Description Usage Arguments Value Examples
Computes the linear regression coefficient estimates (ridge-penalization and weights, optional)
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X |
matrix or data frame |
y |
matrix or data frame of response values |
lam |
optional tuning parameter for ridge regularization term. If passing a list of values, the function will choose the optimal value based on K-fold cross validation. Defaults to 'lam = seq(0, 2, 0.1)' |
alpha |
optional tuning parameter for bridge regularization term. If passing a list of values, the function will choose the optimal value based on K-fold cross validation. Defaults to 'alpha = 1.5' |
penalty |
choose from c('none', 'ridge', 'bridge'). Defaults to 'none' |
weights |
optional vector of weights for weighted least squares |
intercept |
add column of ones if not already present. Defaults to TRUE |
kernel |
use linear kernel to compute ridge regression coefficeients. Defaults to TRUE when p >> n (for 'SVD') |
method |
optimization algorithm. Choose from 'SVD' or 'MM'. Defaults to 'SVD' |
tol |
tolerance - used to determine algorithm convergence for 'MM'. Defaults to 10^-5 |
maxit |
maximum iterations for 'MM'. Defaults to 10^5 |
vec |
optional vector to specify which coefficients will be penalized |
init |
optional initialization for MM algorithm |
K |
specify number of folds for cross validation, if necessary |
returns the selected tuning parameters, coefficient estimates, MSE, and gradients
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