# powerplot: Power plots for multivariate RR methods In danheck/RRreg: Correlation and Regression Analyses for Randomized Response Data

## Description

Uses the function `RRsimu` to estimate the power of the multivariate RR methods (correlation `RRcor`, logistic regression `RRlog`, and/or linear regression `RRlin`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```powerplot( numRep, n = c(100, 500, 1000), pi, cor = c(0, 0.1, 0.3), b.log = NULL, model, p, method = c("RRcor", "RRlog", "RRlin"), complyRates = c(1, 1), sysBias = c(0, 0), groupRatio = 0.5, alpha = 0.05, nCPU = 1, show.messages = TRUE ) ```

## Arguments

 `numRep` number of boostrap replications `n` vector of samples sizes `pi` true prevalence `cor` vector of true correlations `b.log` vector of true logistic regression coefficients `model` randomized response model `p` randomization probability `method` multivariate RR method `complyRates` probability of compliance within carriers/noncarriers of sensitive attribute `sysBias` probability of responding 'yes' in case of noncompliance `groupRatio` ratio of subgroups in two-group RR designs `alpha` type-I error used to estimate power `nCPU` either the number of CPU cores or a cluster initialized via `makeCluster`. `show.messages` toggle printing of progress messages

## Value

a list of the class `powerplot` containing an array `res` with the power estimates and details of the simulation (e.g., model, p, pi, etc.)

`RRsimu` for Monte-Carlo simulation / parametric bootstrap
 ```1 2 3 4``` ```# Not run # pplot <- powerplot(100, n=c(150,250), cor=c(0,.3,.5), # method="RRlog", pi=.6, model="Warner", p=.3) # plot(pplot) ```