nnlasso.normal: Entire regularization path of non-negative penalized...
In nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

Description Usage Arguments Value Author(s) References

The function computes coefficients of a penalized generalized linear model subject to non-negativity constraint for normal family using Multiplicative Iterative Algorithm for a sequence of lambda values or alternatively for a single lambda value. Currently lasso and elastic net penalty are supported.

1 2	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)

`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 ≤ τ ≤ 1 in elastic net penalty λ\{τ\\|β\\|_1+(1-τ)\\|β\\|_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.

An object of class ‘nnlasso’ for which plot, predict and coef method exists. The object has following components:

`beta0`	A vector of order nstep of intercept estimates. Each value denote an estimate for a particular lambda. Corresponding lambda values are available in ‘lambdas’ element of the ‘nnlasso’ object.
`coef`	A matrix of order nstep x p. Each row denotes solution for a particular lambda. Corresponding lambda values are available in ‘lambdas’ element of the ‘nnlasso’ object. Here p is number of predictor variables.
`lambdas`	Sequence of lambda values for which coefficients are obtained
`L1norm`	L1norm of the coefficients
`norm.frac`	Fractions of norm computed as L1 norm at current lambda divided by maximum L1 norm
`lambda.iter`	Number of iterations used for different lambdas
`of.value`	Objective function values
`normx`	Norm of x variables
`se`	The standard errors of coefficient estimates