COUNT (version 1.3.2)

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

Description

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

Usage

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, ...)

Value

• py
  binomial probit numerator; number of successes
• sim.data
  synthetic data set

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.

References

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

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)

# default
sim.data <- probit_syn()
dprobit <- glm(cbind(py,dpy) ~ . , family=binomial(link="probit"), data = sim.data)
summary(dprobit)