View source: R/first_order_glm.R

first_order_GLM | R Documentation |

This is a function taking X, y and required parameters, and using gradient descent to fit a GLM

```
first_order_GLM(
X,
y,
family,
lr = "constant",
gamma = 1e-04,
iter = 1e+05,
tol = 1e-15,
decay = 0
)
```

`X` |
numeric data matrix |

`y` |
response vector |

`family` |
the family of GLM |

`lr` |
adaptive or constant learning rate, input "step" or "constant", default is constant |

`gamma` |
gamma_k, the starting learning rate, default is 0.0001 |

`iter` |
max number of iterations, default is 1e5 |

`tol` |
tolerance, default is 1e-15 |

`decay` |
step decay rate if chosen step for lr, default is 0.00 |

```
n <- 5000; p <- 3
beta <- c(-1, 0.2, 0.1)
X <- cbind(1, matrix(rnorm(n * (p- 1)), ncol = p - 1))
eta <- X %*% beta
lambda <- exp(eta)
y <- rpois(n, lambda = lambda)
first_order_GLM(X,y,family = poisson(link = "log"), lr = "constant")
```

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