the input/design matrix augmented with a first column of ones
the binary response vector, y_i in {0, 1}
Return the fit (parameter vector b, quality of fit rSquared)
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
For a given parameter vector b, compute -2 * Log-Likelihood (-2LL).
For a given parameter vector b, compute -2 * Log-Likelihood (-2LL). -2LL is the standard measure that follows a Chi-Square distribution.
the parameters to fit
www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
Compute the log of the odds of an event ocurring (e.
Compute the log of the odds of an event ocurring (e.g., success, 1).
the probability, a number between 0 and 1.
Compute the inverse of the logit function.
Compute the inverse of the logit function.
the logit value
Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
the new matrix to predict
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e., (b0, b1) dot (1., z1).
the new vector to predict
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
the vector to use for prediction
Train the predictor by fitting the parameter vector (b-vector) in the logit regression equation using maximum likelihood.
Train the predictor by fitting the parameter vector (b-vector) in the logit regression equation using maximum likelihood. Do this by minimizing -2LL.
The LogitRegression class supports logit regression. In this case, x is multi-dimensional (1, x1, ... xk). Fit the parameter vector b in the logit regression equation.
see.stanford.edu/materials/lsoeldsee263/05-ls.pdf