R: Compute the fused lasso solution path for a general graph, or...

fusedlasso {genlasso} | R Documentation

## Compute the fused lasso solution path for a general graph, or a 1d or 2d grid

### Description

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.

### Usage

    fusedlasso(y, X, D, graph, gamma = 0, approx = FALSE, maxsteps = 2000,
               minlam = 0, rtol = 1e-07, btol = 1e-07, eps = 1e-4,
    	   verbose = FALSE)
    fusedlasso1d(y, pos, X, gamma = 0, approx = FALSE, maxsteps = 2000,
                 minlam = 0, rtol = 1e-07, btol = 1e-07, eps = 1e-4,
    	     verbose = FALSE)
    fusedlasso2d(y, X, dim1, dim2, gamma = 0, approx = FALSE, maxsteps = 2000,
    	     minlam = 0, rtol = 1e-07, btol = 1e-07, eps = 1e-4,
    	     verbose = FALSE)

### Arguments

`y` | a numeric response vector. Alternatively, for `fusedlasso2d` with no matrix `X` passed, `y` can be a matrix (its dimensions corresponding to the underlying 2d grid). Note that when `y` is given as a vector in `fusedlasso2d`, with no `X` passed, it should be in column major order.
`pos` | only for `fusedlasso1d`, these are the optional positions of the positions in the 1d grid. If missing, the 1d grid is assumed to have unit spacing.
`X` | an optional matrix of predictor variables, with observations along the rows, and variables along the columns. If the passed `X` has more columns than rows, then a warning is given, and a small ridge penalty is added to the generalized lasso criterion before the path is computed. If `X` has less columns than rows, then its rank is not checked for efficiency, and (unlike the `genasso` function) a ridge penalty is not automatically added if it is rank deficient. Therefore, a tall, rank deficient `X` may cause errors.
`D` | only for `fusedlasso`, this is the penalty matrix, i.e., the oriented incidence matrix over the underlying graph (the orientation of each edge being arbitrary). Only one of `D` or `graph` needs to be specified.
`graph` | only for `fusedlasso`, this is the underlying graph as an `igraph` object from the `igraph` package. Only one of `D` or `graph` needs to be specified.
`dim1` | only for `fusedlasso2d`, this is the number of rows in the underlying 2d grid. If missing and `y` is given as a matrix, it is assumed to be the number of rows of `y`.
`dim2` | only for `fusedlasso2d`, this is the number of columns in the underlying 2d grid. If missing and `y` is given as a matrix, it is assumed to be the number of columns of `y`.
`gamma` | a numeric variable greater than or equal to 0, indicating the ratio of the two tuning parameters, one for the fusion penalty, and the other for the pure \ell_1 penalty. Default is 0. See "Details" for more information.
`approx` | a logical variable indicating if the approximate solution path should be used (with no dual coordinates leaving the boundary). Default is `FALSE`. Note that for the 1d fused lasso, with identity predicor matrix, this approximate path is the same as the exact solution path.
`maxsteps` | an integer specifying the maximum number of steps for the algorithm to take before termination. Default is 2000.
`minlam` | a numeric variable indicating the value of lambda at which the path should terminate. Default is 0.
`rtol` | a numeric variable giving the tolerance for determining the rank of a matrix: if a diagonal value in the R factor of a QR decomposition is less than R, in absolute value, then it is considered zero. Hence making rtol larger means being less stringent with determination of matrix rank. In general, do not change this unless you know what you are getting into! Default is 1e-7.
`btol` | a numeric variable giving the tolerance for accepting "late" hitting and leaving times: future hitting times and leaving times should always be less than the current knot in the path, but sometimes for numerical reasons they are larger; any computed hitting or leaving time larger than the current knot + btol is thrown away. Hence making btol larger means being less stringent withthe determination of hitting and leaving times. Again, in general, do not change this unless you know what you are getting into! Default is 1e-7.
`eps` | a numeric variable indicating the multiplier for the ridge penalty, in the case that `X` is wide (more columns than rows). If numeric problems occur, make `eps` larger. Default is 1e-4.
`verbose` | a logical variable indicating if progress should be reported after each knot in the path.

### Details

The fused lasso estimate minimizes the criterion

1/2 ∑_{i=1}^n (y_i - x_i^T β_i)^2 + λ ∑_{(i,j) \in E} |β_i - β_j| + γ \cdot λ ∑_{i=1}^p |β_i|,

where x_i is the ith row of the predictor matrix and E is the edge set of the underlying graph. The solution \hat{β} is computed as a function of the regularization parameter λ, for a fixed value of γ. The default is to set γ=0, which corresponds to pure fusion of the coefficient vector β. A choice γ>0 introduces both sparsity and fusion in the coefficient vector, with a higher value placing more priority on sparsity.

