# indirectCalibration: Function performing the indirect calibration In SetMethods: Functions for Set-Theoretic Multi-Method Research and Advanced QCA

## 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

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

## Arguments

 `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 `TRUE`, which means that binomial regression is used.

## Value

It returns a vector of indirectly calibrated values.

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.

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

 ``` 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") ```

SetMethods documentation built on May 29, 2017, 2:58 p.m.