Usage
nnlasso.normal(x,y,lambda=NULL, intercept=TRUE,normalize=TRUE,tau=
1,tol=1e-6,maxiter=1e5,nstep=100,min.lambda=1e-4,eps=1e-6,path=TRUE,SE=FALSE)
Arguments
x
x is matrix of order n x p where n is number of observations and p is number of predictor variables. Rows should represent observations and columns should represent predictor variables.
y
y is a vector of response variable of order n x 1. y should follow normal distribution.
lambda
The value of lambda for which coefficients are desired. The value of path must be FALSE in this case.
intercept
If TRUE, model includes intercept, else the model does not have intercept.
normalize
If TRUE, columns of x matrix are normalized with mean 0 and norm 1 prior to fitting the model. The coefficients at end are returned on the original scale. Default is normalize = TRUE.
tau
Elastic net parameter, $0 \le \tau \le 1$ in elastic net penalty $\lambda\{\tau\|\beta\|_1+(1-\tau)\|\beta\|_2^2\}$. Default tau = 1 corresponds to LASSO penalty.
tol
Tolerance criteria for convergence of solutions. Default is tol = 1e-6.
maxiter
Maximum number of iterations permissible for solving optimization problem for a particular lambda. Default is 10000. Rarely you need to change this to higher value.
nstep
Number of steps from maximum value of lambda to minimum value of lambda. Default is nstep = 100.
min.lambda
Minimum value of lambda. Default is min.lambda=1e-4.
eps
A small value below which a coefficient would be considered as zero.
path
Logical. If path=TRUE, entire regularization path will be obtained for a sequence of lambda values which are calculated automatically. To get coefficient estimates for a single lambda value, set path=FALSE with lambda=value. Default is path=TRUE.
SE
logical. If SE=TRUE, standard errors are produced for estimated coefficient at a given lambda. Standard errors are not produced if path=TRUE. Default is SE=FALSE.