GLM
Related Docs:
object 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
A General Linear Model (GLM) can be developed using the
GLMtrait and object (see below). The implementation currently supports univariate models with multivariate models (where each response is a vector) planned for the future. It provides factory methods for the following special types of GLMs:SimpleRegression- simple linear regression,Regression- multiple linear regression using OLSRegression_WLS- multiple linear regression using WLSRidgeRegression- robust multiple linear regression,TranRegression- transformed (e.g., log) multiple linear regression,PolyRegression- polynomial regression,TrigRegression- trigonometric regressionResponseSurface- response surface regression,ANOVA- GLM form of ANalysis Of VAriance,ANCOVA- GLM form of ANalysis of COVAriance.