This predict method for the genlasso class makes a prediction for the
fitted values at new predictor measurements. Hence it is really only
useful when the generalized lasso model has been fit with a
nonidentity predictor matrix. In the case that the predictor matrix
is the identity, it does the same thing as coef.genlasso.
Usage
## S3 method for class 'genlasso'
predict(object, lambda, nlam, df, Xnew, ...)
Arguments
object
object of class "genlasso", or an object which inherits this class
(i.e., "fusedlasso", "trendfilter").
lambda
a numeric vector of tuning parameter values at which coefficients
should be calculated. The user can choose to specify one of
lambda, nlam, or df; if none are specified,
then coefficients are returned at every knot in the solution path.
nlam
an integer indicating a number of tuning parameters values at which
coefficients should be calculated. The tuning parameter values are
then chosen to be equally spaced on the log scale over the first
half of the solution path (this is if the full solution path has
been computed; if only a partial path has been computed, the tuning
parameter values are spaced over the entirety of the computed path).
df
an integer vector of degrees of freedom values at which coefficients
should be calculated. In the case that a single degrees of freedom
value appears multiple times throughout the solution path, the least
regularized solution (corresponding to the smallest value
of lambda) is chosen. If a degrees of freedom value does not appear
at all in the solution path, the least regularized solution at which
this degrees of freedom value is not exceeded is chosen.
Xnew
a numeric matrix X, containing new predictor measurements at which
predictions should be made. If missing, it is assumed to be
the same as the existing predictor measurements in object.
...
additional arguments passed to predict.
Value
Returns a list with the following components:
fit
a numeric matrix of predictor values, one column for each value of
lambda.
lambda
a numeric vector containing the sequence of tuning parameter values,
corresponding to the columns of fit.
df
if df was specified, an integer vector containing the
sequence of degrees of freedom values corresponding to the columns
of fit.