extlasso.poisson {extlasso} | R Documentation |
Entire regularization path of penalized generalized linear model for poisson family using modified Jacobi Algorithm
Description
The function computes coefficients of a penalized generalized linear model for poisson family using modified Jacobi Algorithm for a sequence of lambda values. Currently lasso and elastic net penalty are supported.
Usage
extlasso.poisson(x,y,intercept=TRUE,normalize=TRUE,tau=1,alpha=1e-12,
eps=1e-6,tol=1e-6,maxiter=1e5,nstep=100,min.lambda=1e-4)
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 poisson distribution and y should be a vector of counts. |
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, |
alpha |
The quantity in approximating |
eps |
A value which is used to set a coefficient to zero if coefficients value is within - eps to + eps. Default is eps = 1e-6. |
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. |
Value
An object of class ‘extlasso’ 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 ‘extlasso’ object. |
coef |
A matrix of order nstep x p of slope coefficients. Each row denote solution for a particular lambda. Corresponding lambda values are available in ‘lambdas’ element of the ‘extlasso’ object. |
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 |
Author(s)
B N Mandal and Jun Ma
References
Mandal, B.N. and Jun Ma, (2014). A Jacobi-Armijo Algorithm for LASSO and its Extensions.
Examples
x=matrix(rnorm(100*30),100,30)
y=sample(c(1:5),100,replace=TRUE)
g1=extlasso.poisson(x,y)
plot(g1)
plot(g1,xvar="lambda")
g1$of.value