extlasso.norm.lambda | R Documentation |
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)
n |
Number of observations |
p |
Number of predictors. |
p1 |
Number of active predictors |
x |
A n by p1 matrix of predictors. |
y |
A vector of n observations. |
xpx |
Matrix X'X |
dxpx |
Diagonals of X'X |
xpy |
Vector X'y |
beta.old |
A vector of initial values of beta. |
tau |
Elastic net paramter. Default is 1 |
alpha |
Approximation to be used for absolute value. Default is 10^-6. |
lambda1 |
The value of lambda |
tol |
Tolerance criterion. Default is 10^-6 |
maxiter |
Maximum number of iterations. Default is 10000. |
eps |
value for which beta is set to zero if -eps<beta<eps. Default is 10^-6 |
xbeta.old |
A n by 1 vector of xbeta values. |
This function is internal and used by extlasso.normal function. User need not call this function.
A list with following components
beta.new |
Coefficient estimates |
conv |
"yes" means converged and "no" means did not converge |
iter |
Number of iterations to estimate the coefficients |
ofv.new |
Objective function value at solution |
xbeta.new |
xbeta values at solution |
B N Mandal and Jun Ma
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.