Description Usage Arguments Value Author(s)
Heteroskedastic Ridge Regression
1 2 | hridge(y, X, lambda, predictor_weights = "varbased",
optim_method = "Nelder-Mead")
|
y |
Vector of response variable |
X |
Matrix of predictor variables |
lambda |
Value of ridge (L2) regularization penalty |
predictor_weights |
Vector of length ncol(X) with weights for predictor variables. Defaults to "varbased", in which case weights are based on the variance of parameter estimates obtained through a set of univaraite regressions. |
optim_method |
Optimization algorithm, passed to optim() |
A list with parameter estimates, fitted values and multiple R-squared.
Michal Oleszak
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