# power.rr.plot: Power Analysis Plot for Randomized Response In rr: Statistical Methods for the Randomized Response Technique

## 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: "forced-known", "mirrored", "disguised", "unrelated-known", "forced-unknown", and "unrelated-unknown". `n.seq` A sequence of number of observations or sample sizes. `r` For the modified designs only (i.e. "forced-unknown" for Forced Response with Unknown Probability and "unrelated-unknown" for Unrelated Question with Unknown Probability), `r` is the proportion of respondents allocated to the first group, which is the group that is directed to answer the sensitive question truthfully with probability `p` as opposed to the second group which is directed to answer the sensitive question truthfully with probability `1-p`. `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 `NULL` meaning zero probability of possessing the sensitive trait. `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 `NULL` meaning there is no difference between the treated and control groups, specifically that `prespT.null` is the same as `prespC.null`, the probability of the control group possessing the sensitive trait under the null hypothesis. `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 `TRUE`. `legend.x` Placement on the x-axis of the legend. The default is `"bottomright"`. `legend.y` Placement on the y-axis of the legend. `par` Option to set or query graphical parameters within the function. The default is `TRUE`. `...` Additional arguments to be passed to `par()`

## Details

This function generates a power analysis plot for randomized response survey designs, both for the standard designs ("forced-known", "mirrored", "disguised", "unrelated-known") and modified designs ("forced-unknown", and "unrelated -unknown"). The x-axis shows the population proportions with the sensitive trait; the y-axis shows the statistical power; and different sample sizes are shown as different lines in grayscale.

## 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

 ``` 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 truth-telling 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 = "forced-known", sig.level = .01, type = "one.sample", alternative = "one.sided", legend = TRUE) ## Replicates the results for Figure 2 in Blair, Imai, and Zhou (2014) ```

rr documentation built on May 29, 2017, 9:26 p.m.