Calibrate Raw Data into Configurational Data
This function generates configurational data from raw data (base variables) and some specified threshold(s). The calibration of bivalent fuzzy-set factors is possible for positive and negative end-point and mid-point concepts, using the method of transformational assignment.
calibrate(x, type = "crisp", thresholds = NA, include = TRUE,
logistic = FALSE, idm = 0.95, ecdf = FALSE, p = 1, q = 1)x |
An interval or ratio-scaled base variable. |
type |
The calibration type, either "crisp" or "fuzzy". |
thresholds |
A vector of thresholds. |
include |
Logical, include threshold(s) ( |
logistic |
Calibrate to fuzzy-set variable using the logistic function. |
idm |
The set inclusion degree of membership for the logistic function. |
ecdf |
Calibrate to fuzzy-set variable using the empirical cumulative distribution function of the base variable. |
p |
Parameter: if p > 1 concentration, if 0 < p < 1 dilation below crossover. |
q |
Parameter: if q > 1 dilation, if 0 < q < 1 concentration above crossover. |
Calibration is the process by which configurational data is produced, that is, by which set membership scores are assigned to cases. With interval and ratio-scaled base variables, calibration can be based on transformational assignments using (piecewise-defined) membership functions.
For type = "crisp", one threshold produces a factor with two levels: 0 and
1. More thresholds produce factors with multiple levels. For example, two thresholds
produce three levels: 0, 1 and 2.
For type = "fuzzy", this function can generate bivalent fuzzy-set variables
by linear, s-shaped, inverted s-shaped and logistic transformation for end-point
concepts. It can generate bivalent fuzzy-set variables by trapezoidal, triangular
and bell-shaped transformation for mid-point concepts (Bojadziev and Bojadziev
2007; Clark et al. 2008; Thiem 2014; Thiem and Dusa 2013).
For calibrating bivalent fuzzy-set variables based on end-point concepts,
thresholds should be specified as a numeric vector c(thEX, thCR, thIN),
where thEX is the threshold for full exclusion, thCR the threshold
for the crossover, and thIN the threshold for full inclusion.
If thEX < thCR < thIN, then the membership
function is increasing from thEX to thIN. If thIN <
thCR < thEX, then the membership function is decreasing from
thIN to thEX.
For calibrating bivalent fuzzy-set variables based on mid-point concepts,
thresholds should be specified as a numeric vector
c(thEX1, thCR1, thIN1, thIN2, thCR2, thEX2), where thEX1 is the first
(left) threshold for full exclusion, thCR1 the first (left) threshold for
the crossover, thIN1 the first (left) threshold for full inclusion,
thIN2 the second (right) threshold for full inclusion, thCR2 the
second (right) threshold for crossover, and thEX2 the second (right) threshold
for full exclusion.
If thEX1 < thCR1 < thIN1 ≤ thIN2
< thCR2 < thEX2, then the membership function is first
increasing from thEX1 to thIN1, then flat between thIN1 and
thIN2, and finally decreasing from thIN2 to thEX2. In contrast,
if thIN1 < thCR1 < thEX1 ≤ thEX2
< thCR2 < thIN2, then the membership function is first
decreasing from thIN1 to thEX1, then flat between thEX1 and
thEX2, and finally increasing from thEX2 to thIN2.
The parameters p and q control the degree of concentration and
dilation. They should be left at their default values unless good reasons for
changing them exist.
If logistic = TRUE, the argument idm specifies the inclusion degree
of membership.
If ecdf = TRUE, calibration is based on the empirical cumulative distribution
function of x.
A numeric vector of set membership scores between 0 and 1 for bivalent crisp-set factors and bivalent fuzzy-set variables, or a numeric vector of levels for multivalent crisp-set factors (beginning with 0 at increments of 1).
| Dusa, Adrian | : programming |
| Thiem, Alrik | : development, documentation, programming, testing |
Alrik Thiem (Personal Website; ResearchGate Website)
Bojadziev, George, and Maria Bojadziev. 2007. Fuzzy Logic for Business, Finance, and Management. 2nd ed. Hackensack, NJ: World Scientific. Link.
