logit: Logistic Regression

Description Usage Arguments Details Value Author(s) Examples

View source: R/logit.R

Description

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

Usage

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  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, [email protected], Marco Radojevic [email protected]

Examples

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

RadojevicM/OLSLog2 documentation built on Nov. 18, 2017, 8:53 a.m.