Description Usage Arguments Details Value References See Also Examples
View source: R/MangatSinghSingh.R
Computes the randomized response estimation, its variance estimation and its confidence interval through the Mangat-Singh-Singh model. The function can also return the transformed variable. The Mangat-Singh-Singh model was proposed by Mangat, Singh and Singh in 1992.
1 |
z |
vector of the observed variable; its length is equal to n (the sample size) |
p |
proportion of marked cards with the sensitive attribute in the box |
alpha |
proportion of people with the innocuous attribute |
pi |
vector of the first-order inclusion probabilities |
type |
the estimator type: total or mean |
cl |
confidence level |
N |
size of the population. By default it is NULL |
pij |
matrix of the second-order inclusion probabilities. By default it is NULL |
In the Mangat-Singh-Singh scheme, a person labelled i, if sampled, is offered a box and told to answer "yes" if the person bears A. But if the person bears A^c then the person is to draw a card from the box with a proportion p(0<p< 1) of cards marked A and the rest marked B; if the person draws a card marked B he/she is told to say "yes" again if he/she actually bears B; in any other case, "no" is to be answered.
The transformed variable is r_i=\frac{z_i-(1-p)α}{1-(1-p)α} and the estimated variance is \widehat{V}_R(r_i)=r_i(r_i-1).
Point and confidence estimates of the sensitive characteristics using the Mangat-Singh-Singh model. The transformed variable is also reported, if required.
Mangat, N.S., Singh, R., Singh, S. (1992). An improved unrelated question randomized response strategy. Calcutta Statistical Association Bulletin, 42, 277-281.
1 2 3 4 5 6 | data(MangatSinghSinghData)
dat=with(MangatSinghSinghData,data.frame(z,Pi))
p=0.6
alpha=0.5
cl=0.95
MangatSinghSingh(dat$z,p,alpha,dat$Pi,"total",cl)
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