Description Usage Arguments Details Value See Also Examples
Dyadic algorithm using the Bolasso technique to order the variables
1 | dyadiqueordre(data,Y,m,maxordre,var_nonselect,showtest,showordre,random)
|
data |
Input matrix of dimension n * p; each of the n rows is an observation vector of p variables. The intercept should be included in the first column as (1,...,1). If not, it is added. |
Y |
Response variable of length n. |
m |
Number of bootstrap iteration of the Lasso. Default is |
maxordre |
Number of variables to order. Default is min(n/2-1,p/2-1). |
var_nonselect |
Number of variables that don't undergo feature selection. They have to be in the first columns of data. Default is 1, the selection is not performed on the intercept. |
showtest |
Logical value. If TRUE, show the number of regularization parameters tested to show the steps of the algorithm. Default is FALSE. |
showordre |
Logical value. If TRUE, shows the ordered variables at each step of the algorithm. Default is TRUE. |
random |
optionnal parameter. Matrix of size n*m, the m bootstrap samples are constructed from the m columns. |
The algorithm starts from a large regularization parameter given by one run of Lasso. It proceeds by dyadic splitting until one variable is isolated; e.g one variable alone achieve a frequency of 1; it is the first ordered variable. And so on until maxordre
variables are ordered.
A 'bolasso' object is returned for which the method plot
is available.
data |
A list containing:
|
ordre |
The order obtained on the variables. |
mu |
Vector of the positive regularization sequence that was used in the algorithm. |
frequency |
Matrix of p rows. Appearance frequency of each variable for the regularization parameter in |
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