Description Usage Arguments Value References Examples

This is a wrapper function for the `lars.c`

PaC
constrained Lasso function. `lasso.c`

controls the overall path,
providing checks for the path and allowing the user to control
how the path is computed (and what to do in the case of a stopped path).

1 2 3 |

`x` |
independent variable matrix of data to be used in calculating PaC coefficient paths |

`y` |
response vector of data to be used in calculating PaC coefficient paths |

`C.full` |
complete constraint matrix C (with constraints of the form |

`b` |
constraint vector b |

`l.min` |
lowest value of lambda to consider (used as 10^ |

`l.max` |
largest value of lambda to consider (used as 10^ |

`step` |
step size increase in lambda attempted at each iteration (by a factor of 10^ |

`beta0` |
initial guess for beta coefficient vector. Default is NULL (indicating initial vector should be calculated by algorithm) |

`verbose` |
should function print output at each iteration (TRUE) or not (FALSE). Default is FALSE |

`max.it` |
maximum number of times step size is halved before the algorithm terminates and gives a warning. Default is 12 |

`intercept` |
should intercept be included in modeling (TRUE) or not (FALSE). Default is TRUE. |

`normalize` |
should X data be normalized. Default is TRUE |

`backwards` |
which direction should algorithm go, backwards from lambda = 10^ |

`coefs`

A `p`

by length(`lambda`

) matrix with each column corresponding to the beta estimate for that lambda

`lambda`

vector of values of lambda that were fit

`intercept`

vector with each element corresponding to intercept for corresponding lambda

`error`

Indicator of whether the algorithm terminated early because max.it was reached

Gareth M. James, Courtney Paulson, and Paat Rusmevichientong (JASA, 2019) "Penalized and Constrained Optimization." (Full text available at http://www-bcf.usc.edu/~gareth/research/PAC.pdf)

1 2 3 4 5 6 7 8 9 10 11 12 | ```
random_data = generate.data(n = 500, p = 20, m = 10)
lasso_fit = lasso.c(random_data$x, random_data$y, random_data$C.full, random_data$b)
lasso_fit$lambda
lasso_fit$error
### The coefficients for the first lambda value
lasso_fit$coefs[1,]
### Example of code where path is unable to be finished
### (only one iteration), so both directions will be tried
lasso_err = lasso.c(random_data$x, random_data$y, random_data$C.full,
random_data$b, max.it = 1)
lasso_err$error
lasso_err$lambda
``` |

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