EMcvlasso | R Documentation |
EMlasso
cross validation function for EMlasso
.
EMcvlasso(
X,
y,
lambda = NULL,
nbFolds = 10,
maxSteps = 1000,
intercept = TRUE,
model = c("linear", "logistic"),
burn = 30,
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 |
Values at which prediction error should be computed. |
nbFolds |
the number of folds for the cross-validation. |
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 for the burn period. |
threshold |
Zero tolerance. Coefficients under this value are set to zero. |
eps |
Tolerance of the EM algorithm. |
epsCG |
Epsilon for the convergence of the conjugate gradient. |
A list containing
Mean prediction error for each value of index.
Standard error of lambda
.
Minimal lambda
criterion.
Values of lambda
at which prediction error should be computed.
Value of lambda
for which the cv criterion is minimal.
Quentin Grimonprez, Serge Iovleff
dataset <- simul(50, 100, 0.4, 1, 10, matrix(c(0.1, 0.8, 0.02, 0.02), nrow = 2))
result <- EMcvlasso(
X = dataset$data, y = dataset$response,
lambda = 5:1, nbFolds = 5, intercept = FALSE
)
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