Predicted Probabilities for Randomized Response as a Regression Predictor

predict.rrreg.predictor is used to generate predicted probabilities from a multivariate regression object of survey data using the randomized response item as a predictor for an additional outcome.

predict.rrreg.predictor(object, fix.z = NULL, alpha = .05,
n.sims = 1000, avg = FALSE, newdata = NULL, quasi.bayes = FALSE, keep.draws
= FALSE, ...)

Arguments

object	An object of class "rrreg.predictor" generated by the `rrreg.predictor()` function.
fix.z	An optional value or vector of values between 0 and 1 that the user inputs as the proportion of respondents with the sensitive trait or probability that each respondent has the sensitive trait, respectively. If the user inputs a vector of values, the vector must be the length of the data from the "rrreg.predictor" object. Default is `NULL` in which case predicted probabilities are generated for the randomized response item.
alpha	Confidence level for the hypothesis test to generate upper and lower confidence intervals. Default is `.05`.
n.sims	Number of sampled draws for quasi-bayesian predicted probability estimation. Default is `1000`.
avg	Whether to output the mean of the predicted probabilities and uncertainty estimates. Default is `FALSE`.
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 `FALSE` meaning no uncertainty estimates are outputted.
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.predictor()` command.

Value

predict.rrreg.predictor 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:

est

Predicted probabilities of the additional outcome variable given the randomized response item as a predictor generated either using fitted values or quasi-Bayesian simulations. If avg is set to TRUE, the output will only include the mean estimate.

Standard errors for the predicted probabilities of the additional outcome variable given the randomized response item as a predictor generated using Monte Carlo simulations. If quasi.bayes is set to FALSE, no standard errors will be outputted.

ci.lower

Estimates for the lower confidence interval. If quasi.bayes is set to FALSE, no confidence interval estimate will be outputted.

ci.upper

Estimates for the upper confidence interval. If quasi.bayes is set to FALSE, no confidence interval estimate will be outputted.

qoi.draws

Monte Carlos draws of the quantity of interest, returned only if keep.draws is set to TRUE.

Details

This function allows users to generate predicted probabilities for the additional outcome variables with the randomized response item as a covariate given an object of class "rrreg.predictor" from the rrreg.predictor() 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.predictor" object, is then directly inputted into this function.

References

Blair, Graeme, Kosuke Imai and Yang-Yang Zhou. (2014) "Design and Analysis of the Randomized Response Technique." Working Paper. Available at http://imai.princeton.edu/research/randresp.html.

Examples


# NOT RUN {
data(nigeria)

## Define design parameters

set.seed(44)

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 joint model of responses to an outcome regression of joining a civic 
## group and the randomized response item of having a militant social connection

rr.q1.pred.obj <-
    rrreg.predictor(civic ~ cov.asset.index + cov.married + I(cov.age/10) +
              I((cov.age/10)^2) + cov.education + cov.female
              + rr.q1, rr.item = "rr.q1", parstart = FALSE, estconv = TRUE,
              data = nigeria, verbose = FALSE, optim = TRUE,
              p = p, p1 = p1, p0 = p0, design = "forced-known")

## Generate predicted probabilities for the likelihood of joining 
## a civic group across respondents using quasi-Bayesian simulations. 

rr.q1.rrreg.predictor.pred <- predict(rr.q1.pred.obj,
                                 avg = TRUE, quasi.bayes = TRUE,
                                 n.sims = 10000)

# }