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
.
1 2 
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

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 
... 
additional arguments passed to predict. 
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 
df 
if 
coef.genlasso
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