glmnet.path {glmnet}  R Documentation 
Fit a GLM with elastic net regularization for a path of lambda values
Description
Fit a generalized linear model via penalized maximum likelihood for a path of lambda values. Can deal with any GLM family.
Usage
glmnet.path(
x,
y,
weights = NULL,
lambda = NULL,
nlambda = 100,
lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e04),
alpha = 1,
offset = NULL,
family = gaussian(),
standardize = TRUE,
intercept = TRUE,
thresh = 1e10,
maxit = 1e+05,
penalty.factor = rep(1, nvars),
exclude = integer(0),
lower.limits = Inf,
upper.limits = Inf,
trace.it = 0
)
Arguments
x 
Input matrix, of dimension 
y 
Quantitative response variable. 
weights 
Observation weights. Default is 1 for each observation. 
lambda 
A user supplied lambda sequence. Typical usage is to have the
program compute its own lambda sequence based on 
nlambda 
The number of lambda values, default is 100. 
lambda.min.ratio 
Smallest value for lambda as a fraction of lambda.max,
the (data derived) entry value (i.e. the smallest value for which all
coefficients are zero). The default depends on the sample size 
alpha 
The elasticnet mixing parameter, with

offset 
A vector of length 
family 
A description of the error distribution and link function to be
used in the model. This is the result of a call to a family function. Default
is 
standardize 
Logical flag for x variable standardization, prior to
fitting the model sequence. The coefficients are always returned on the
original scale. Default is 
intercept 
Should intercept be fitted (default=TRUE) or set to zero (FALSE)? 
thresh 
Convergence threshold for coordinate descent. Each inner
coordinatedescent loop continues until the maximum change in the objective
after any coefficient update is less than thresh times the null deviance.
Default value is 
maxit 
Maximum number of passes over the data; default is 
penalty.factor 
Separate penalty factors can be applied to each
coefficient. This is a number that multiplies 
exclude 
Indices of variables to be excluded from the model. Default is none. Equivalent to an infinite penalty factor. 
lower.limits 
Vector of lower limits for each coefficient; default

upper.limits 
Vector of upper limits for each coefficient; default

trace.it 
Controls how much information is printed to screen. Default is

Details
glmnet.path
solves the elastic net problem for a path of lambda values.
It generalizes glmnet::glmnet
in that it works for any GLM family.
Sometimes the sequence is truncated before nlambda
values of lambda
have been used. This happens when glmnet.path
detects that the decrease
in deviance is marginal (i.e. we are near a saturated fit).
Value
An object with class "glmnetfit" and "glmnet".
a0 
Intercept sequence of length 
beta 
A 
df 
The number of nonzero coefficients for each value of lambda. 
dim 
Dimension of coefficient matrix. 
lambda 
The actual sequence of lambda values used. When alpha=0, the largest lambda reported does not quite give the zero coefficients reported (lambda=inf would in principle). Instead, the largest lambda for alpha=0.001 is used, and the sequence of lambda values is derived from this. 
dev.ratio 
The fraction of (null) deviance explained. The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat  loglike), where loglike_sat is the loglikelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1dev/nulldev. 
nulldev 
Null deviance (per observation). This is defined to be 2*(loglike_sat loglike(Null)). The null model refers to the intercept model. 
npasses 
Total passes over the data summed over all lambda values. 
jerr 
Error flag, for warnings and errors (largely for internal debugging). 
offset 
A logical variable indicating whether an offset was included in the model. 
call 
The call that produced this object. 
family 
Family used for the model. 
nobs 
Number of observations. 
Examples
set.seed(1)
x < matrix(rnorm(100 * 20), nrow = 100)
y < ifelse(rnorm(100) > 0, 1, 0)
# binomial with probit link
fit1 < glmnet:::glmnet.path(x, y, family = binomial(link = "probit"))