lambdamax {grplasso} | R Documentation |
Determines the value of the penalty parameter lambda when the first penalized parameter group enters the model.
lambdamax(x, ...) ## S3 method for class 'formula': lambdamax(formula, nonpen = ~1, data, weights, subset, na.action, coef.init, penscale = sqrt, model = LogReg(), standardize = TRUE, contrasts = NULL, nlminb.opt = list(), ...) ## Default S3 method: lambdamax(x, y, index, weights = rep(1, length(y)), offset = rep(0, length(y)), coef.init = rep(0, ncol(x)), penscale = sqrt, model = LogReg(), center = TRUE, standardize = TRUE, nlminb.opt = list(), ...)
x |
design matrix (including intercept) |
y |
response vector |
formula |
formula of the penalized variables. The response
has to be on the left hand side of '~'. |
nonpen |
formula of the nonpenalized variables. This will
be added to the formula argument above and doesn't need to have the
response on the left hand side. |
data |
data.frame containing the variables in the model. |
index |
vector which defines the grouping of the
variables. Components sharing the same
number build a group. Non-penalized coefficients are marked with
NA . |
weights |
vector of observation weights. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain 'NA's. |
offset |
vector of offset values. |
coef.init |
initial parameter vector. Penalized groups are discarded. |
penscale |
rescaling function to adjust the value of the penalty parameter to the degrees of freedom of the parameter group. See the reference below. |
model |
an object of class grpl.model implementing
the negative log-likelihood, gradient, hessian etc. See
grpl.model for more details. |
center |
logical. If true, the columns of the design matrix will be centered (except a possible intercept column). |
standardize |
logical. If true, the design matrix will be blockwise orthonormalized, such that for each block X^TX = n 1 (*after* possible centering). |
contrasts |
an (optional) list with the contrasts for the factors in the model. |
nlminb.opt |
arguments to be supplied to nlminb . |
... |
additional arguments to be passed to the functions defined
in model . |
Uses nlminb
to optimize the non-penalized parameters.
An object of type numeric is returned.
data(splice) lambdamax(y ~ ., data = splice, model = LogReg(), center = TRUE, standardize = TRUE)