The SimpleRegression companion object provides a simple factory method for building simple regression linear regression models.
Attributes
- Companion
- class
- Graph
-
- Supertypes
-
class Objecttrait Matchableclass Any
- Self type
-
SimpleRegression.type
Members list
Value members
Concrete methods
Create a Simple Linear Regression model from a combined data matrix. The first column of matrix xy should have all ones corresponding to the intercept (matrix from has two column vectors [1 x]). Take the first two columns for the predictor and the last column for the response.
Create a Simple Linear Regression model from a combined data matrix. The first column of matrix xy should have all ones corresponding to the intercept (matrix from has two column vectors [1 x]). Take the first two columns for the predictor and the last column for the response.
Value parameters
- fname
-
the feature/variable names (defaults to null)
- xy
-
the combined data matrix
Attributes
- See also
-
SimplerRegressionfor a model without an intercept parameter
Create a Simple Linear Regression model by automatically prepending the column of ones (form matrix from two column vectors [1 x]).
Create a Simple Linear Regression model by automatically prepending the column of ones (form matrix from two column vectors [1 x]).
Value parameters
- fname
-
the feature/variable names
- x
-
the data/input m-by-1 vector
- y
-
the response/output m-vector
Attributes
Create the Best Simple Linear Regression model using the first column of all ones and the column/variable that is the best predictor xj (matrix [1 xj]). Caveat: assumes matrix x has a first column of all one.
Create the Best Simple Linear Regression model using the first column of all ones and the column/variable that is the best predictor xj (matrix [1 xj]). Caveat: assumes matrix x has a first column of all one.
Value parameters
- fname
-
the feature/variable names (defaults to null)
- x
-
the m-by-n data/input matrix
- y
-
the response/output m-vector
Attributes
Compute the SimpleRegression coefficients directly from the x and y vectors.
Compute the SimpleRegression coefficients directly from the x and y vectors.
Value parameters
- x
-
the data/input m-vector
- y
-
the response/output m-vector
Attributes
Create a Simple Linear Regression model quadratic in x by automatically prepending the column of ones (form matrix from two column vectors [1 x^2]).
Create a Simple Linear Regression model quadratic in x by automatically prepending the column of ones (form matrix from two column vectors [1 x^2]).
Value parameters
- fname
-
the feature/variable names (defaults to null)
- x
-
the data/input m-by-1 vector (to be squared)
- y
-
the response/output m-vector