bsw {BSW} | R Documentation |
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 |
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 |
formula |
An object of class |
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)