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

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.

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

# 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")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(SetMethods)
Loading required package: lattice
Loading required package: betareg
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SetMethods/indirectCalibration.Rd_%03d_medium.png", width=480, height=480)
> ### Name: indirectCalibration
> ### Title: Function performing the indirect calibration
> ### Aliases: indirectCalibration
> 
> ### ** 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
 [1] 0.6 0.0 0.2 0.2 0.8 0.0 0.6 0.8 1.0 0.0 0.8 0.2 0.0 1.0 0.2 0.4 1.0 0.4 1.0
[20] 0.8
> 
> # 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)
[1] 0.9996942
> 
> # Plot
> plot(x, a); points(x, b, col = "red")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>