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
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