ridgesumR: Function to obtain RIDGE estimates of a regression problem...

In SeojinHwang/scadsum: nonconvex penalty with summary statistics and a reference panel

Function to obtain RIDGE estimates of a regression problem given summary statistics and a reference panel (without PLINK bfile)

Description

Function to obtain RIDGE estimates of a regression problem given summary statistics and a reference panel (without PLINK bfile)

Usage

ridgesumR(
  cor,
  refpanel,
  lambda = exp(seq(log(0.001), log(0.1), length.out = 20)),
  shrink = 0.9,
  thr = 1e-04,
  init = NULL,
  trace = 0,
  maxiter = 10000,
  blocks = NULL
)

Arguments

cor	A vector of correlations (r)
refpanel	reference panel as data.frame or matrix
lambda	A vector of \lambdas (the tuning parameter)
shrink	The shrinkage parameter s for the correlation matrix R
thr	convergence threshold for \beta
init	Initial values for \beta
trace	An integer controlling the amount of output generated.
maxiter	Maximum number of iterations
blocks	A vector to split the genome by blocks (coded as c(1,1,..., 2, 2, ..., etc.))
ridge	Produce ridge regression results also (slow if nrow(refpanel) > 2000)

Details

A function to find the minimum of \beta in

f(\beta)=\beta'R\beta - 2\beta'r + \lambda||\beta||_2

where

R=(1-s)X'X/n + sI

is a shrunken correlation matrix, with X being standardized reference panel. s should take values in (0,1]. r is a vector of correlations.

Value

A list with the following

lambda	same as the lambda input
beta	A matrix of estimated coefficients
conv	A vector of convergence indicators. 1 means converged. 0 not converged.
pred	=(1-s)X\beta
loss	=(1-s)\beta'X'X\beta/n - 2\beta'r
fbeta	=\beta'R\beta - 2\beta'r + \lambda||\beta||_2
sd	The standard deviation of the reference panel SNPs
shrink	same as input
nparams	Number of non-zero coefficients

Note

• Missing values in refpanel are filled with 0.

• Unlike lassosum, we do not provide the options keep/remove/extract/exclude. It is thus up to the user to ensure the SNPs in the reference panel corresponds to those in the correlations.

SeojinHwang/scadsum documentation built on June 30, 2023, 10:52 p.m.