Description Usage Arguments Details Value Author(s) References Examples

This function transforms (calibrates) the raw data to either crisp or fuzzy sets values, using both the direct and the indirect methods of calibration.

1 2 |

`x` |
A numerical causal condition. |

`type` |
Calibration type, either .`"fuzzy"` |

`method` |
Calibration method, either .`"indirect"` |

`thresholds` |
A vector of (named) thresholds. |

`logistic` |
Calibrate to fuzzy sets using the logistic function. |

`idm` |
The set inclusion degree of membership for the logistic function. |

`ecdf` |
Calibrate to fuzzy sets using the empirical cumulative distribution function of the raw data. |

`below` |
Numeric (non-negative), determines the shape below crossover. |

`above` |
Numeric (non-negative), determines the shape above crossover. |

`...` |
Additional parameters, mainly for backwards compatibility. |

Calibration is a transformational process from raw numerical data (interval or ratio level of measurement) to set membership scores, based on a certain number of qualitative anchors.

When ** type = "crisp"**, the process is similar to recoding the original
values to a number of categories defined by the number of thresholds. For one
threshold, the calibration produces two categories (intervals): 0 if below, 1 if above.
For two thresholds, the calibration produces three categories: 0 if below the first threshold,
1 if in the interval between the thresholds and 2 if above the second threshold etc.

When ** type = "fuzzy"**, calibration produces fuzzy set membership scores, using
three anchors for the increasing or decreasing s-shaped distributions (including
the logistic function), and six anchors for the increasing or decreasing bell-shaped
distributions.

The argument ** thresholds** can be specified either as a simple numeric vector, or as a
named numeric vector. If used as a named vector, for the first category of s-shaped
distributions, the names of the thresholds should be:

`"e"` | for the full set exclusion |

`"c"` | for the set crossover |

`"i"` | for the full set inclusion |

For the second category of bell-shaped distributions, the names of the thresholds
should be:

`"e1"` | for the first (left) threshold for full set exclusion |

`"c1"` | for the first (left) threshold for set crossover |

`"i1"` | for the first (left) threshold for full set inclusion |

`"i2"` | for the second (right) threshold for full set inclusion |

`"c2"` | for the second (right) threshold for set crossover |

`"e2"` | for the second (right) threshold for full set exclusion |

If used as a simple numerical vector, the order of the values matter.

If `e`

*<* `c`

*<* ** i**, then the membership
function is increasing from

`e`

`i`

`i`

`c`

`e`

`i`

`e`

Same for the bell-shaped distribution, if `e1`

*<* `c1`

*<* `i1`

*≤* `i2`

*<* `c2`

*<*
** e2**, then the membership function is first increasing from

`e1`

`i1`

`i1`

`i2`

`i2`

`e2`

`i1`

`c1`

`e1`

`e2`

`c2`

`i1`

`i1`

`e1`

`e1`

`e2`

`e2`

`i2`

When ** logistic = TRUE** (the default), the argument

`idm`

`logistic = FALSE`

`below`

`above`

`ecdf`

If there is no prior knowledge on the shape of the distribution, the argument ** ecdf**
asks the computer to determine the underlying distribution of the empirical, observed points,
and the calibrated measures are found along that distribution.

Both ** logistic** and

`ecdf`

The parameters ** below** and

`above`

`logistic`

`ecdf`

`0 < below < 1` | dilates in a concave shape below the crossover |

`below = 1` | produces a linear shape (neither convex, nor concave) |

`below > 1` | concentrates in a convex shape below the crossover |

`0 < above < 1` | dilates in a concave shape above the crossover |

`above = 1` | produces a linear shape (neither convex, nor concave) |

`above > 1` | concentrates in a convex shape above the crossover |

Usually, ** below** and

`above`

For the ** type = "fuzzy"** it is also possible to use the

`"indirect"`

The results are also fuzzy between 0 and 1, but the method is entirely different: it has no anchors (specific to the direct method), and it doesn't need to specify a calibration function to calculate the scores along with.

A numeric vector of set membership scores, either crisp (starting from 0 with increments of 1), or fuzzy numeric values between 0 and 1.

Adrian Dusa

Ragin, C. (2008) “Fuzzy Sets: Calibration Versus Measurement.”
In *The Oxford Handbook of Political Methodology*, edited by Janet
Box-Steffensmeier, Henry E. Brady, and David Collier, pp.87-121.
Oxford: Oxford University Press.

Thiem, A.; Dusa, A. (2013) *Qualitative Comparative Analysis with R:
A User's Guide*. New York: Springer.

