RRsimu  R Documentation 
Simulate and analyse bivariate data including either one RR variable (either correlation, logistic, or linear regression model) or two RR variables (only correlations). Useful for power analysis, parametric bootstraps or for testing the effects of noncompliance on the stability of estimates.
RRsimu( numRep, n, pi, model, p, cor = 0, b.log = 0, complyRates = c(1, 1), sysBias = c(0, 0), method = c("RRuni", "RRcor", "RRlog", "RRlin"), alpha = 0.05, groupRatio = 0.5, MLest = FALSE, getPower = TRUE, nCPU = 1 )
numRep 
number of replications 
n 
sample size 
pi 
true proportion of carriers of sensitive attribute (for 2 RR
variables: 
model 
either one or two RR model (as 
p 
randomization probability (for 2 RR variables: a 
cor 
true Pearsoncorrelation used for data generation (for

b.log 
true regression coefficient in logistic regression (for

complyRates 
vector with two values giving the proportions of
participants who adhere to the instructions in the subset with or without
the sensitive attribute, respectively (for 2 RR variables: a 
sysBias 
probability of responding 'yes' (coded as 1 in the RR
variable) in case of noncompliance for carriers and noncarriers,
respectively. See 
method 
vector specifying which RR methods to be used in each
replication. For a single RR variable, the methods 
alpha 
significance threshold for testing the logistic regression
parameter 
groupRatio 
proportion of participants in group 1. Only for twogroup
models (e.g., 
MLest 
concerns 
getPower 
whether to compute power for 
nCPU 
either the number of CPU cores or a cluster initialized via

For a single RR variable:
The parameter b.log
is the slopecoefficient for the true, latent
values in a logistic regression model that is used for data generation.
The argument cor
is used for data generation for linear models. The
directly measured covariate is sampled from a normal distribution with
shifted means, depending on the true state on the sensitive attribute
(i.e., the true, underlying values on the RR variable). For dichotomous RR
variables, this corresponds to the assumption of an ordinary ttest, where
the dependent variable is normally distributed within groups with equal
variance. The difference in means is chosen in a way, to obtain the
pointbiserial correlation defined by cor
.
For two RR variables:
cor
has to be used. In case of two dichotomous RR variables, the
true group membership of individuals is sampled from a 2x2 cross table.
Within this table, probabilities are chosen in a way, to obtain the
pointtetrachoric correlation defined by cor
Note, that for the FR model with multiple response categories (e.g., from 0
to 4), the specified cor
is not the exact target of the sampling
procedure. It assumes a normal distribution for each true state, with
constant differences between the groups (i.e., it assumes an interval
scaled variable).
A list containing
parEsts 
matrix containing the estimated parameters 
results 
matrix with mean parameters, standard errors, and number of samples to which the respective method could not be fitted 
power 
vector with the estimated power of the selected randomized response procedures 
# Not run: Simulate data according to the Warner model # mcsim < RRsimu(numRep=100, n=300, pi=.4, # model="Warner", p=.2, cor=.3) # print(mcsim)
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