View source: R/MultiLambdaCVfun.R
IWLSridge | R Documentation |
Iterative weighted least squares algorithm for linear and logistic ridge regression. Updates the weights and linear predictors until convergence.
IWLSridge(XXT, Y, X1 = NULL, intercept = TRUE, frac1 = NULL, eps = 1e-07, maxItr = 25, trace = FALSE, model = NULL, E0 = NULL)
XXT |
Matrix. Dimensions |
Y |
Response vector: numeric, binary, or two-class factor |
X1 |
Matrix. Dimension |
intercept |
Boolean. Should an intercept be included? |
frac1 |
Scalar. Prior fraction of cases. Only relevant for |
eps |
Scalar. Numerical bound for IWLS convergence. |
maxItr |
Integer. Maximum number of iterations used in IWLS. |
trace |
Boolean. Should the output of the IWLS algorithm be traced? |
model |
Character. Any of |
E0 |
Numerical vector or |
An (unpenalized) intercept is included by default. To keep the function computationally efficient it returns the linear predictors (which suffice for predictions), instead of parameter estimates. These may be obtained by applying the betasout
function to the output of this function.
List, containing:
etas |
Numerical vector: Final linear predictors |
Ypred |
Predicted survival |
convergence |
Boolean: has IWLS converged? |
nIt |
Number of iterations |
Hres |
Auxiliary list object. Passed on to other functions |
linearized |
Linearized predictions |
unpen |
Boolean: are there any unpenalized covariates involved? Passed on to other functions |
intercept |
Boolean: Is an intercept included? |
eta0 |
Numerical vector: Initial linear predictors |
X1 |
Matrix: design matrix unpenalized variables |
Mark A. van de Wiel, Mirrelijn van Nee, Armin Rauschenberger (2021). Fast cross-validation for high-dimensional ridge regression. J Comp Graph Stat
IWLSCoxridge
for Cox ridge. betasout
for obtaining parameter estimates.
predictIWLS
for predictions on new samples. A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4
data(dataXXmirmeth) resp <- dataXXmirmeth[[1]] XXmirmeth <- dataXXmirmeth[[2]] lambdas <- c(100,1000) # Prepare fitting for the specified penalties. XXT <- SigmaFromBlocks(XXmirmeth,penalties=lambdas) # Fit. fit$etas contains the n linear predictors fit <- IWLSridge(XXT,Y=resp)
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