Description Usage Arguments Value Author(s) References See Also Examples

Linear regression for a continuous criterion, using randomized-response (RR) variables as predictors.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`formula` |
a continuous criterion is predicted by one or more categorical RR variables defined by |

`data` |
an optional data frame, list or environment, containing the variables in the model. |

`models` |
character vector specifying RR model(s) in order of appearance in formula. Available models: |

`p.list` |
list of randomization probabilities for RR models in the same order as specified in |

`group` |
vector or matrix specifying group membership by the indices 1 and 2. Only for multigroup RR models, e.g., |

`Kukrep` |
defines the number of repetitions in Kuk's card playing method |

`bs.n` |
Number of samples used for the non-parametric bootstrap |

`nCPU` |
only relevant for the bootstrap: either the number of CPU cores or a cluster initialized via |

`maxit` |
maximum number of iterations in optimization routine |

`fit.n` |
number of fitting runs with random starting values |

`pibeta` |
approximate ratio of probabilities pi to regression weights beta (to adjust scaling). Can be used for speeding-up and fine-tuning ML estimation (i.e., choosing a smaller value for larger beta values). |

Returns an object `RRlin`

which can be analysed by the generic method `summary`

Daniel W. Heck

van den Hout, A., & Kooiman, P. (2006). Estimating the linear regression model with categorical covariates subject to randomized response. *Computational Statistics & Data Analysis, 50*, 3311-3323.

`vignette('RRreg')`

or http://www.dwheck.de/separate_content/RRregManual/index.html for a detailed description of the RR models and the appropriate definition of `p`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# generate two RR predictors
dat <- RRgen(n=500, pi=.4, model="Warner", p=.3)
dat2 <- RRgen(n=500, pi=c(.4,.6), model="FR", p=c(.1,.15))
dat$FR <- dat2$response
dat$trueFR <- dat2$true
# generate a third predictor and continuous dependent variables
dat$nonRR <- rnorm(500, 5, 1)
dat$depvar <- 2*dat$true - 3*dat2$true +
.5*dat$nonRR +rnorm(500, 1, 7)
# use RRlin and compare to regression on non-RR variables
linreg <- RRlin(depvar~response+FR+nonRR, data=dat,
models=c("Warner","FR"),
p.list=list(.3, c(.1,.15)), fit.n=1)
summary(linreg)
summary(lm(depvar~true +trueFR+nonRR, data=dat))
``` |

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