genip.montecarlo: Generate Simulated Responses of Respondents with Ideal Points
In gentok/ipbridging: Bridging Ideal Point Estimates

Description Usage Arguments Value Author(s)

Generate simulated responses of survey respondents with ideal points through Monte Carlo method.

genip.montecarlo(
  n = 1200,
  q = 20,
  ncat = 5,
  ndim = 2,
  missing = 0.1,
  correlations.lim = c(-0.1, 0.7),
  utility.probs = c(0.33, 0.33, 0.33),
  error.respondents = c(0.1, 1),
  error.issues = c(2, 1),
  idealpoints.lim = c(-2, 2)
)

`n`	Number of respondents.
`q`	Number of issues.
`ncat`	Number of ordered category in responses.
`ndim`	Number of dimentions in ideal points.
`missing`	Proportion of missing responses. Ranges from 0-1.
`correlations.lim`	Limits in the range of correlation between respondents and issues. Higher value indicates stronger association between respondent's values and their ideal points in the issue.
`utility.probs`	Utility function is selected randomly from three options – "linear","normal","quadratic". This argument defines sampling weights for utility functions.
`error.respondents`	The lower and upper limits of uniform distribution that are used to initiate values of respondents.
`error.issues`	The `shape` and `rate` parameter in `rgamma` distribution, respectively. Used to set initial values of issue positions.
`idealpoints.lim`	Limits in the range of ideal points. The values outside of this range is recoded into minimum and maximum value of the range.

A list with the following elements

simulated.responses: n*q data.frame of simulated responses.
perfect.responses: Hypothetical responses if each respondents give responses without error.
idealpoints: Ideal point coordinates of each respondent.
normalvectors: Normal vectors.
heteroskedastic.respondents: Initial values assigned to respondents.
heteroskedastic.issues: Initial values assigned to issues.
correlations: Correlation between ideal point dimensions.
knowledge: Correlation binned into three categories.
error: Proportions of incorrect choices.