Power Analysis Plot for Randomized Response
Description
power.rr.plot
generates a power analysis plot for randomized response
survey designs.
Usage
1 2 3 4 5  power.rr.plot(p, p0, p1, q, design, n.seq, r, presp.seq, presp.null =
NULL, sig.level, prespT.seq, prespC.seq, prespT.null = NULL, prespC.null,
type = c("one.sample", "two.sample"), alternative = c("one.sided",
"two.sided"), solve.tolerance = .Machine$double.eps, legend = TRUE, legend.x
= "bottomright", legend.y, par = TRUE, ...)

Arguments
p 
The probability of receiving the sensitive question (Mirrored Question Design, Unrelated Question Design); the probability of answering truthfully (Forced Response Design); the probability of selecting a red card from the 'yes' stack (Disguised Response Design). 
p0 
The probability of forced 'no' (Forced Response Design). 
p1 
The probability of forced 'yes' (Forced Response Design). 
q 
The probability of answering 'yes' to the unrelated question, which is assumed to be independent of covariates (Unrelated Question Design). 
design 
Call of design (including modified designs) used: "forcedknown", "mirrored", "disguised", "unrelatedknown", "forcedunknown", and "unrelatedunknown". 
n.seq 
A sequence of number of observations or sample sizes. 
r 
For the modified designs only (i.e. "forcedunknown" for Forced
Response with Unknown Probability and "unrelatedunknown" for Unrelated
Question with Unknown Probability), 
presp.seq 
For a one sample test, a sequence of probabilities of possessing the sensitive trait under the alternative hypothesis. 
presp.null 
For a one sample test, the probability of possessing the
sensitive trait under the null hypothesis. The default is 
sig.level 
Significance level (Type I error probability). 
prespT.seq 
For a two sample test, a sequence of probabilities of the treated group possessing the sensitive trait under the alternative hypothesis. 
prespC.seq 
For a two sample test, a sequence of probabitilies of the control group possessing the sensitive trait under the alternative hypothesis. 
prespT.null 
For a two sample test, the probability of the treated
group possessing the sensitive trait under the null hypothesis. The default
is 
prespC.null 
For a two sample test, the probability of the control group possessing the sensitive trait under the null hypothesis. 
type 
One or two sample test. For a two sample test, the alternative and null hypotheses refer to the difference between the two samples of the probabilities of possessing the sensitive trait. 
alternative 
One or two sided test. 
solve.tolerance 
When standard errors are calculated, this option specifies the tolerance of the matrix inversion operation solve. 
legend 
Indicator of whether to include a legend of sample sizes. The
default is 
legend.x 
Placement on the xaxis of the legend. The default is

legend.y 
Placement on the yaxis of the legend. 
par 
Option to set or query graphical parameters within the function.
The default is 
... 
Additional arguments to be passed to 
Details
This function generates a power analysis plot for randomized response survey designs, both for the standard designs ("forcedknown", "mirrored", "disguised", "unrelatedknown") and modified designs ("forcedunknown", and "unrelated unknown"). The xaxis shows the population proportions with the sensitive trait; the yaxis shows the statistical power; and different sample sizes are shown as different lines in grayscale.
References
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.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Generate a power plot for the forced design with known
## probabilities of 2/3 in truthtelling group, 1/6 forced to say "yes"
## and 1/6 forced to say "no", varying the number of respondents from
## 250 to 2500 and the population proportion of respondents
## possessing the sensitive trait from 0 to .15.
presp.seq < seq(from = 0, to = .15, by = .0025)
n.seq < c(250, 500, 1000, 2000, 2500)
power.rr.plot(p = 2/3, p1 = 1/6, p0 = 1/6, n.seq = n.seq,
presp.seq = presp.seq, presp.null = 0,
design = "forcedknown", sig.level = .01,
type = "one.sample",
alternative = "one.sided", legend = TRUE)
## Replicates the results for Figure 2 in Blair, Imai, and Zhou (2014)
