Return the null deviance, starting mu and lambda max values for initialization. For internal use only.
get_start(x, y, weights, family, intercept, is.offset, offset, exclude, vp,
alpha)Input matrix, of dimension nobs x nvars; each row is an
observation vector. If it is a sparse matrix, it is assumed to be unstandardized.
It should have attributes xm and xs, where xm(j) and
xs(j) are the centering and scaling factors for variable j respsectively.
If it is not a sparse matrix, it is assumed to be standardized.
Quantitative response variable.
Observation weights.
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.
(See family for details on family functions.)
Does the model we are fitting have an intercept term or not?
Is the model being fit with an offset or not?
Offset for the model. If is.offset=FALSE, this should be
a zero vector of the same length as y.
Indices of variables to be excluded from the model.
Separate penalty factors can be applied to each coefficient.
The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).
This function is called by glmnet.path for null deviance, starting mu
and lambda max values. It is also called by glmnet.fit when used
without warmstart, but they only use the null deviance and starting mu values.
When x is not sparse, it is expected to already by centered and scaled.
When x is sparse, the function will get its attributes xm and
xs for its centering and scaling factors.
Note that whether x is centered & scaled or not, the values of mu
and nulldev don't change. However, the value of lambda_max does
change, and we need xm and xs to get the correct value.