Ridgecv: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Ridge regression with cross validation. | Ridge regression with cross validation. | ||
===Synopsis=== | ===Synopsis=== | ||
:[b,theta,cumpress] = ridge(x,y,thetamax,divs,split) | :[b,theta,cumpress] = ridge(x,y,thetamax,divs,split) | ||
===Description=== | ===Description=== | ||
The function ridgecv uses cross-validation to create a ridge regression model for a matrix of predictor variables (x-block) x, and a matrix of predicted variables (y-block) y. The maximum value of the ridge parameter to consider is given by thetamax (0 < thetamax). divs specifies the number of values of the ridge parameter between 0 and thetamax to be used for calculating models used in the cross validation and shown in plots created by the routine, and split is the number of times the model is rebuilt on a different subset of samples. | The function ridgecv uses cross-validation to create a ridge regression model for a matrix of predictor variables (x-block) x, and a matrix of predicted variables (y-block) y. The maximum value of the ridge parameter to consider is given by thetamax (0 < thetamax). divs specifies the number of values of the ridge parameter between 0 and thetamax to be used for calculating models used in the cross validation and shown in plots created by the routine, and split is the number of times the model is rebuilt on a different subset of samples. | ||
Outputs are b the regression column vector at optimum ridge parameter theta as determined by cross-validation. | Outputs are b the regression column vector at optimum ridge parameter theta as determined by cross-validation. | ||
In most instances the optimum ridge parameter will be less than 0.1, often as low as 0.01. A good starting guess when working with the method is to specify thetamax = 0.1 with divs = 20. | In most instances the optimum ridge parameter will be less than 0.1, often as low as 0.01. A good starting guess when working with the method is to specify thetamax = 0.1 with divs = 20. | ||
Note: RIDGECV uses the venetian blinds cross-validation method. | Note: RIDGECV uses the venetian blinds cross-validation method. | ||
===See Also=== | ===See Also=== | ||
[[crossval]], [[pcr]], [[pls]], [[analysis]], [[ridge]] | [[crossval]], [[pcr]], [[pls]], [[analysis]], [[ridge]] | ||
Revision as of 15:26, 3 September 2008
Purpose
Ridge regression with cross validation.
Synopsis
- [b,theta,cumpress] = ridge(x,y,thetamax,divs,split)
Description
The function ridgecv uses cross-validation to create a ridge regression model for a matrix of predictor variables (x-block) x, and a matrix of predicted variables (y-block) y. The maximum value of the ridge parameter to consider is given by thetamax (0 < thetamax). divs specifies the number of values of the ridge parameter between 0 and thetamax to be used for calculating models used in the cross validation and shown in plots created by the routine, and split is the number of times the model is rebuilt on a different subset of samples.
Outputs are b the regression column vector at optimum ridge parameter theta as determined by cross-validation.
In most instances the optimum ridge parameter will be less than 0.1, often as low as 0.01. A good starting guess when working with the method is to specify thetamax = 0.1 with divs = 20.
Note: RIDGECV uses the venetian blinds cross-validation method.