indirectCalibration: Function performing the indirect calibration

In nenaoana/SetMethods: Functions for Set-Theoretic Multi-Method Research and Advanced QCA

Function performing the indirect calibration

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

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.

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")

nenaoana/SetMethods documentation built on April 7, 2023, 2:20 p.m.