sim.bin {ldt} | R Documentation |
Generate Random Sample from a DC Model
Description
This function generates a random sample from an discrete choice regression model.
Usage
sim.bin(
coef = 2L,
nObs = 100,
probit = FALSE,
maxWeight = 1,
pPos = 0.5,
sampleFactor = 4,
toNumeric = TRUE
)
Arguments
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 |
maxWeight |
Integer value indicating the maximum weight of the observations.
If |
pPos |
The percentage of positive observations ( |
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 |
Value
A list with the following items:
y |
The endogenous variable. |
x |
The exogenous variables. |
w |
The weights of the observations. It is |
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. |
See Also
Examples
# Generate data from a logit model with 3 variables
sample <- sim.bin(3L, 100)
# see the examples in 'estim.bin' or 'search.bin' functions