COUNT (version 1.3.2)

logit_syn: Logistic regression : generic synthetic binary/binomial logistic data and model

Description

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

Usage

logit_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 logistic 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

• by
  binomial logistic numerator; number of successes
• sim.data
  synthetic data set

Details

Create a synthetic logistic regression model using the appropriate arguments. Binomial denominator must be declared. For a binary logistic 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

probit_syn

Examples

# Binary logistic regression (denominator=1)
sim.data <-logit_syn(nobs = 500, d = 1, xv = c(1, .5, -1.5))
mylogit <- glm(cbind(by,dby) ~ ., family=binomial(link="logit"), data = sim.data)
summary(mylogit)
confint(mylogit)

# Binary logistic regression with odds ratios (denominator=1); 3 predictors
sim.data <-logit_syn(nobs = 500, d = 1, xv = c(1, .75, -1.5, 1.15))
mylogit <- glm(cbind(by,dby) ~ ., family=binomial(link="logit"), data = sim.data)
exp(coef(mylogit))
exp(confint(mylogit))

# Binomial or grouped logistic regression with defined denominator, den
den <- rep(1:5, each=100, times=1)*100
sim.data <- logit_syn(nobs = 500, d = den, xv = c(1, .5, -1.5))
gby <- glm(cbind(by,dby) ~ ., family=binomial(link="logit"), data = sim.data)
summary(gby)

# default
sim.data <- logit_syn(nobs=500, d=1,  xv = c(2, -.55, 1.15))
dlogit <- glm(cbind(by,dby) ~ . , family=binomial(link="logit"), data = sim.data)
summary(dlogit)