glmpath {glmpath} | R Documentation |
This algorithm uses predictor-corrector method to compute the entire regularization path for generalized linear models with L1 penalty.
glmpath(x, y, data, nopenalty.subset = NULL, family = binomial, weight = rep(1, n), offset = rep(0, n), lambda2 = 1e-5, max.steps = 10*min(n, m), max.norm = 100*m, min.lambda = (if (m >= n) 1e-6 else 0), max.vars = Inf, max.arclength = Inf, frac.arclength = 1, add.newvars = 1, bshoot.threshold = 0.1, relax.lambda = 1e-8, standardize = TRUE, function.precision = 3e-13, eps = .Machine$double.eps, trace = FALSE)
x |
matrix of features |
y |
response |
data |
a list consisting of x: a matrix of features and y:
response. data is not needed if x and y are
input separately.
|
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 binomial , gaussian ,
or poisson . For each one, the canonical link function is
used; logit for binomial, identity for gaussian, and
log for poisson distribution. Default is binomial.
|
weight |
an optional vector of weights for observations |
offset |
an optional vector of offset. If a column of x is used as
offset, the corresponding column must be removed from x.
|
lambda2 |
regularization parameter for the L2 norm of the
coefficients. Default is 1e-5.
|
max.steps |
an optional bound for the number of steps to be taken. Default is
10 * min{nrow(x), ncol(x)}.
|
max.norm |
an optional bound for the L1 norm of the coefficients. Default is
100 * ncol(x).
|
min.lambda |
an optional (lower) bound for the size of λ. Default is
0 for ncol(x) < nrow(x) cases and 1e-6
otherwise.
|
max.vars |
an optional bound for the number of active variables. Default is
Inf.
|
max.arclength |
an optional bound for arc length (L1 norm) of a step. If
max.arclength is extremely small, an exact nonlinear path is
produced. Default is Inf.
|
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
frac.arclength is assigned some fraction between 0 and 1, the
step size is decreased by the factor of frac.arclength in arc
length. If frac.arclength=0.2, the step length is adjusted so
that the active set would change after five smaller steps. Either
max.arclength or frac.arclength can be used to force
the path to be more accurate. Default is 1.
|
add.newvars |
add.newvars candidate variables (that are currently not in
the active set) are used in the corrector step as potential active
variables. Default is 1.
|
bshoot.threshold |
If the absolute value of a coefficient is larger than
bshoot.threshold at the first corrector step it becomes
nonzero (therefore when λ is considered to have been
decreased too far), λ is increased again. i.e. A
backward distance in λ that makes the coefficient zero
is computed. Default is 0.1.
|
relax.lambda |
A variable joins the active set if |l'(β)| >
λ*(1-relax.lambda ). Default is 1e-8. If no
variable joins the active set even after many (>20) steps, the user
should increase relax.lambda to 1e-7 or 1e-6,
but not more than that. This adjustment is sometimes needed because
of the numerical precision/error propagation problems. In general,
the paths are less accurate with relaxed lambda.
|
standardize |
If TRUE, predictors are standardized to have a unit variance.
|
function.precision |
function.precision parameter used in the internal
solver. Default is 3e-13. The algorithm is faster, but less
accurate with relaxed, larger function precision.
|
eps |
an effective zero |
trace |
If TRUE, the algorithm prints out its progress.
|
This algorithm implements the predictor-corrector 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 log-likelihood) |
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 |
TRUE if the predictors were standardized before fitting
|
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, 659-677.
cv.glmpath, plot.glmpath, predict.glmpath, summary.glmpath
data(heart.data) attach(heart.data) fit.a <- glmpath(x, y, family=binomial) fit.b <- glmpath(x, y, family=gaussian) detach(heart.data)