gradF: Deriving the first derivatives of the log likelihood function...
In BSW: Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil (BSW) Algorithm
Description
Usage
Arguments
Value
Author(s)
Description
gradF() derives the first derivatives of the log likelihood function of the log-binomial model.
Usage
1
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 March 22, 2021, 5:07 p.m.
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Description Usage Arguments Value Author(s)
Description
gradF() derives the first derivatives of the log likelihood function of the log-binomial model.
Usage
1 | 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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