Description Usage Arguments Value Author(s) References Examples

The function computes coefficients of a penalized generalized linear model for normal/binomial/poisson family using modified Jacobi Algorithm for a sequence of lambda values. Currently lasso and elastic net penalty are supported.

1 2 |

`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 either normal/binomial/poisson distribution. |

`family` |
family should be one of these: "normal","binomial","poisson" |

`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. |

An object of class ‘extlasso’ with 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 estimates. Each row denotes solution for a particular lambda. Corresponding lambda values are available in ‘lambdas’ element of the ‘extlasso’ 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 |

B N Mandal and Jun Ma

Mandal, B.N. and Jun Ma, (2014). A Jacobi-Armijo Algorithm for LASSO and its Extensions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
#LASSO
x=matrix(rnorm(100*30),100,30)
y=rnorm(100)
g1=extlasso(x,y,family="normal")
plot(g1)
plot(g1,xvar="lambda")
#Elastic net
g2=extlasso(x,y,family="normal",tau=0.6)
plot(g2)
plot(g2,xvar="lambda")
#Ridge regression
g3=extlasso(x,y,family="normal",tau=0)
plot(g3)
plot(g3,xvar="lambda")
#L1 penalized GLM for binomial family
x=matrix(rnorm(100*30),100,30)
y=sample(c(0,1),100,replace=TRUE)
g1=extlasso(x,y,family="binomial")
plot(g1)
plot(g1,xvar="lambda")
#Elastic net with GLM with binomial family
g2=extlasso(x,y,family="binomial",tau=0.8)
plot(g2)
plot(g2,xvar="lambda")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.