Description Usage Arguments Value Author(s) See Also Examples
This function allows implementing differentiable lasso (dlasso) for arbitrary values of λ and s.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
x |
Matrix of predictors |
y |
Response vector |
s |
A single or a vector of precision value, s, given adp=FALSE. Default is 1. See "adp" parameter. |
intercept |
Logical flag. If TRUE, an intercept is included in the model, otherwise no intercept is included. Default is FALSE. |
c |
Choose between dlasso (c=1) and dSCAD (c=-1). Default is dlasso. See futher "force" parameter. |
adp |
Logical flag. TRUE to use adaptive adjustment for s. If TRUE then the function ignores the initial s. |
lambda |
Optional values for the tuning parameter. A single value or a sequence of values. Useful for manually searching over user defined set of tuning values. Set to any negative value to activate the automatic setting for λ.max and λ.min. |
split |
The number of splits between λ.min and λ.max. |
maxIter |
The maximum iterations for the algorithm. Default is 500. |
adj |
Positive value. This value adjusts the upper value for the penalty term, adj*λ.max. |
lowlambda |
The lower limit for the tuning parameter. Default is 10^-3. |
digit |
The maximum number of digits before setting an estimation to zero. The default is 5 digits. |
cauchy |
Logical flag. Set to TRUE to use Cauchy CDF instead of Gaussian one in the penalty function. The default is Gaussian. |
force |
Logical flag. Set to TRUE to let only a decrease in absolute estimation of the parameters. Default is 'auto' that is only applied if sqrt(n)>2*log(p) for n the number of observations and p the number of parameters. |
trace |
Logical flag. If TRUE, output contains some information about the steps. Default is FALSE. |
A "dlasso" object of the form of a matrix ( λ | s | AICc | GIC | BIC | GCV | estimations).
Hamed Haselimashhadi <hamedhaseli@gmail.com>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.