Description Usage Arguments Details Value Author(s) References See Also
This algorithm uses predictorcorrector method to compute the entire regularization path for generalized linear models with L1 penalty.
1 2 3 4 5 6 7 8  glmpath(x, y, data, nopenalty.subset = NULL, family = binomial,
weight = rep(1, n), offset = rep(0, n), lambda2 = 1e5,
max.steps = 10*min(n, m), max.norm = 100*m,
min.lambda = (if (m >= n) 1e6 else 0), max.vars = Inf,
max.arclength = Inf, frac.arclength = 1, add.newvars = 1,
bshoot.threshold = 0.1, relax.lambda = 1e8,
standardize = TRUE, function.precision = 3e13,
eps = .Machine$double.eps, trace = FALSE)

x 
matrix of features 
y 
response 
data 
a list consisting of 
nopenalty.subset 
a set of indices for the predictors that are not subject to the L1 penalty 
family 
name of a family function that represents the distribution of y to
be used in the model. It must be 
weight 
an optional vector of weights for observations 
offset 
an optional vector of offset. If a column of 
lambda2 
regularization parameter for the L2 norm of the
coefficients. Default is 
max.steps 
an optional bound for the number of steps to be taken. Default is

max.norm 
an optional bound for the L1 norm of the coefficients. Default is

min.lambda 
an optional (lower) bound for the size of λ. Default is

max.vars 
an optional bound for the number of active variables. Default is

max.arclength 
an optional bound for arc length (L1 norm) of a step. If

frac.arclength 
Under the default setting, the next step size is computed so that
the active set changes right at the next value of lambda. When

add.newvars 

bshoot.threshold 
If the absolute value of a coefficient is larger than

relax.lambda 
A variable joins the active set if l'(β) >
λ*(1 
standardize 
If 
function.precision 

eps 
an effective zero 
trace 
If 
This algorithm implements the predictorcorrector method to determine the entire path of the coefficient estimates as the amount of regularization varies; it computes a series of solution sets, each time estimating the coefficients with less regularization, based on the previous estimate. The coefficients are estimated with no error at the knots, and the values are connected, thereby making the paths piecewise linear.
We thank Michael Saunders of SOL, Stanford University for providing the solver used for the convex optimization in corrector steps of glmpath.
A glmpath
object is returned.
lambda 
vector of λ values for which the exact coefficients are computed 
lambda2 
λ_2 used 
step.length 
vector of step lengths in λ 
corr 
matrix of l'(β) values (derivatives of the loglikelihood) 
new.df 
vector of degrees of freedom (to be used in the plot function) 
df 
vector of degrees of freedom at each step 
deviance 
vector of deviance computed at each step 
aic 
vector of AIC values 
bic 
vector of BIC values 
b.predictor 
matrix of coefficient estimates from the predictor steps 
b.corrector 
matrix of coefficient estimates from the corrector steps 
new.A 
vector of boolean values indicating the steps at which the active set changed (to be used in the plot/predict functions) 
actions 
actions taken at each step 
meanx 
means of the columns of x 
sdx 
standard deviations of the columns of x 
xnames 
column names of x 
family 
family used 
weight 
weights used 
offset 
offset used 
nopenalty.subset 
nopenalty.subset used 
standardize 

Mee Young Park and Trevor Hastie
Mee Young Park and Trevor Hastie (2007) L1 regularization path algorithm for generalized linear models. J. R. Statist. Soc. B, 69, 659677.
cv.glmpath, plot.glmpath, predict.glmpath, summary.glmpath
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