The function computes regression coefficients for a penalized generalized linear models for a given lambda value for response variable following normal distribution.
extlasso.norm.lambda(n,p,p1,x,y,xpx,dxpx,xpy,beta.old,
tau,alpha,lambda1,tol,maxiter,eps,xbeta.old)Number of observations
Number of predictors.
Number of active predictors
A n by p1 matrix of predictors.
A vector of n observations.
Matrix X'X
Diagonals of X'X
Vector X'y
A vector of initial values of beta.
Elastic net paramter. Default is 1
Approximation to be used for absolute value. Default is 10^-6.
The value of lambda
Tolerance criterion. Default is 10^-6
Maximum number of iterations. Default is 10000.
value for which beta is set to zero if -eps<beta<eps. Default is 10^-6
A n by 1 vector of xbeta values.
A list with following components
Coefficient estimates
"yes" means converged and "no" means did not converge
Number of iterations to estimate the coefficients
Objective function value at solution
xbeta values at solution
This function is internal and used by extlasso.normal function. User need not call this function.