

Arguments

Author(s)

Ioana-Elena Oana

References

Ragin, C. C. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press, pp. 118-121

Schneider, C. Q., Wagemann, C. 2012. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 11.3

See Also

minimize

Examples

## Not run: 
# Import your data. For example:

data(SCHF)

# Get the intermediate solution:

sol_yi <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))


# Specify the theory. Let's assume the theory says that the 
# absence of EMP and the presence of MA is sufficient for EXPORT:

t<-"~EMP*MA"


# Perform theory evaluation (get only the names of the cases and the Boolean intersections):

TH <- theory.evaluation(theory = t, empirics = sol_yi, outcome = "EXPORT", sol = 1, 
                  print.data=FALSE, print.fit=FALSE)
TH

# Get only the case names:

TH$cases

# Or only the parameters of fit:

TH$fit
## End(Not run)

Description

The THOF data frame has 76 rows and 7 sets. The study analyses how European Union (EU) member states adapt EU directives to domestic contexts during transposition and asks how and why fully compliant countries 'customize' EU directives.

Usage

data(THOF)

Format

A data frame with 76 observations on the following 7 sets.

RESP

Cond.: Responsive regulatory mode

SAL

Cond.: Salient issue

RES

Cond.: Domestic resistance

VPO

Cond.: Many veto points

VPL

Cond.: Many veto players

COERC

Cond.: Coercive interventionist style

CUSTOM

Outcome: Extensive customization

Details

Data used by Thomann (2015) to explain customization. Data are fuzzy-sets.

References

Thomann, E. 2015. Customizing Europe: Transposition as bottom-up implementation. Journal of European Public Policy 22(10): 1368-1387.

Examples

data(THOF)

Description

The VISC data frame has 25 rows and 4 sets

Usage

data(VISC)

Format

A data frame with 25 observations on the following 4 sets.

P

a numeric vector. Condition, weak political positions, with parties in government expecting losses at the next election.

S

a numeric vector. Condition, deteriorating economic situation.

R

a numeric vector. Condition, government dominated by parties from the right of the political spectrum.

U

a numeric vector. Outcome, unpopular reform.

Details

Data are used by Vis (2009) to analyze the pursuit of unpopular reforms by governments. Data are crisp-sets.

References

Vis, B. (2009) "Government and Unpopular Social Policy Reforms: Biting the Bullet or Steering Clear?", European Journal of Political Research 48, pp. 31-57.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(VISC)

Description

The VISF data frame has 25 rows and 4 sets

Usage

data(VISF)

Format

A data frame with 25 observations on the following 4 sets.

p

a numeric vector. Condition, weak political positions, with parties in government expecting losses at the next election.

s

a numeric vector. Condition, deteriorating economic situation.

r

a numeric vector. Condition, government dominated by parties from the right of the political spectrum.

u

a numeric vector. Outcome, unpopular reform.

Details

Data are used by Vis (2009) to analyze the pursuit of unpopular reforms by governments. Data are fuzzy-sets.

References

Vis, B. (2009) "Government and Unpopular Social Policy Reforms: Biting the Bullet or Steering Clear?", European Journal of Political Research 48, pp. 31-57.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Examples

data(VISF)

Description

xy.plot produces XY plots and provides values for consistency, Haesebrouck's consistency, coverage, RoN, PRI. Several graphic parameters can be decided by the user.

Usage

xy.plot(x, y, data, 
           labcol = "black",
           main = "XY plot", 
           ylab = "Outcome", 
           xlab = "Condition",
           necessity = FALSE,
           jitter = FALSE, 
           font = "sans",
           fontface = "italic", 
           fontsize = 3,
           labs = rownames(data),
           crisp = FALSE,
           shape = 19,
           consH = FALSE,
           ...)

Arguments

Value

It returns an enhanced XY plot.

Author(s)

Mario Quaranta and Ioana-Elena Oana.

References

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Ragin, C. C. (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. The Chicago University Press: Chicago and London.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge Univeristy Press: Cambridge.

Examples

# Load the Schneider data:

data(SCHF)

# Plot of condition EMP as necessary for outcome EXPORT with case labels 
# and names for the plot and axes:

xy.plot("EMP", "EXPORT", data=SCHF, necessity = TRUE,
            main = "EMP as necessary for EXPORT", ylab = "EXPORT", xlab = "EMP")