The function computes regression coefficients for a penalized generalized linear models for a given lambda value for response variable following binomial distribution.
extlasso.binom.lambda(n,p,p1,sumy,beta0.old,beta1.old,x,y,
dxkx0,dxkx1,tau,lambda1,alpha,tol,maxiter,eps,xbeta.old,mu1)Number of observations
Number of predictors
Number of active predictors
Sum of y values
Initial value of intercept
A vector of initial values of slope coefficients
A n by p1 matrix of predictors
A vector of n observations
In case of a model with intercept, first diagonal of X'X
Diagonals of X'X
Elastic net paramter. Default is 1
The value of lambda
Approximation to be used for absolute value. Default is 10^-6
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
The value of mu at beta.old
A list with following components
Intercept estimate
Slope 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
Value of mu at solution
This function is internal and used by extlasso.binomial function. User need not call this function.