RRlog  R Documentation 
A dichotomous variable, measured once or more per person by a randomized response method, serves as dependent variable using one or more continuous and/or categorical predictors.
RRlog( formula, data, model, p, group, n.response = 1, LR.test = TRUE, fit.n = 3, EM.max = 1000, optim.max = 500, ... )
formula 
specifying the regression model, see 
data 

model 
Available RR models: 
p 
randomization probability/probabilities (depending on model, see

group 
vector specifying group membership. Can be omitted for singlegroup RR designs (e.g., Warner). For twogroup RR designs (e.g., 
n.response 
number of responses per participant, e.g., if a participant
responds to 5 RR questions with the same randomization probability 
LR.test 
test regression coefficients by a likelihood ratio test, i.e.,
fitting the model repeatedly while excluding one parameter at a time (each
nested model is fitted only once, which can result in local maxima).
The likelihoodratio test statistic G^2(df=1) is reported in the table
of coefficiencts as 
fit.n 
Number of fitting replications using random starting values to avoid local maxima 
EM.max 
maximum number of iterations of the EM algorithm.
If 
optim.max 
Maximum number of iterations within each run of 
... 
ignored 
The logistic regression model is fitted first by an EM algorithm,
in which the dependend RR variable is treated as a misclassified binary variable
(Magder & Hughes, 1997). The results are used as starting values for a
NewtonRaphson based optimization by optim
.
Returns an object RRlog
which can be analysed by the generic
method summary
. In the table of coefficients, the column Wald
refers to the Chi^2 test statistic which is computed as Chi^2 = z^2 = Estimate^2/StdErr^2.
If LR.test = TRUE
, the test statistic deltaG2
is
the likelihoodratiotest statistic, which is computed by fitting a nested logistic
model without the corresponding predictor.
Daniel W. Heck
van den Hout, A., van der Heijden, P. G., & Gilchrist, R. (2007). The logistic regression model with response variables subject to randomized response. Computational Statistics & Data Analysis, 51, 60606069.
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 data set without biases dat < RRgen(1000,pi=.3,"Warner",p=.9) dat$covariate < rnorm(1000) dat$covariate[dat$true==1] < rnorm(sum(dat$true==1),.4,1) # analyse ana < RRlog(response~covariate,dat,"Warner", p=.9, fit.n = 1) summary(ana) # check with true, latent states: glm(true~covariate, dat, family=binomial(link="logit"))
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