If the predictor matrix is the identity, and the primal solution path β is desired at several levels of the ratio parameter γ, it is much more efficient to compute the solution path once with γ=0, and then use soft-thresholding via the `softthresh` function.

Finally, for the image denoising problem, i.e., the fused lasso over a 2d grid with identity predictor matrix, it is easy to specify a huge graph with a seemingly small amount of data. For instance, running the 2d fused lasso (with identity predictor matrix) on an image at standard 1080p HD resolution yields a graph with over 2 million edges. Moreover, in image denoising problems—somewhat unlike most other applications of the fused lasso (and generalized lasso)—a solution is often desired near the dense end of the path (λ=0) as opposed to the regularized end (λ=∞). The dual path algorithm implemented by the `fusedlasso2d` function begins at the fully regularized end and works its way down to the dense end. For a problem with many edges (dual variables), if a solution at the dense is desired, then it must usually pass through a huge number knots in the path. Hence it is not advisable to run `fusedlasso2d` on image denoising problems of large scale, as the dual solution path is computationally infeasible. It should be noted that a faster algorithm for the 2d fused lasso solution path (when the predictor matrix is the identity), which begins at the dense end of the path, is available in the `flsa` package.

### Value

The function returns an object of class "fusedlasso", and subclass "genlasso". This is a list with at least following components:

`lambda` | values of lambda at which the solution path changes slope, i.e., kinks or knots.
`beta` | a matrix of primal coefficients, each column corresponding to a knot in the solution path.
`fit` | a matrix of fitted values, each column corresponding to a knot in the solution path.
`u` | a matrix of dual coefficients, each column corresponding to a knot in the solution path.
`hit` | a vector of logical values indicating if a new variable in the dual solution hit the box contraint boundary. A value of `FALSE` indicates a variable leaving the boundary.
`df` | a vector giving an unbiased estimate of the degrees of freedom of the fit at each knot in the solution path.
`y` | the observed response vector. Useful for plotting and other methods.
`completepath` | a logical variable indicating whether the complete path was computed (terminating the path early with the `maxsteps` or `minlam` options results in a value of `FALSE`).
`bls` | the least squares solution, i.e., the solution at lambda = 0. This can be `NULL` when `completepath` is `FALSE`.
`gamma` | the value of the lambda ratio.
`call` | the matched call.

### Author(s)

Taylor B. Arnold and Ryan J. Tibshirani

### References

Tibshirani, R. J. and Taylor, J. (2011), "The solution path of the generalized lasso", Annals of Statistics 39 (3) 1335–1371.

Arnold, T. B. and Tibshirani, R. J. (2014), "Efficient implementations of the generalized lasso dual path algorithm", arXiv: 1405.3222.

Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. and Knight, K. (2005), "Sparsity and smoothness via the fused lasso", Journal of the Royal Statistics Society: Series B 67(1), 91–108.

### See Also

`softthresh`, `genlasso`

### Examples

    # Fused lasso on a custom graph
    set.seed(0)
    edges = c(1,2,1,3,1,5,2,4,2,5,3,6,3,7,3,8,6,7,6,8)
    gr = graph(edges=edges,directed=FALSE)
    plot(gr)
    y = c(1,1,0,1,1,0,0,0) + rnorm(8,0.1)

    # Can either pass the graph object directly, or
    # first construct the penalty matrix, and then
    # pass this
    a1 = fusedlasso(y,graph=gr)
    D = getDgSparse(gr)
    a2 = fusedlasso(y,D=D)

    plot(a1,numbers=TRUE)

    ## Not run:
    # The 2d fused lasso with a predictor matrix X
    set.seed(0)
    dim1 = dim2 = 16
    p = dim1*dim2
    n = 300
    X = matrix(rnorm(n*p),nrow=n)
    beta0 = matrix(0,dim1,dim2)
    beta0[(row(beta0)-dim1/2)^2 + (col(beta0)-dim2/2)^2 <=
    (min(dim1,dim2)/3)^2] = 1
    y = X %*% as.numeric(beta0) + rnorm(n)

    # Takes about 30 seconds for the full solution path
    out = fusedlasso2d(y,X,dim1=dim1,dim2=dim2)

    # Grab the solution at 8 values of lambda over the path
    a = coef(out,nlam=8)

    # Plot these against the true coefficients
    par(mar=c(1,1,2,1),mfrow=c(3,3))
    cols = terrain.colors(30)
    zlim = range(c(range(beta0),range(a$beta)))
    image(beta0,col=cols,zlim=zlim,axes=FALSE)

    for (i in 1:8) {
      image(matrix(a$beta[,i],nrow=dim1),col=cols,zlim=zlim,
      axes=FALSE)
      mtext(bquote(lambda==.(sprintf("%.3f",a$lambda[i]))))
    }

    ## End(Not run)

[Package genlasso version 1.3 Index]