powerplot: Power plots for multivariate RR methods

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

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
)

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.)

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

Examples

Run this code

# Not run
# pplot <- powerplot(100, n=c(150,250), cor=c(0,.3,.5),
#                   method="RRlog", pi=.6, model="Warner", p=.3)
# plot(pplot)

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