probit_syn function

Probit regression : generic synthetic binary/binomial probit data and model

probit_syn is a generic function for developing synthetic probit regression data and a model given user defined specifications.

probit_syn(nobs=50000, d=1,  xv = c(1, 0.5, -1.5))

Arguments

• nobs: number of observations in model, Default is 50000
• d: binomial denominator, Default is 1, a binary probit model. May use a variable containing different denominator values.
• xv: predictor coefficient values. First argument is intercept. Use as xv = c(intercept , x1_coef, x2_coef, ...)

Details

Create a synthetic probit regression model using the appropriate arguments. Binomial denominator must be declared. For a binary probit model, d=1. A variable may be used as the denominator when values differ. See examples.

Returns

• py: binomial probit numerator; number of successes

• sim.data: synthetic data set

References

Hilbe, J.M. (2011), Negative Binomial Regression, second edition, Cambridge University Press. Hilbe, J.M. (2009), Logistic Regression Models, Chapman & Hall/CRCD

Author(s)

Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of Technology Andrew Robinson, Universty of Melbourne, Australia.

See Also

logit_syn

Examples

# Binary probit regression (denominator=1)
sim.data <-probit_syn(nobs = 5000, d = 1, xv = c(1, .5, -1.5))
myprobit <- glm(cbind(py,dpy) ~ ., family=binomial(link="probit"), data = sim.data)
summary(myprobit)
confint(myprobit)

# Binary probit regression with 3 predictors (denominator=1)
sim.data <-probit_syn(nobs = 5000, d = 1, xv = c(1, .75, -1.5, 1.15))
myprobit <- glm(cbind(py,dpy) ~ ., family=binomial(link="probit"), data = sim.data)
summary(myprobit)
confint(myprobit)

# Binomial or grouped probit regression with defined denominator, den
den <- rep(1:5, each=1000, times=1)*100
sim.data <- probit_syn(nobs = 5000, d = den, xv = c(1, .5, -1.5))
gpy <- glm(cbind(py,dpy) ~ ., family=binomial(link="probit"), data = sim.data)
summary(gpy)

## Not run:

# default
sim.data <- probit_syn()
dprobit <- glm(cbind(py,dpy) ~ . , family=binomial(link="probit"), data = sim.data)
summary(dprobit)
## End(Not run)