lasso.ineq: Complete Run of Constrained LASSO Path Function with... In PACLasso: Penalized and Constrained Lasso Optimization

Description

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).

Usage

 ```1 2 3``` ```lasso.ineq(x, y, C.full, b, l.min = -2, l.max = 6, step = 0.2, beta0 = NULL, verbose = F, max.it = 12, intercept = T, normalize = T, backwards = F) ```

Arguments

 `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 inequality constraints of the form `C.full`*`beta` >= `b`)) `b` constraint vector b `l.min` lowest value of lambda to consider (used as 10^`l.min`). Default is -2 `l.max` largest value of lambda to consider (used as 10^`l.max`). Default is 6 `step` step size increase in lambda attempted at each iteration (by a factor of 10^`step`). Default is 0.2 `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^`l.max` (TRUE) or forwards from 10^`l.max` and then backwards if algorithm gets stuck (FALSE). Default is FALSE.

Value

`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

References

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)

Examples

 ``` 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.ineq(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.ineq(random_data\$x, random_data\$y, random_data\$C.full, random_data\$b, max.it = 1) lasso_err\$error lasso_err\$lambda ```

PACLasso documentation built on May 2, 2019, 2:29 p.m.