Description Usage Arguments Details Value See Also Examples
Analyse a data vector response
with a specified RR model (e.g., Warner
) with known randomization probability p
1 
response 
either vector of responses containing 0='no' and 1='yes' or name of response variable in 
data 
optional 
model 
defines RR model. Available models: 
p 
randomization probability (see details or 
group 
a group vector of the same length as 
MLest 
whether to use 
Kukrep 
number of repetitions of Kuk's carddrawing method 
Each RR design model
differs in the definition of the randomization probability p
, which is defined as a single probability for
"Warner"
: Probabiltiy to get sensitive Question
"Mangat"
: Prob. for noncarriers to respond truthfully (i.e., with No=0)
"Crosswise"
: Probability to respond 'yes' to irrelevant second question (coding of responses: 1=['nono' or 'yesyes']; 0=['yesno' or 'noyes'])
"Triangular"
: Probability to respond 'yes' to irrelevant second question (coding of responses: 0='no' to both questions (='circle'); 1='yes' to at least one question ('triangle'))
and as a twovalued vector of probabilities for
"Kuk"
: Probability of red cards in first and second set, respectively (red=1, black=0);
Unrelated Question ("UQTknown"
): Prob. to respond to sensitive question and known prevalence of 'yes' responses to unrelated question
Unrelated Question ("UQTunknown"
): Prob. to respond to sensitive question in group 1 and 2, respectively
Cheating Detection ("CDM"
): Prob. to be prompted to say yes in group 1 and 2, respectively
Symmetric CDM ("CDMsym"
): 4valued vector: Prob. to be prompted to say 'yes'/'no' in group 1 and 'yes'/'no' in group 2
Stochastic Lie Detector ("SLD"
): Prob. for noncarriers to reply with 0='no' in group 1 and 2, respectively
Forced Response model ("FR"
): mvalued vector (m=number of response categories) with the probabilities of being prompted to select response categories 0,1,..,m1, respectively (requires sum(p)<1
)
RR as misclassification ("custom"
): a quadratic misclassification matrix is specified, where the entry p[i,j]
defines the probability of responding i (ith row) given a true state of j (jth column)) (see getPW
)
For the continuous RR models:
"mix.norm"
: 3valued vector  Prob. to respond to sensitive question and mean and SD of the masking normal distribution of the unrelated question
"mix.exp"
: 2valued vector  Prob. to respond to sensitive question and mean of the masking exponential distribution of the unrelated question
"mix.unknown"
: 2valued vector  Prob. of responding to sensitive question in group 1 and 2, respectively
an RRuni
object, can by analyzed by using summary
vignette('RRreg')
or https://www.dwheck.de/separate_content/RRregManual/index.html for a detailed description of the RR models and the appropriate definition of p
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # Generate responses of 1000 people according to Warner's model
# with an underlying true proportion of .3
genData < RRgen(n=1000, pi=.3, model="Warner", p=.7)
# Analyse univariate data to estimate 'pi'
analyse < RRuni(response=genData$response, model="Warner", p=.7)
summary(analyse)
# Generate data in line with the Stochastic Lie Detector
# assuming that 90% of the respondents answer truthfully
genData2 < RRgen(n=1000, pi=.3, model="SLD", p=c(.2,.8),
complyRates=c(.8,1),groupRatio=0.4)
analyse2 < RRuni(response=genData2$response, model="SLD",
p=c(.2,.8), group=genData2$group)
summary(analyse2)

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