GLM
Related Docs:
trait GLM
| package analytics
Build an ANalysis of COVAriance (ANCOVA) model.
Build an ANalysis of COVAriance (ANCOVA) model.
the data/design matrix of continuous variables
the treatment/categorical variable vector
the response vector
the number of treatment levels (1, ... levels)
Build an ANalysis Of VAriance (ANOVA) model.
Build an ANalysis Of VAriance (ANOVA) model.
the treatment/categorical variable vector
the response vector
the number of treatment levels (1, ... levels)
Build a Response Surface model.
Build a Response Surface model.
the input vectors/points
the response vector
the order of the surface (false for quadratic, true for cubic)
Build a Trigonomtetric Regression model.
Build a Trigonomtetric Regression model.
the combined input vector and response vector
the maximum multiplier in the trig function (kwt)
the base displacement angle in radians
Build a Trigonometric Regression model.
Build a Trigonometric Regression model.
the input vector: t_i expands to x_i
the response vector
the maximum multiplier in the trig function (kwt)
the base displacement angle in radians
Build a Polynomial Regression model.
Build a Polynomial Regression model.
the combined input vector and response vector
the order of the polynomial
Build a Polynomial Regression model.
Build a Polynomial Regression model.
the input vector: t_i expands to x_i = [1, t_i, t_i2, ... t_ik]
the response vector
the order of the polynomial
Build a Transformed Multiple Linear Regression model.
Build a Transformed Multiple Linear Regression model.
the combined input/design m-by-n matrix and response m-vector
the transformation function
Build a Transformed Multiple Linear Regression model.
Build a Transformed Multiple Linear Regression model.
the input/design m-by-n matrix
the response m-vector
the transformation function (e.g., log)
Build a Multiple Linear Robust Regression model.
Build a Multiple Linear Robust Regression model.
the shrinkage parameter (0 => OLS) in the penalty term 'lambda * b dot b'
Build a Multiple Linear Robust Regression model.
Build a Multiple Linear Robust Regression model.
the centered input/design m-by-n matrix NOT augmented with a first column of ones
the centered response vector
the shrinkage parameter (0 => OLS) in the penalty term 'lambda * b dot b'
Build a Multiple Linear Regression model using Weighted Least Squares (WLS).
Build a Multiple Linear Regression model using Weighted Least Squares (WLS).
the input/design m-by-n matrix
the response m-vector
Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).
Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).
the combined input/design m-by-n matrix and response m-vector
Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).
Build a Multiple Linear Regression model using Ordinary Least Squares (OLS).
the input/design m-by-n matrix
the response m-vector
Build a Simple Linear Regression model, automatically prepending the column of ones.
Build a Simple Linear Regression model, automatically prepending the column of ones.
the input/design m-by-1 vector
the response m-vector
Explicitly set the add_1 flag (column of all ones corresponding to b_0).
Explicitly set the add_1 flag (column of all ones corresponding to b_0).
the value to set the add_1 flag to
Explicitly set the regression technique to use.
Explicitly set the regression technique to use.
the value to set technique to
The
GLMobject makes theGLMtrait's methods directly available. This approach (using traits and objects) allows the methods to also be inherited.