Clark, Terry D., Jennifer M. Larson, John N. Mordeson, Joshua D. Potter, and Mark J. Wierman. 2008. Applying Fuzzy Mathematics to Formal Models in Comparative Politics. Berlin: Springer. Link.
Thiem, Alrik. 2014. “Membership Function Sensitivity of Descriptive Statistics in Fuzzy-Set Relations.” International Journal of Social Research Methodology 17 (6):625-42. DOI: 10.1080/13645579.2013.806118.
Thiem, Alrik, and Adrian Dusa. 2013. Qualitative Comparative Analysis with R: A User's Guide. New York: Springer. Link.
# base variable; random draw from standard normal distribution
set.seed(30)
x <- rnorm(30)
# calibration thresholds
th <- quantile(x, seq(from = 0.05, to = 0.95, length = 6))
# calibration of bivalent crisp-set factor
calibrate(x, thresholds = th[3])
# calibration of trivalent crisp-set factor
calibrate(x, thresholds = c(th[2], th[4]))
# fuzzy-set calibration
# 1. positive end-point concept, linear
# 2. positive and corresponding negative end-point concept, logistic
# 3. positive end-point concept, ECDF
# 4. negative end-point concept, s-shaped (quadratic)
# 5. negative end-point concept, inverted s-shaped (root)
# 6. positive mid-point concept, triangular
# 7. positive mid-point concept, trapezoidal
# 8. negative mid-point concept, bell-shaped
yl <- "Set Membership"
xl <- "Base Variable Value"
par(mfrow = c(2,4), cex.main = 1)
plot(x, calibrate(x, type = "fuzzy", thresholds = c(th[1], (th[3]+th[4])/2,
th[6])), xlab = xl, ylab = yl,
main = "1. positive end-point concept,\nlinear")
plot(x, calibrate(x, type = "fuzzy", thresholds = c(th[1], (th[3]+th[4])/2,
th[6]), logistic = TRUE, idm = 0.99), xlab = xl, ylab = yl,
main = "2. positive and corresponding negative\nend-point concept, logistic")
points(x, calibrate(x, type = "fuzzy", thresholds = c(th[6], (th[3]+th[4])/2,
th[1]), logistic = TRUE, idm = 0.99))
plot(x, calibrate(x, type = "fuzzy", thresholds = c(th[1], (th[3]+th[4])/2,
th[6]), ecdf = TRUE), xlab = xl, ylab = yl,
main = "3. positive end-point concept,\nECDF")
plot(x, calibrate(x, type = "fuzzy", thresholds = c(th[6], (th[3]+th[4])/2,
th[1]), p = 2, q = 2), xlab = xl, ylab = yl,
main = "4. negative end-point concept,\ns-shaped (quadratic)")
plot(x, calibrate(x, type = "fuzzy", thresholds = c(th[6], (th[3]+th[4])/2,
th[1]), p = 0.5, q = 0.5), xlab = xl, ylab = yl,
main = "5. negative end-point concept,\ninverted s-shaped (root)")
plot(x, calibrate(x, type = "fuzzy", thresholds = th[c(1,2,3,3,4,5)]),
xlab = xl, ylab = yl, main = "6. positive mid-point concept,\ntriangular")
plot(x, calibrate(x, type = "fuzzy", thresholds = th[c(1,2,3,4,5,6)]),
xlab = xl, ylab = yl, main = "7. positive mid-point concept,\ntrapezoidal")
plot(x, calibrate(x, type = "fuzzy", thresholds = th[c(3,2,1,5,4,3)],
p = 3, q = 3), xlab = xl, ylab = yl,
main = "8. negative mid-point concept,\nbell-shaped")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.