krr: Kernel Ridge Regression
In TimothyKBook/krr: Kernel Ridge Regression
Description
Usage
Arguments
Value
Description
A function for producing a krr (kernel ridge regression) model.
Usage
1
Arguments
x
Quantitative explanatory variables (vector, matrix, or data.frame).
y
Quantitative response variable.
lambda
Tuning parameter for regression.
sigma
Tuning parameter for Gaussian kernel.
Value
Returns the predictions, alphas, and MSE associated with the
regression, as well as the inputs x, y, the kernel function, and lambda.
TimothyKBook/krr documentation built on May 9, 2019, 4:48 p.m.
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Description Usage Arguments Value
Description
A function for producing a krr (kernel ridge regression) model.
Usage
1 |
Arguments
x |
Quantitative explanatory variables (vector, matrix, or data.frame). |
y |
Quantitative response variable. |
lambda |
Tuning parameter for regression. |
sigma |
Tuning parameter for Gaussian kernel. |
Value
Returns the predictions, alphas, and MSE associated with the regression, as well as the inputs x, y, the kernel function, and lambda.
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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