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
Predict the value of y = f(z) by evaluating the formula y = f(z_i, b) for each row of matrix z.
Predict the value of y = f(z) by evaluating the formula y = f(z_i, b) for each row of matrix z.
the new matrix to predict
Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.
Predict the value of y = f(z) by evaluating the formula y = f(z, b), i.e.0, (b0, b1) dot (1.0, 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
Function to compute the Sum of Squares Error (SSE) for given values for the parameter vector b.
Function to compute the Sum of Squares Error (SSE) for given values for the parameter vector b.
the parameter vector
Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation y = f(x, b) using the least squares method.
Train the predictor by fitting the parameter vector (b-vector) in the non-linear regression equation y = f(x, b) using the least squares method. Caveat: Optimizer may converge to an unsatisfactory local optima. If the regression can be linearized, use linear regression for starting solution.
The
NonLinRegressionclass supports non-linear regression. In this case, 'x' can be multi-dimensional [1, x1, ... xk] and the function 'f' is non-linear in the parameters 'b'. Fit the parameter vector 'b' in the regression equationy = f(x, b) + e
where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to fit the parameter vector 'b' by using Non-linear Programming to minimize Sum of Squares Error (SSE).
www.bsos.umd.edu/socy/alan/stats/socy602_handouts/kut86916_ch13.pdf