| Title: | Path Algorithm for the General Fused Lasso Signal Approximator |
|---|---|
| Description: | Implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>. |
| Authors: | Holger Hoefling |
| Maintainer: | Holger Hoefling <[email protected]> |
| License: | GPL-2 |
| Version: | 1.5.5 |
| Built: | 2024-11-13 05:44:04 UTC |
| Source: | https://github.com/cran/flsa |
These functions are the main interface functions for calculating FLSA solutions
flsa(y, lambda1=0, lambda2=NULL, connListObj = NULL, splitCheckSize=1e+09, verbose=FALSE, thr = 1e-09, maxGrpNum=4*length(y)) flsaTopDown(y, lambda1=0, groups=1:length(y), lambda2=NULL) flsaGetSolution(solObj, lambda1=0, lambda2=NULL, dim=NULL)flsa(y, lambda1=0, lambda2=NULL, connListObj = NULL, splitCheckSize=1e+09, verbose=FALSE, thr = 1e-09, maxGrpNum=4*length(y)) flsaTopDown(y, lambda1=0, groups=1:length(y), lambda2=NULL) flsaGetSolution(solObj, lambda1=0, lambda2=NULL, dim=NULL)
y |
response variable; numeric |
lambda1 |
penalty parameter vector (non-negative) for the absolute values uf the coefficients; numeric |
lambda2 |
penalty parameter vector (non-negative) for the difference of certain coefficients; numeric |
groups |
Return solutions for which the given number of groups is present - solutions found exactly at the breakpoint |
connListObj |
an object specifying which differences are to be penalized by lambda2. If NULL, then the dimensionalty of y is being used. If y is a vector, the differences of neighbouring coefficients are penalized. If y is a matrix, differences of neighbouring coefficients in 2 dimensions are being penalized. For more information see |
splitCheckSize |
a parameter specifying from which size on, groups of variables are not being checked for breaking up; can be used to reduce computation time; may lead to inaccurate results |
solObj |
Solution object as returned by FLSA if lambda2=NULL |
dim |
dimensions how the result should be formatted for a specific lambda. Used to format the 2-dimensional FLSA as a matrix in the response. For this, just include the dimensions of |
verbose |
print status messages during fitting |
thr |
the error threshold used in the algorithm |
maxGrpNum |
if every step of the algorithm, a group with a higher number is generated; this limits the number of steps the algorithm can take |
flsa is the main function for calculate a flsa fit. If lambda2=NULL, then it returns an object that encodes the whole solution path. Solutions for specific values of lambda1 and lambda2 can then be obtained by using flsaGetSolution.
flsaTopDown calculates the solution of the 1-dimensional FLSA, starting at large values of lambda2. If only solutions for large values of lambda2 are needed, this is more efficient.
Holger Hoefling
library(flsa) # generate some artificial data, 1 and 2 dimensional y <- rnorm(100) y2Dim = matrix(rnorm(100), ncol=10) ### apply function flsa and get solution directly lambda2= 0:10/10 res <- flsa(y, lambda2=lambda2) res2Dim <- flsa(y2Dim, lambda2=lambda2) ### apply the function and get the solution later resSolObj <- flsa(y, lambda2=NULL) resSolObjTopDown <- flsaTopDown(y) resSolObj2Dim <- flsa(y2Dim, lambda2=NULL) res2 <- flsaGetSolution(resSolObj, lambda2=lambda2) ### here note that the solution object does not store that the input was 2 dimensional ### therefore, res2Dim does not give out the solution as a 2 ### dimensional matrix (unlike the direct version above) res2Dim2 <- flsaGetSolution(resSolObj2Dim, lambda2=lambda2)library(flsa) # generate some artificial data, 1 and 2 dimensional y <- rnorm(100) y2Dim = matrix(rnorm(100), ncol=10) ### apply function flsa and get solution directly lambda2= 0:10/10 res <- flsa(y, lambda2=lambda2) res2Dim <- flsa(y2Dim, lambda2=lambda2) ### apply the function and get the solution later resSolObj <- flsa(y, lambda2=NULL) resSolObjTopDown <- flsaTopDown(y) resSolObj2Dim <- flsa(y2Dim, lambda2=NULL) res2 <- flsaGetSolution(resSolObj, lambda2=lambda2) ### here note that the solution object does not store that the input was 2 dimensional ### therefore, res2Dim does not give out the solution as a 2 ### dimensional matrix (unlike the direct version above) res2Dim2 <- flsaGetSolution(resSolObj2Dim, lambda2=lambda2)
Describes the makeup of a connection list object
is.connListObj(obj)is.connListObj(obj)
obj |
the object to be tested |
A connection list object can be used to specifiy which differences in
fusedlasso or flsa functions are to be penalized. Here, it
is assumed that the n coefficients in the model are numbered from 0 to
n-1. The connection list object is a list of length n with each element
corresponding to one of the coefficients. The i-th element of the list
here corresponds to coefficient with number i-1. Each element of the
list is a vector of integers, naming the numbers of the coefficients to
which the coefficient corresponding to the current list element is
linked (i.e. the difference of the two coefficients is being penalized).
I.e., assume that value $j$ is a member of the list under list element
$i$. Then this means that coeffient $i-1$ and coefficient $j$ are being
penalized. To understand this, consider that R-lists when viewed in
C-code are being numbered starting with 0, not 1 and note that all
computation is being done in C-code.
Furthermore, the connection list object has class connListObj.
Also note that the vectors in the list are of type integer not numeric. An empty vector should be set to NULL.
Holger Hoefling
connList <- vector("list", 4) y <- 1:4 class(connList) = "connListObj" connList[[1]] = as.integer(c(1,2)) connList[[2]] = as.integer(c(0,3)) connList[[3]] = as.integer(c(3,0)) connList[[4]] = as.integer(c(2,1)) names(connList) <- as.character(0:3) ## not necessary, just for illustration res <- flsa(y, connListObj=connList) res2 <- flsa(matrix(y, nrow=2)) res$BeginLambda res2$BeginLambda flsaGetSolution(res, lambda2=c(0, 0.5, 1)) flsaGetSolution(res2, lambda2=c(0, 0.5, 1))connList <- vector("list", 4) y <- 1:4 class(connList) = "connListObj" connList[[1]] = as.integer(c(1,2)) connList[[2]] = as.integer(c(0,3)) connList[[3]] = as.integer(c(3,0)) connList[[4]] = as.integer(c(2,1)) names(connList) <- as.character(0:3) ## not necessary, just for illustration res <- flsa(y, connListObj=connList) res2 <- flsa(matrix(y, nrow=2)) res$BeginLambda res2$BeginLambda flsaGetSolution(res, lambda2=c(0, 0.5, 1)) flsaGetSolution(res2, lambda2=c(0, 0.5, 1))