predict.rrreg
is used to generate predicted probabilities from a
multivariate regression object of survey data using randomized response
methods.
# S3 method for rrreg predict(object, given.y = FALSE, alpha = .05, n.sims = 1000, avg = FALSE, newdata = NULL, quasi.bayes = FALSE, keep.draws = FALSE, ...)
object  An object of class "rrreg" generated by the 

given.y  Indicator of whether to use "y" the response vector to
calculate the posterior prediction of latent responses. Default is

alpha  Confidence level for the hypothesis test to generate upper and
lower confidence intervals. Default is 
n.sims  Number of sampled draws for quasibayesian predicted
probability estimation. Default is 
avg  Whether to output the mean of the predicted probabilities and
uncertainty estimates. Default is 
newdata  Optional new data frame of covariates provided by the user. Otherwise, the original data frame from the "rreg" object is used. 
quasi.bayes  Option to use Monte Carlo simulations to generate
uncertainty estimates for predicted probabilities. Default is 
keep.draws  Option to return the Monte Carlos draws of the quantity of interest, for use in calculating differences for example. 
...  Further arguments to be passed to 
predict.rrreg
returns predicted probabilities either for each
observation in the data frame or the average over all observations. The
output is a list that contains the following components:
Predicted probabilities for the randomized response item
generated either using fitted values, posterior predictions, or
quasiBayesian simulations. If avg
is set to TRUE
, the output
will only include the mean estimate.
Standard errors for the
predicted probabilities of the randomized response item generated using
Monte Carlo simulations. If quasi.bayes
is set to FALSE
, no
standard errors will be outputted.
Estimates for the lower
confidence interval. If quasi.bayes
is set to FALSE
, no
confidence interval estimate will be outputted.
Estimates
for the upper confidence interval. If quasi.bayes
is set to
FALSE
, no confidence interval estimate will be outputted.
Monte Carlos draws of the quantity of interest, returned
only if keep.draws
is set to TRUE
.
This function allows users to generate predicted probabilities for the
randomized response item given an object of class "rrreg" from the
rrreg()
function. Four standard designs are accepted by this
function: mirrored question, forced response, disguised response, and
unrelated question. The design, already specified in the "rrreg" object, is
then directly inputted into this function.
Blair, Graeme, Kosuke Imai and YangYang Zhou. (2014) "Design and Analysis of the Randomized Response Technique." Working Paper. Available at http://imai.princeton.edu/research/randresp.html.
rrreg
to conduct multivariate regression analyses in
order to generate predicted probabilities for the randomized response item.
# NOT RUN { data(nigeria) set.seed(1) ## Define design parameters p < 2/3 # probability of answering honestly in Forced Response Design p1 < 1/6 # probability of forced 'yes' p0 < 1/6 # probability of forced 'no' ## Fit linear regression on the randomized response item of ## whether citizen respondents had direct social contacts to armed groups rr.q1.reg.obj < rrreg(rr.q1 ~ cov.asset.index + cov.married + I(cov.age/10) + I((cov.age/10)^2) + cov.education + cov.female, data = nigeria, p = p, p1 = p1, p0 = p0, design = "forcedknown") ## Generate the mean predicted probability of having social contacts to ## armed groups across respondents using quasiBayesian simulations. rr.q1.reg.pred < predict(rr.q1.reg.obj, given.y = FALSE, avg = TRUE, quasi.bayes = TRUE, n.sims = 10000) ## Replicates Table 3 in Blair, Imai, and Zhou (2014) # }