RRcor  R Documentation 
RRcor
calculates bivariate Pearson correlations of variables measured
with or without RR.
RRcor( x, y = NULL, models, p.list, group = NULL, bs.n = 0, bs.type = c("se.n", "se.p", "pval"), nCPU = 1 )
x 
a numeric vector, matrix or data frame. 
y 

models 
a vector defining which RR design is used for each variable.
Must be in the same order as variables appear in 
p.list 
a 
group 
a matrix defining the group membership of each participant
(values 1 and 2) for all multiple group models( 
bs.n 
number of samples used to get bootstrapped standard errors 
bs.type 
to get boostrapped standard errors, use 
nCPU 
only relevant for the bootstrap: either the number of CPU cores
or a cluster initialized via 
Correlations of RR variables are calculated by the method of Fox & Tracy (1984) by interpreting the variance induced by the RR procedure as uncorrelated measurement error. Since the error is independent, the correlation can be corrected to obtain an unbiased estimator.
Note that the continuous RR model mix.norm
with the randomization
parameter p=c(p.truth, mean, SD)
assumes that participants respond
either to the sensitive question with probability p.truth
or otherwise
to a known masking distribution with known mean and SD. The estimated
correlation only depends on the mean and SD and does not require normality.
However, the assumption of normality is used in the parametric bootstrap to
obtain standard errors.
RRcor
returns a list with the following components::
r
estimated correlation matrix
rSE.p
, rSE.n
standard errors from parametric/nonparametric
bootstrap
prob
twosided pvalues from parametric bootstrap
samples.p
, samples.n
sampled correlations from
parametric/nonparametric bootstrap (for the standard errors)
Fox, J. A., & Tracy, P. E. (1984). Measuring associations with randomized response. Social Science Research, 13, 188197.
vignette('RRreg')
or
https://www.dwheck.de/vignettes/RRreg.html for a
detailed description of the RR models and the appropriate definition of
p
# generate first RR variable n < 1000 p1 < c(.3, .7) gData < RRgen(n, pi = .3, model = "Kuk", p1) # generate second RR variable p2 < c(.8, .5) t2 < rbinom(n = n, size = 1, prob = (gData$true + 1) / 2) temp < RRgen(model = "UQTknown", p = p2, trueState = t2) gData$UQTresp < temp$response gData$UQTtrue < temp$true # generate continuous covariate gData$cov < rnorm(n, 0, 4) + gData$UQTtrue + gData$true # estimate correlations using directly measured / RR variables cor(gData[, c("true", "cov", "UQTtrue")]) RRcor( x = gData[, c("response", "cov", "UQTresp")], models = c("Kuk", "d", "UQTknown"), p.list = list(p1, p2) )
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