gradF: Deriving the first derivatives of the log likelihood function...
In BSW: Fitting a Log-Binomial Model Using the Bekhit–Schöpe–Wagenpfeil (BSW) Algorithm
gradF R Documentation
Deriving the first derivatives of the log likelihood function of the log-binomial model in bsw()
Description
gradF() derives the first derivatives of the log likelihood function of the log-binomial model.
Usage
gradF(theta, y, x)
Arguments
theta
A numeric vector containing the initial values of the model parameters.
y
A numeric vector containing the dependent variable of the model.
x
The model matrix.
Value
A numeric vector containing the first derivatives of the log likelihood function of the log-binomial model.
Author(s)
Adam Bekhit, Jakob Schöpe
BSW documentation built on Nov. 5, 2025, 6:26 p.m.
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| gradF | R Documentation |
Deriving the first derivatives of the log likelihood function of the log-binomial model in bsw()
Description
gradF() derives the first derivatives of the log likelihood function of the log-binomial model.
Usage
gradF(theta, y, x)
Arguments
theta |
A numeric vector containing the initial values of the model parameters. |
y |
A numeric vector containing the dependent variable of the model. |
x |
The model matrix. |
Value
A numeric vector containing the first derivatives of the log likelihood function of the log-binomial model.
Author(s)
Adam Bekhit, Jakob Schöpe
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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