`adalasso.net(X, k = 10,use.Gram=FALSE,both=TRUE,verbose=FALSE,intercept=TRUE)`

X

matrix of observations. The rows of

`X`

contain the
samples, the columns of `X`

contain the observed variables.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 value 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`

=FALSE. both

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

verbose

Print information on conflicting signs etc. Default is

`verbose=FALSE`

intercept

Should an intercept be included in the regression models? Default is

`intercept=TRUE`

.- pcor.adalasso
- estimated matrix of partial correlation coefficients for adaptive lasso.
- pcor.lasso
- estimated matrix of partial correlation coefficients for lasso. ...

`X`

, a regression model based on
(adaptive) lasso is computed. In each of the `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! Finally, the results of the regression models are
transformed via the function `Beta2parcor`

.
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`

```
n<-20
p<-10
X<-matrix(rnorm(n*p),ncol=p)
pc<-adalasso.net(X,k=5)
```

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