Source: vignettes/logisticregression.Rmd

Logistic Regression with Iterative (re-)Weighted Least Squares

Logistic regression (LR) models are generalized linear models and often used for binary response models where an observation \(\mathit{y}\) is binary zero or one. The logit-link is used as cannonical link to ensure that the modelled probabilities \(\mathit{\pi}\) lie within \(]0, 1[\). \(\pi_i\) for a specific observation \(i \in \{1, \dots, N\}\) is the probability that we will observe \(y_i = 1\). The model can be written as follows:

  • \(\log\big(\frac{\mathit{\pi}}{1 - \mathit{\pi}}\big) = \mathbf{x}^\top \mathit{\alpha}~~\) or \(~~\pi = \frac{\exp(\mathbf{x}^\top \mathit{\alpha})}{1 + \exp(\mathbf{x}^\top \mathit{\alpha})}\).

\(\mathit{\alpha}\) is a vector of length \(P\) of regression coefficients, \(\mathbf{x}\) a matrix of dimension \(N \times P\) containing the covariates. Given as set of observations \(\mathit{y} \in \{0, 1\}\) and the corresponding covariates \(\mathbf{x}\) the regression coefficients \(\mathit{\alpha}\) can be estimated using e.g., maximum likelihood. The log-likelihood sum of the LR model can be written as follows:

  • \(\ell(\mathit{\alpha}~|~\mathit{y}, \mathbf{x}) = \sum_{i=1}^N \big( \mathit{y} \mathbf{x}^\top \mathit{\alpha} - \log(1 + \exp(\mathbf{x}^\top \mathit{\alpha})) \big)\).

Fitting Logistic Regression Models

The parameters of binary LR models can be estimated using an interative (re-)Weighted least squares (IWLS) solver. The regression coefficients \(\mathit{\alpha}\) are iteratively updated using a Newton-Raphson update procedure. A single Newton update (one single iteration) is:

  • \(\mathit{\alpha}^{(j+1)} = \mathit{\alpha}^{(j)} - \Big(\frac{\partial^2\ell(\mathit{\alpha})}{\partial\mathit{\alpha}\partial\mathit{\alpha}^\top}\Big)^{-1} \frac{\partial\ell(\mathit{\alpha})}{\partial\mathit{\alpha}}\)

Where the derivates are evaluated at \(\mathit{\alpha}^{(j)}\) from the previouse iteration. The first order and second order derivatives of the log-likelihood are:

  • \(\frac{\partial\ell(\mathit{\alpha})}{\partial\mathit{\alpha}} = \sum_{i=1}^N x_i \big(y_i - \frac{\exp(\mathit{x}_i^\top \mathit{\alpha})}{1 + \exp(\mathit{x}_i^\top \mathit{\alpha})}\big) = \sum_{i=1}^N x_i (y_i - \pi_i)\)
  • \(\frac{\partial^2\ell(\mathit{\alpha})}{\partial\mathit{\alpha}\partial\mathit{\alpha}^\top} = \sum_{i=1}^N x_i^2 \big(\frac{\exp(\mathit{x}_i^\top \mathit{\alpha})^2}{(1 + \exp(\mathit{x}_i^\top \mathit{\alpha}))^2} - \frac{\exp(\mathit{x}_i^\top \mathit{\alpha})}{1 + \exp(\mathit{x}_i^\top \mathit{\alpha})}\big) = - \sum_{i=1}^N x_i^2 \pi_i (1 - \pi_i)\)

The same can be written in matrix notation:

  • \(\frac{\partial^2\ell(\mathit{\alpha})}{\partial\mathit{\alpha}\partial\mathit{\alpha}^\top} = - \mathbf{x}^\top \mathbf{w} \mathbf{x}\), \(~~~\frac{\partial\ell(\mathit{\alpha})}{\partial\mathit{\alpha}} = \mathbf{x}^\top (\mathit{y} - \mathit{\pi})\)

… where \(\mathbf{w}\) is an \(N \times N\) diagonal matrix of weights with the diagonal elements \(\mathit{\pi} (1 - \mathit{\pi})\) evaluated at \(\mathit{\alpha}^{(j)}\). Thus, the Newton step in matrix notation is given as:

