Description Usage Arguments Details Value References Examples
Determines the value of the penalty parameter lambda when the first penalized parameter group enters the model.
1 2 3 4 5 6 7 8 9 10 11 12 13  lambdamax(x, ...)
## S3 method for class 'formula'
lambdamax(formula, nonpen = ~1, data, weights, subset,
na.action, coef.init, penscale = sqrt, model = LogReg(),
center = TRUE, 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 

nonpen 

data 

index 
vector which defines the grouping of the
variables. Components sharing the same
number build a group. Nonpenalized coefficients are marked with

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 
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 
... 
additional arguments to be passed to the functions defined
in 
Uses nlminb
to optimize the nonpenalized parameters.
An object of type numeric is returned.
Lukas Meier, Sara van de Geer and Peter B\"uhlmann (2008), The Group Lasso for Logistic Regression, Journal of the Royal Statistical Society, 70 (1), 53  71
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[1] 68.30414
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