wherein \(\theta_{i}\) is the cumulative probability that the variant is placed in the class \(1\leq j\leq i\)\(x_{i1}\)\(x_{i2}\),\(\ldots\)\(x_{\text{iu}}\) are the independent variables that have some effect on the classification probability, beginning at \(x_{i1}\); and \(\beta_{1}\)\(\beta_{2}\)\(\ldots\)\(\beta_{u}\) are regression coefficients, which define the relative weightings placed on each \(x\) value up to \(u\) independent variables. Thus, eq. \ref{eq:1} may be expanded to the more pragmatic model: