sim.Kernelheaping: Simulation of heaping correction method

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

Simulation of heaping correction method

Usage

sim.Kernelheaping(
  simRuns,
  n,
  distribution,
  rounds,
  thresholds,
  downbias = 0.5,
  setBias = FALSE,
  Beta = 0,
  unequal = FALSE,
  burnin = 5,
  samples = 10,
  bw = "nrd0",
  offset = 0,
  boundary = FALSE,
  adjust = 1,
  ...
)

Arguments

simRuns	number of simulations runs
n	sample size
distribution	name of the distribution where random sampling is available, e.g. "norm"
rounds	rounding values, numeric vector of length >=1
thresholds	rounding thresholds
downbias	Bias parameter used in the simulation
setBias	if TRUE a rounding Bias parameter is estimated. For values above 0.5, the respondents are more prone to round down, while for values < 0.5 they are more likely to round up
Beta	Parameter of the probit model for rounding probabilities used in simulation
unequal	if TRUE a probit model is fitted for the rounding probabilities with log(true value) as regressor
burnin	burn-in sample size
samples	sampling iteration size
bw	bandwidth selector method, defaults to "nrd0" see density for more options
offset	location shift parameter used simulation in simulation
boundary	TRUE for positive only data (no positive density for negative values)
adjust	as in density, the user can multiply the bandwidth by a certain factor such that bw=adjust*bw
...	additional attributes handed over to createSim.Kernelheaping

Value

List of estimation results

Examples

## Not run: Sims1 <- sim.Kernelheaping(simRuns=2, n=500, distribution="norm",
rounds=c(1,10,100), thresholds=c(0.3,0.4,0.3), sd=100)
## End(Not run)

Kernelheaping documentation built on Jan. 27, 2022, 1:09 a.m.