logit: Logistic Regression

In RadojevicM/OLSLog2: OLS and Logistic Regressions for R

Description

The function estimates a logistic regression model with the Maximum-Likelihood Method.

Usage

  logit(fml, dat)

Arguments

fml	Formula-object of the desired independent and dependent variables: formula.object <- as.formula("y ~ x1 + x2 + ... xn").
dat	Data frame which contains the variables specified in the formula-object.

Details

Estimates a logistic regression for a binary independent variable where the data generating process can be described with a Bernoulli-distribution. Parameters are maximised with the Maximum-Likelihood-Method using a Quasi-Newton-Algorithm.

Value

Output are the coefficient estimates of a logistic regression and its respective standard errors.

Author(s)

Johannes Besch, [e-mail], Marco Radojevic [e-mail]

Examples

require(MASS)
model1 = glm(Sex ~ Height + Smoke + Pulse, data=MASS::survey, family=binomial(link=logit))
summary(model1)

RadojevicM/OLSLog2 documentation built on May 28, 2019, 2:26 p.m.