EMfusedlasso | R Documentation |
EM algorithm for fused-lasso penalty
EMfusedlasso(
X,
y,
lambda1,
lambda2,
maxSteps = 1000,
burn = 50,
intercept = TRUE,
model = c("linear", "logistic"),
eps = 1e-05,
eps0 = 1e-08,
epsCG = 1e-08
)
X |
the matrix (of size n*p) of the covariates. |
y |
a vector of length n with the response. |
lambda1 |
a positive real. Parameter associated with the lasso penalty. |
lambda2 |
a positive real. Parameter associated with the fusion penalty. |
maxSteps |
Maximal number of steps for EM algorithm. |
burn |
Number of steps before regrouping some variables in segment. |
intercept |
If TRUE, there is an intercept in the model. |
model |
"linear" or "logistic" |
eps |
tolerance for convergence of the EM algorithm. |
eps0 |
Zero tolerance. Coefficients under this value are set to zero. |
epsCG |
tolerance for convergence of the conjugate gradient. |
A list containing :
Vector containing the number of steps of the algorithm for every lambda.
List of vector of size "step+1". The i+1-th item contains the index of non-zero coefficients at the i-th step.
List of vector of size "step+1". The i+1-th item contains the non-zero coefficients at the i-th step.
Vector of length "step+1", containing the lambda at each step.
Intercept.
Quentin Grimonprez, Serge Iovleff
EMcvfusedlasso
dataset <- simul(50, 100, 0.4, 1, 10, matrix(c(0.1, 0.9, 0.02, 0.02), nrow = 2))
result <- EMfusedlasso(dataset$data, dataset$response, 1, 1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.