EMlasso {HDPenReg} | R Documentation |
EM algorithm for lasso penalty
EMlasso(X, y, lambda, maxSteps = 1000, intercept = TRUE, model = c("linear", "logistic"), burn = 50, threshold = 1e-08, eps = 1e-05, epsCG = 1e-08)
X |
the matrix (of size n*p) of the covariates. |
y |
a vector of length n with the response. |
lambda |
a sequence of l1 penalty regularization term. If no sequence is provided, the function computes his own sequence. |
maxSteps |
Maximal number of steps for EM algorithm. |
intercept |
If TRUE, there is an intercept in the model. |
model |
"linear" or "logistic" |
burn |
Number of steps before thresholding some variables to zero. |
threshold |
Zero tolerance. Coefficients under this value are set to zero. |
eps |
Epsilon for the convergence of the EM algorithm. |
epsCG |
Epsilon for the convergence of the conjugate gradient. |
A list containing :
Vector containing the number of steps of the algorithm for every lambda
.
List of vector of the same length as lambda
. The i-th item contains the index of non-zero coefficients for the i-th lambda
value.
List of vector of the same length as lambda
. The i-th item contains the non-zero coefficients for the i-th lambda
value.
Vector containing the lambda
values.
Intercept.
Quentin Grimonprez, Serge Iovleff
dataset=simul(50,100,0.4,1,10,matrix(c(0.1,0.9,0.02,0.02),nrow=2)) result=EMlasso(dataset$data,dataset$response) # Obtain estimated coefficient in matrix format coefficient = listToMatrix(result)