Consider a binary outcome response variable \(Y\in\{0,1\}\) and let \(p\) be the probability that \(Y\) is \(1\), i.e. \(p=P(Y=1)\).

The logistic model is formally given by:-

\(\log(\frac{p}{1-p})=\beta_0+\beta_1x_1+\beta_2x_2+\cdots+\beta_px_p\)

In the two texareas below, the first (narrow) one on the left has the response variable \(Y\), while the second one has the corresponding comma separated predictor variable entries \(x_k\). Example values have been entered (2000 samples), which can be altered as appropriate. In addition, it is possible to load a CSV format file by clicking on the "Choose File" button - the first column has to be the response variable (taking either 1 or 0 as a value), while column 2 onwards are the real valued predictor variables.

To run the algorithm once the values have been entered in the textareas, simply click on the "Estimate model parameters" button. A plot indicating algorithm convergence will be updated (showing the increase of the log likelihood function), and could be useful in specifying the number of iterations needed for the Newton-Raphson algorithm. Finally, the Receiver Operating Characteristic (ROC) plot will be generated. For the ROC, a black line of gradient 1 will be generated as reference.

Results pending...

Algorithm convergence | ||

Iteration number |

Receiver Operating Characteristic | ||

Sensitivity | ||

1 - Specificity |