bolasso {mht} | R Documentation |
Perform a bootstrapped Lasso on some random subsamplings of the input data
bolasso(data,Y,mu,m,probaseuil,penalty.factor,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. |
mu |
Positive regularization sequence to be used for the Lasso. |
m |
Number of bootstrap iteration of the Lasso. Default is m=100. |
probaseuil |
A frequency threshold for selecting the most stable variables over the |
penalty.factor |
Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables except the intercept. |
random |
optionnal parameter, matrix of size n*m. If |
The Lasso from the glmnet
package is performed with the regularization parameter mu over m bootstrap samples. An appearance frequency is obtained which shows the predictive power of each variable. It is calculated as the number of times a variables has been selected by the Lasso over the m
bootstrap iteration.
A 'bolasso' object is returned for which the method plot
is available.
data |
A list containing:
|
ind |
Set of selected variables for the regularization |
frequency |
Appearance frequency of each variable; number of times each variables is selected over the m bootstrap iterations. |
Model-consistent sparse estimation through the bootstrap; F. Bach 2009
## Not run: x=matrix(rnorm(100*20),100,20) beta=c(rep(1,5),rep(0,15)) y=x%*%beta+rnorm(100) mod=bolasso(x,y,mu=seq(1.5,0.1,-0.1)) mod ## End(Not run)