# rq.fit.lasso

##### Lasso Penalized Quantile Regression

The fitting method implements the lasso penalty of Tibshirani for
fitting quantile regression models. When the argument `lambda`

is a scalar the penalty function is the l1
norm of the last (p-1) coefficients, under the presumption that the
first coefficient is an intercept parameter that should not be subject
to the penalty. When `lambda`

is a vector it should have length
equal the column dimension of the matrix `x`

and then defines a
coordinatewise specific vector of lasso penalty parameters. In this
case `lambda`

entries of zero indicate covariates that are not
penalized. There should be a sparse version of this, but isn't (yet).

- Keywords
- regression

##### Usage

`rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06)`

##### Arguments

- x
the design matrix

- y
the response variable

- tau
the quantile desired, defaults to 0.5.

- lambda
the value of the penalty parameter(s) that determine how much shrinkage is done. This should be either a scalar, or a vector of length equal to the column dimension of the

`x`

matrix.- beta
step length parameter for Frisch-Newton method.

- eps
tolerance parameter for convergence.

##### Value

Returns a list with a coefficient, residual, tau and lambda components.
When called from `"rq"`

(as intended) the returned object
has class "lassorqs".

##### References

Koenker, R. (2005 *Quantile Regression*, CUP.

##### See Also

##### Examples

```
# NOT RUN {
n <- 60
p <- 7
rho <- .5
beta <- c(3,1.5,0,2,0,0,0)
R <- matrix(0,p,p)
for(i in 1:p){
for(j in 1:p){
R[i,j] <- rho^abs(i-j)
}
}
set.seed(1234)
x <- matrix(rnorm(n*p),n,p) %*% t(chol(R))
y <- x %*% beta + rnorm(n)
f <- rq(y ~ x, method="lasso",lambda = 30)
g <- rq(y ~ x, method="lasso",lambda = c(rep(0,4),rep(30,4)))
# }
```

*Documentation reproduced from package quantreg, version 5.54, License: GPL (>= 2)*