RRuni | R Documentation |
Analyse a data vector response
with a specified RR model (e.g., Warner
) with known randomization probability p
RRuni(response, data, model, p, group = NULL, MLest = TRUE, Kukrep = 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 card-drawing 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=['no-no' or 'yes-yes']; 0=['yes-no' or 'no-yes'])
"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 two-valued 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"
): 4-valued 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"
): m-valued vector (m=number of response categories) with the probabilities of being prompted to select response categories 0,1,..,m-1, 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 (i-th row) given a true state of j (j-th column)) (see getPW
)
For the continuous RR models:
"mix.norm"
: 3-valued vector - Prob. to respond to sensitive question and mean and SD of the masking normal distribution of the unrelated question
"mix.exp"
: 2-valued vector - Prob. to respond to sensitive question and mean of the masking exponential distribution of the unrelated question
"mix.unknown"
: 2-valued 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
# 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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