These functions produce the solution path for a general fused lasso problem. The fusedlasso function takes either a penalty matrix or a graph object from the igraph package. The fusedlasso1d and fusedlasso2d functions are convenience functions that construct the penalty matrix over a 1d or 2d grid.
These are utility functions for creating penalty matrices for the fused lasso and trend filtering problems. Most users will not need to explicitly construct these as they are created internally by the fusedlasso or trendfilter functions. The sparse variants output sparse matrices, which should be used whenever possible because of a significant savings in both construction speed and memory usage.
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.
The function plot.genlasso produces a plot of the coordinate paths for objects of class "genlasso". This can be helpful for visualizing the full solution path for small problems; however, for moderate or large problems, the plot produced can be quite dense and difficult to interpret. The function plot.trendfilter applies to objects of class "trendfilter", and plots trend filtering coefficients at a single value of lambda (or multiple values, as specified by the user) as a function of the input positions (which, recall, are assumed to be evenly spaced if not specified). The function plot.cv.trendfilter plots the output of cv.trendfilter.
This function computes the solution path for the trend filtering problem of an arbitrary polynomial order. When the order is set to zero, trend filtering is equivalent to the 1d fused lasso, see fusedlasso1d.
Given an incomplete genlasso path object, this function continues the path computation from the last computed knot, either until the complete path has been computed or the step limit specified by moresteps has been reached. All options are assumed to be the same as those in the initial call to a genlasso function (as in genlasso, fusedlasso, or trendfilter), with the exception of minlam and verbose, which can be changed with a call to iterate.
This function performs k-fold cross-validation to choose the value of the regularization parameter lambda for a trend filtering problem, given the computed solution path. This function only applies to trend filtering objects with identity predictor matrix (no X passed).
This function computes the solution path of the generalized lasso problem for an arbitrary penalty matrix. Speciality functions exist for the trend filtering and fused lasso problems; see trendfilter and fusedlasso.
This function extracts coefficients from a generalized lasso solution path object, for any set of tuning parameter values along the path. It can return dual coefficients. The requested coefficients can also be parametrized by degrees of freedom value instead of tuning parameter value.
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