R: Generate Random Sample from a DC Model

sim.bin

R Documentation

Generate Random Sample from a DC Model

This function generates a random sample from an discrete choice regression model.

sim.bin(
  coef = 2L,
  nObs = 100,
  probit = FALSE,
  maxWeight = 1,
  pPos = 0.5,
  sampleFactor = 4,
  toNumeric = TRUE
)

`coef`	Either a single integer specifying the number of variables in the model, or a numeric vector of coefficients for the regression.
`nObs`	The number of observations to generate.
`probit`	Logical value indicating whether to generate data from a probit model (if `TRUE`) or a logit model (if `FALSE`).
`maxWeight`	Integer value indicating the maximum weight of the observations. If `1`, observations are not weighted. If larger than `1`, a vector of weights is generated and included in the return list. The weights are drawn from a discrete uniform distribution with a maximum value determined by `maxWeight`. If weighted, a larger sample is created (`nObs * sampleFactor * maxWeight`) and a subset of them is randomly selected, where the probability of selection is determined by the weight.
`pPos`	The percentage of positive observations (`y=1`) in the endogenous variable y. Must be between 0 and 1. In the current implementation, this is independent of the weights, if `maxWeight` is larger than 1.
`sampleFactor`	The factor used to control the size of the initial sample. A larger value generates a larger initial sample, which can increase the accuracy of the generated sample but also takes more time and memory.
`toNumeric`	If `TRUE`, `y` and `w` are transformed to have numeric vector. Otherwise, they contain an integer vector.

A list with the following items:

`y`	The endogenous variable.
`x`	The exogenous variables.
`w`	The weights of the observations. It is `NULL` if `weighted` is `FALSE`.
`p1`	Prob(Y=1)
`coef`	The coefficients of the regression.
`probit`	Logical value indicating whether data was generated from a probit model.
`pPos`	The percentage of negative observations in y.

# Generate data from a logit model with 3 variables
sample <- sim.bin(3L, 100)

# see the examples in 'estim.bin' or 'search.bin' functions