betasout: Coefficient estimates from (converged) IWLS fit

In multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression

Coefficient estimates from (converged) IWLS fit

Description

Extracts estimated regression coefficients from the final Iterative Weighted Least Squares fit, as obtained from linear, logistic, or Cox ridge regression.

Usage

betasout(IWLSfit, Xblocks, X1=NULL, penalties, pairing = NULL)

Arguments

IWLSfit	List object, see details
Xblocks	List of data frames or matrices, representing b=1,...,B data blocks of dimensions n x p_b.
X1	Matrix. Dimension n x p_0, p_0 < n, representing unpenalized covariates.
penalties	Numerical vector.
pairing	Numerical vector of length 3 or NULL.

Details

IWLSfit should be the output of either IWLSridge or IWLSCoxridge. Xblocks may be created by createXblocks.

Value

List. Number of components equals number of components of Xblocks plus one, as the output is augmented with an intercept estimate (first component, NULL if absent). Each component is a numerical vector representing regression parameter estimates. Lengths of vectors match column dimensions of Xblocks (nr of variables for given data type)

See Also

createXblocks. A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4

Examples

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)

# Computation of the regression coefficients requires the original
# (large!) nxp data sets, available from link above
## Not run: 
Xbl <- createXblocks(list(datamir,datameth))
betas <- betasout(fit, Xblocks=Xbl, penalties=lambdas)

## End(Not run)

multiridge documentation built on June 13, 2022, 5:07 p.m.