RRreg (version 0.1.3)

RRsimu: Monte Carlo simulation for one or two RR variables

Description

Simulate and analyse bivariate data including either one or two RR variables. Useful for power analysis, parametric bootstraps or for testing the effects of noncompliance on the stability of estimates.

Usage

RRsimu(numRep, n, pi, model, p, rho, complyRates = c(1, 1), sysBias = c(0,
  0), method = c("RRuni", "RRcor", "RRlog"), alpha = 0.05,
  groupRatio = 0.5, MLest = TRUE, nCPU = 1)

Arguments

numRep

number of replications

n

sample size

pi

true proportion of carriers of sensitive attribute (for 2 RR variables: vector)

model

either one or two RR model (as vector), see RRuni

p

randomization probability (for 2 RR variables: a list)

rho

true correlation in population (also known as point-biserial/point-tetrachoric correlation, in case of one/two dichotomous RR variables)

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 list)

sysBias

probability of responding 'yes' (coded as 1 in the RR variable) in case of non-compliance for carriers and non-carriers, respectively. See RRgen

method

vector specifying which RR methods to be used in each replication. For a single RR variable, all three methods can be used. For 2 RR variables, only RRcor is available.

alpha

significance threshold for testing the logistic regression parameter beta

groupRatio

only for multiple group models: ratio of groups (for 2 RR variables: vector)

MLest

correct estimates of RRuni if pi is outside of [0,1]

nCPU

integer: how many processors to use? (use 'max' for automatic detection on Windows)

Value

• an object RRsimu which contains the estimated parameters parEsts and a matrix results with mean parameters and standard errors across replications

Details

In case of using only one RR variable, the second, directly measured variable 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 t-test, where the dependent variable is normally distributed within groups with equal variance. The difference in means is chosen in a way, to obtain the point-biserial correlation defined by rho. 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 point-tetrachoric correlation defined by rho Note, that for the FR model with multiple response categories (e.g., from 0 to 4), the specified rho 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).

Examples

# Simulate data according to the Warner model
mcsim <-  RRsimu(numRep=5, n=200, pi=.3, model="Warner", p=2/12, rho=.6)
mcsim
plot(mcsim)