cv_ridge: Use cross validation to find the optimal ridge penalty...
In siqiangsu/bis557: BIS557 Homework Package
Description
Usage
Arguments
Value
Examples
Description
Use cross validation to find the optimal ridge penalty parameter
Usage
1
cv_ridge(form, d, lambdas, tol = 1e-08, seed)
Arguments
form
A formula specifying the model.
d
A dataframe.
lambdas
The candidate tuning parameters for the ridge penalty term
tol
Tolerance for the condition number to truncate colinear variables
seed
A seed to control the randomness
Value
A list with fitted coefficients, regression data X,Y and tolerance
Examples
1
siqiangsu/bis557 documentation built on Dec. 21, 2020, 2:15 a.m.
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Description Usage Arguments Value Examples
Description
Use cross validation to find the optimal ridge penalty parameter
Usage
1 | cv_ridge(form, d, lambdas, tol = 1e-08, seed)
|
Arguments
form |
A formula specifying the model. |
d |
A dataframe. |
lambdas |
The candidate tuning parameters for the ridge penalty term |
tol |
Tolerance for the condition number to truncate colinear variables |
seed |
A seed to control the randomness |
Value
A list with fitted coefficients, regression data X,Y and tolerance
Examples
1 |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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