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# generate heights for 100 people
# with an average of 175cm and a standard deviation of 10cm
set.seed(12345)
x <- rnorm(n = 100, mean = 175, sd = 10)
cx <- calibrate(x, type = "crisp", thresholds = 175)
plot(x, cx, main="Binary crisp set using 1 threshold",
xlab = "Raw data", ylab = "Calibrated data", yaxt="n")
axis(2, at = 0:1)
cx <- calibrate(x, type = "crisp", thresholds = c(170, 180))
plot(x, cx, main="3 value crisp set using 2 thresholds",
xlab = "Raw data", ylab = "Calibrated data", yaxt="n")
axis(2, at = 0:2)
# calibrate to a increasing, s-shaped fuzzy-set
cx <- calibrate(x, thresholds = "e=165, c=175, i=185")
plot(x, cx, main = "Membership scores in the set of tall people",
xlab = "Raw data", ylab = "Calibrated data")
# calibrate to an decreasing, s-shaped fuzzy-set
cx <- calibrate(x, thresholds = "i=165, c=175, e=185")
plot(x, cx, main = "Membership scores in the set of short people",
xlab = "Raw data", ylab = "Calibrated data")
# when not using the logistic function, linear increase
cx <- calibrate(x, thresholds = "e=165, c=175, i=185", logistic = FALSE)
plot(x, cx, main = "Membership scores in the set of tall people",
xlab = "Raw data", ylab = "Calibrated data")
# tweaking the parameters "below" and "above" the crossover,
# at value 3.5 approximates a logistic distribution, when e=155 and i=195
cx <- calibrate(x, thresholds = "e=155, c=175, i=195", logistic = FALSE,
below = 3.5, above = 3.5)
plot(x, cx, main = "Membership scores in the set of tall people",
xlab = "Raw data", ylab = "Calibrated data")
# calibrate to a bell-shaped fuzzy set
cx <- calibrate(x, thresholds = "e1=155, c1=165, i1=175, i2=175, c2=185, e2=195",
below = 3, above = 3)
plot(x, cx, main = "Membership scores in the set of average height",
xlab = "Raw data", ylab = "Calibrated data")
# calibrate to an inverse bell-shaped fuzzy set
cx <- calibrate(x, thresholds = "i1=155, c1=165, e1=175, e2=175, c2=185, i2=195",
below = 3, above = 3)
plot(x, cx, main = "Membership scores in the set of non-average height",
xlab = "Raw data", ylab = "Calibrated data")
# the default values of "below" and "above" will produce a triangular shape
cx <- calibrate(x, thresholds = "e1=155, c1=165, i1=175, i2=175, c2=185, e2=195")
plot(x, cx, main = "Membership scores in the set of average height",
xlab = "Raw data", ylab = "Calibrated data")
# different thresholds to produce a linear trapezoidal shape
cx <- calibrate(x, thresholds = "e1=155, c1=165, i1=172, i2=179, c2=187, e2=195")
plot(x, cx, main = "Membership scores in the set of average height",
xlab = "Raw data", ylab = "Calibrated data")
# larger values of above and below will increase membership in or out of the set
cx <- calibrate(x, thresholds = "e1=155, c1=165, i1=175, i2=175, c2=185, e2=195",
below = 10, above = 10)
plot(x, cx, main = "Membership scores in the set of average height",
xlab = "Raw data", ylab = "Calibrated data")
# while extremely large values will produce virtually crisp results
cx <- calibrate(x, thresholds = "e1=155, c1=165, i1=175, i2=175, c2=185, e2=195",
below = 10000, above = 10000)
plot(x, cx, main = "Binary crisp scores in the set of average height",
xlab = "Raw data", ylab = "Calibrated data", yaxt="n")
axis(2, at = 0:1)
abline(v = c(165, 185), col = "red", lty = 2)
# check if crisp
round(cx, 0)
# using the empirical cumulative distribution function
# require manually setting logistic to FALSE
cx <- calibrate(x, thresholds = "e=155, c=175, i=195", logistic = FALSE,
ecdf = TRUE)
plot(x, cx, main = "Membership scores in the set of tall people",
xlab = "Raw data", ylab = "Calibrated data")
## the indirect method, per capita income data from Ragin (2008)
inc <- c(40110, 34400, 25200, 24920, 20060, 17090, 15320, 13680, 11720,
11290, 10940, 9800, 7470, 4670, 4100, 4070, 3740, 3690, 3590,
2980, 1000, 650, 450, 110)
cinc <- calibrate(inc, method = "indirect",
thresholds = "1000, 4000, 5000, 10000, 20000")
plot(inc, cinc, main = "Membership scores in the set of high income",
xlab = "Raw data", ylab = "Calibrated data")
``` |

QCA documentation built on April 1, 2018, 12:12 p.m.

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