bsw {BSW}

Fitting a log-binomial model using the Bekhit-Schöpe-Wagenpfeil (BSW) algorithm

Description

bsw() fits a log-binomial model using a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem under linear inequality constraints.

Usage

bsw(formula, data, maxit = 200L)

Arguments

formula

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

data

A data frame containing the variables in the model.

maxit

A positive integer giving the maximum number of iterations.

Value

An object of S4 class "bsw" containing the following slots:

call

An object of class "call".

formula

An object of class "formula".

coefficients

A numeric vector containing the estimated model parameters.

iter

A positive integer indicating the number of iterations.

converged

A logical constant that indicates whether the model has converged.

y

A numerical vector containing the dependent variable of the model.

x

The model matrix.

data

A data frame containing the variables in the model.

Author(s)

Adam Bekhit, Jakob Schöpe

References

Wagenpfeil S (1996) Dynamische Modelle zur Ereignisanalyse. Herbert Utz Verlag Wissenschaft, Munich, Germany

Wagenpfeil S (1991) Implementierung eines SQP-Verfahrens mit dem Algorithmus von Ritter und Best. Diplomarbeit, TUM, Munich, Germany

Examples

set.seed(123)
x <- rnorm(100, 50, 10)
y <- rbinom(100, 1, exp(-4 + x * 0.04)) 
fit <- bsw(formula = y ~ x, data = data.frame(y = y, x = x))
summary(fit)