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`

.

1 | ```
indirectCalibration(x, x_cal, binom = TRUE)
``` |

`x` |
vector of raw scores. |

`x_cal` |
vector of theoretically calibrated scores. |

`binom` |
logical. If indirect calibration has to be performed using binomial regression or beta regression. The default is |

It returns a vector of indirectly calibrated values.

Mario Quaranta

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.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
# 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")
``` |

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