GLMgradient: GLM Gradient descent with constant step
In damingli09/bis557: Package for homework of BIS557 Computational Statistics
Description
Fits generalized linear model by gradient descent, maximizing log-likelihood
Usage
1
GLMgradient(X, y, mu_fun, T_fun, lrate = 0.01, maxiter = 10000, tol = 1e-05)
Arguments
X
the design matrix
y
the response variable
mu_fun
function from eta to the expected value
T_fun
the sufficient statistic as a function of y
lrate
the learning rate
maxiter
the maximum iteration number
tol
the numerical tolerance
damingli09/bis557 documentation built on Nov. 21, 2020, 9:11 a.m.
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.
Description
Fits generalized linear model by gradient descent, maximizing log-likelihood
Usage
1 | GLMgradient(X, y, mu_fun, T_fun, lrate = 0.01, maxiter = 10000, tol = 1e-05)
|
Arguments
X |
the design matrix |
y |
the response variable |
mu_fun |
function from eta to the expected value |
T_fun |
the sufficient statistic as a function of y |
lrate |
the learning rate |
maxiter |
the maximum iteration number |
tol |
the numerical tolerance |
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.