`adalasso(X, y, k = 10, use.Gram = TRUE,both=TRUE,intercept=TRUE)`

X

matrix of input observations. The rows of

`X`

contain the
samples, the columns of `X`

contain the observed variablesy

vector of responses. The length of y must equal the number of
rows of X

k

the number of splits in

`k`

-fold cross-validation. The
same `k`

is used for the estimation of the weights and the
estimation of the penalty term for adaptive lasso. Default is k=10.use.Gram

When the number of variables is very large, you may not want LARS to precompute the Gram matrix. Default is

`use.Gram`

=TRUE. both

Logical. If both=FALSE, only the lasso coefficients are computed. Default is both=TRUE.

intercept

Should an intercept be included? Default is

`intercept=TRUE`

.- intercept.lasso
- intercept for lasso. If
`intercept=FALSE`

was specified, the intercept is set to 0. - intercept.adalasso
- intercept for adaptive lasso. If
`intercept=FALSE`

was specified, the intercept is set to 0. - coefficients.adalasso
- regression coefficients for adaptive lasso.
- coefficients.lasso
- regression coefficients for lasso.
- cv.lasso
- cv error for the optimal lasso model.
- cv.adalasso
- cv error for the optimal adaptive lasso model.
- lambda.lasso
- optimal lambda value for lasso-
- lambda.adalasso
- optimal lambda value for adaptive lasso.

`k`

-fold cross-validation steps, the weights for adaptive lasso are computed in
terms of a lasso fit. (The optimal value of the
penalty term is selected via `k`

-fold cross-validation). Note that this implies that a lasso solution is computed k*k times!
N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384

`Beta2parcor`

, `adalasso.net`

```
n<-100 # number of observations
p<-60 # number of variables
X<-matrix(rnorm(n*p),ncol=p)
y<-rnorm(n)
ada.object<-adalasso(X,y,k=10)
```

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