Description Usage Arguments Details Value
View source: R/LogisticRegression.R
Optimisation procedure based on iteratively re-weighted least squares, called by lr
.
1 |
y |
response vector |
x |
model matrix of covariates |
init |
initial estimate of |
tol |
tolerance parameter, default |
maxiter |
optional number of iterations |
Iterative method used to find parameter estimates of β from the least squares problem
β = argmin (z - Xβ)^T W (z- Xβ),
where W is a diagonal matrix of weights with i-th diagonal element being
σ(x_i ; β) (1 - σ(x_i ; β)),
and z is the vector
z = X β + W^{-1} (y - σ(x_i ; β)).
The parameter vector β is updated iteratively with a Newton update of the form
β = (X^T W X)^{-1} X^T W z.
a list containing
|
a vector of estimates of |
|
the value of the log-likelihood at the final |
|
the number of iterations needed to converge |
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