Description Usage Arguments Value Author(s) Examples
View source: R/gradient_descent_glm.R
Implement a first-order solution for the GLM maximum likelihood problem using only gradient information, avoiding the Hessian matrix.
1 | glm_constant(X, Y, mu_fun, var_fun, maxit = 1e+06, tol = 1e-10, gamma = 1e-05)
|
X |
design matrix |
Y |
response vector |
mu_fun |
function from eta to the expected value |
maxit |
maximum number of iterations |
tol |
numeric tolerance parameter |
gamma |
The step size |
a list of beta coefficients
Yijun Yang
1 2 3 4 | ## Not run:
glm_constant(X, Y, mu_fun = function(eta) 1/(1+exp(-eta)), var_fun = function(eta) eta)
## End(Not run)
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