# predict.genlasso: Make predictions given a genlasso object In genlasso: Path algorithm for generalized lasso problems

## Description

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

 ```1 2``` ```## 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`.

`coef.genlasso`