  • \(\mathit{\alpha}^{(j+1)} = \mathit{\alpha}^{(j)} + (\mathbf{x}^\top \mathbf{w} \mathbf{x})^{-1} \mathbf{x}^\top (\mathit{y} - \mathit{\pi}) = (\mathbf{x}^\top \mathbf{w} \mathbf{x})^{-1} \mathbf{x}^\top \mathbf{w} (\mathbf{x}\mathit{\alpha}^{(j)} + \mathbf{w}^{-1} (\mathit{y} - \mathit{\pi}))\)

With \(\tilde{\mathbf{w}} = \mathbf{w}^\frac{1}{2}\) (\(\mathbf{w} = \tilde{\mathbf{w}}^2\)) we can write the Newton step as:

  • \(\mathit{\alpha}^{(j+1)} = (\mathbf{x}^\top \tilde{\mathbf{w}} \tilde{\mathbf{w}} \mathbf{x})^{-1} \mathbf{x}^\top \tilde{\mathbf{w}} (\underbrace{\mathbf{x} \tilde{\mathbf{w}} \mathit{\alpha}^{(j)} + \tilde{\mathbf{w}}^{-1} (\mathit{y} - \mathit{\pi})}_{\text{weighted adjusted response}})\).

With \(\mathbf{x^*} = \mathbf{x} \tilde{\mathbf{w}}\) and \(z = \mathbf{x} \tilde{\mathbf{w}} \mathit{\alpha}^{(j)} + \tilde{\mathbf{w}}^{-1} (\mathit{y} - \mathit{\pi})\) the equation can be rewritten as:

  • \(\mathit{\alpha}^{(j+1)} = (\mathbf{x}^{*\top} \mathbf{x}^*)^{-1} \mathbf{x}^{*\top} \mathit{z}\)

… similar to ordinary least squares.

IWLS Algorithm

Given the equations above the iterative algorithm can be written as follows:

Initialization

  1. Initialize \(\mathit{\alpha}^{(0)} = 0\) (set all coefficients to zero)
  2. Initialize \(\mathit{\pi}^{(0)} = 0.5\) (\(\mathit{\pi}^{(0)} = \frac{\exp(\mathbf{x}^\top \mathit{\alpha}^{(0)})}{1 + \exp(\mathbf{x}^\top \mathit{\alpha}^{(0)})}\))

Update step for iteration \(j = 1, \dots, \text{maxit}\):

  1. Update weights: \(\tilde{\mathbf{w}}^{(j)} = \big(\mathit{\pi}^{(j-1)} (1 - \mathit{\pi}^{(j-1)})\big)^\frac{1}{2}\)
  2. Update weighted adjusted response: \(\mathit{z}^{(j)} = \mathbf{x} \tilde{\mathbf{w}}^{(j)} \mathit{\alpha}^{(j-1)} + (\tilde{\mathbf{w}}^{(j)})^{-1} (\mathit{y} - \mathit{\pi}^{(j-1)})\)
  3. Update coefficients: \(\mathit{\alpha}^{(j)} = (\mathbf{x}^\top \tilde{\mathbf{w}}^{(j)} \tilde{\mathbf{w}}^{(j)} \mathbf{x})^{-1} \mathbf{x}^\top \tilde{\mathbf{w}}^{(j)} \mathit{z}^{(j)}\)
  4. Calculate likelihood: \(\ell^{(j)}\). If \(j = 1\) proceed with step 3.
  5. For \(j > 1\): if \((\ell^{(j)} - \ell^{(j-1)}) < \text{tol}\) the likelihood could not have been improved in this iteration (converged or stuck): stop IWLS algorithm and return \(\mathit{\alpha}^{(j-1)}\). If \(j = \text{maxit}\): maximum number of iterations reached, stop algorith and return \(\mathit{\alpha}^{(j)}\). Else proceed with step 3 until one of the stopping criteria is reached.

The manual page of the iwls_logit function contains a practical example. More details about the IWLS procedure can be found in Hastie, Tibshirani, and Friedman (2009, Chap. 4.4.1), McCullagh and Nelder (1999, Chap. 4.4), and many other statistical text books (see References for details).