hridge: Heteroskedastic Ridge Regression
In MichalOleszak/momisc: Michal Oleszak's Miscellaneous Functions
Description
Usage
Arguments
Value
Author(s)
Description
Heteroskedastic Ridge Regression
Usage
1
2
hridge(y, X, lambda, predictor_weights = "varbased",
optim_method = "Nelder-Mead")
Arguments
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()
Value
A list with parameter estimates, fitted values and multiple R-squared.
Author(s)
Michal Oleszak
MichalOleszak/momisc documentation built on May 29, 2019, 3:02 a.m.
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Description Usage Arguments Value Author(s)
Description
Heteroskedastic Ridge Regression
Usage
1 2 | hridge(y, X, lambda, predictor_weights = "varbased",
optim_method = "Nelder-Mead")
|
Arguments
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() |
Value
A list with parameter estimates, fitted values and multiple R-squared.
Author(s)
Michal Oleszak
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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