EMlasso | 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
EMcvlasso
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)
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