Ridgecv
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Visited: Ridgecv
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Purpose
Ridge regression with cross validation.
Synopsis
- [b,theta,cumpress] = ridge(x,y,thetamax,divs,split)
Description
This function calculates a ridge regression model using a matching set of predictor variables (x-block) x and predicted variables (y-block) y, and uses cross-validation to determine the optimum value of the ridge parameter theta. The maximum value of the ridge parameter to consider is given by thetamax (where 0 < thetamax).
Inputs
- x = matrix of independent variables
- y = matching vector of dependent variables
- thetamax = maximum value of ridge parameter theta to consider
- divs = the number of values of theta to test
- split = the number of times to split and test the data for cross-validation
Outputs
- b = the regression column vector, at the optimal ridge parameter value
- theta = the optimal ridge parameter value
- cumpress = Predicted Residual Sum of Squares (PRESS) statistics for the cross-validation
Note: RIDGECV uses the venetian blinds cross-validation method.