rarefit: Fit the rare feature selection model

In rare: Linear Model with Tree-Based Lasso Regularization for Rare Features

Description

Fit the rare feature selection model proposed in Yan and Bien (2018):

min_{β, γ} 0.5 * ||y - Xβ - β_01_n||_2^2 + λ * (α * ||γ_{-root}||_1 + (1-α) * ||β||_1)

using an alternating direction method of multipliers (ADMM) algorithm described in Algorithm 1 of the same paper. The regularization path is computed over a two-dimensional grid of regularization parameters: lambda and alpha. Of the two, lambda controls the overall amount of regularization, and alpha controls the tradeoff between sparsity and fusion of β (larger alpha induces more fusion in β).

Usage

rarefit(y, X, A = NULL, Q = NULL, hc, intercept = T, lambda = NULL,
  alpha = NULL, nlam = 50, lam.min.ratio = 1e-04, nalpha = 10,
  rho = 0.01, eps1 = 1e-06, eps2 = 1e-05, maxite = 1e+06)

Arguments

y	Length-nobs response variable.
X	nobs-by-nvars input matrix: each row is an observation vector and each column stores a count covariate.
A	nvars-by-nnodes binary matrix encoding ancestor-descendant relationships between leaves and tree nodes, where nnodes is the total number of tree nodes. A[i,j] is 1 if the ith leaf is a descendant of the jth node in the tree, and 0 otherwise. A should be in sparse matrix format (inherit from class sparseMatrix as in package Matrix). When A is NULL, the function will learn A from hc.
Q	(nvars+nnodes)-by-nnodes matrix with columns forming an orthonormal basis for the null space of [I_nvars:-A]. When Q is NULL, the function will learn Q using the singular value decomposition.
hc	An hclust tree of nvars leaves where each leaf corresponds to a covariate. If the tree is not an hclust object, user needs to provide the matrix A instead.
intercept	Whether intercept be fitted (default = TRUE) or set to zero (FALSE).
lambda	A user-supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlam and lam.min.ratio.
alpha	A user-supplied alpha sequence. If letting the program compute its own alpha sequence, a length-nalpha sequence of equally-spaced alpha values between 0 and 1 will be used. In practice, user may want to provide a more fine alpha sequence to tune the model to its best performance (e.g., alpha = c(1-exp(seq(0, log(1e-2), len = nalpha - 1)), 1)).
nlam	Number of lambda values (default = 50).
lam.min.ratio	Smallest value for lambda, as a fraction of lambda.max (i.e., the smallest value for which all coefficients are zero). The default value is 1e-4.
nalpha	Number of alpha values (default = 10).
rho	Penalty parameter for the quadratic penalty in the ADMM algorithm. The default value is 1e-2.
eps1	Convergence threshold in terms of the absolute tolerance level for the ADMMM algorithm. The default value is 1e-6.
eps2	Convergence threshold in terms of the relative tolerance level for the ADMM algorithm. The default value is 1e-5.
maxite	Maximum number of passes over the data for every pair of (lambda, alpha). The default value is 1e6.

Details

The function splits model fitting path by alpha. At each alpha value, the model is fit on the entire sequence of lambda with warm start. We recommend including an intercept (by setting intercept=T) unless the input data have been centered.

Value

Returns regression coefficients for beta and gamma and intercept beta0. We use a matrix-nested-within-list structure to store the coefficients: each list item corresponds to an alpha value; matrix (or vector) in that list item stores coefficients at various lambda values by columns (or entries).

beta0	Length-nalpha list with each item storing intercept across various lambda in a vector: beta0[[j]][i] is intercept fitted at (lambda[i], alpha[j]). If intercept = FALSE, beta0 is NULL.
beta	Length-nalpha list with each item storing beta coefficient at various lambda in columns of a nvars-by-nlam matrix: beta[[j]][, i] is beta coeffcient fitted at (lambda[i], alpha[j]).
gamma	Length-nalpha list with each item storing gamma coefficient at various lambda in columns of a nnodes-by-nlam matrix: gamma[[j]][, i] is gamma coeffcient vector fitted at (lambda[i], alpha[j]). If alpha[j] = 0, the problem becomes the lasso on beta and is solved with glmnet on beta, in which case gamma[[j]] = NA.
lambda	Sequence of lambda values used in model fit.
alpha	Sequence of alpha values used in model fit.
A	Binary matrix encoding ancestor-descendant relationship between leaves and nodes in the tree.
Q	Matrix with columns forming an orthonormal basis for the null space of [I_nvars:-A].
intercept	Whether an intercept is included in model fit.

References

Yan, X. and Bien, J. (2018) Rare Feature Selection in High Dimensions, https://arxiv.org/abs/1803.06675.

See Also

rarefit.cv, rarefit.predict

Examples

## Not run: 
# See vignette for more details.
set.seed(100)
ts <- sample(1:length(data.rating), 400) # Train set indices
# Fit the model on train set
ourfit <- rarefit(y = data.rating[ts], X = data.dtm[ts, ], hc = data.hc, lam.min.ratio = 1e-6,
                  nlam = 20, nalpha = 10, rho = 0.01, eps1 = 1e-5, eps2 = 1e-5, maxite = 1e4)

## End(Not run)

rare documentation built on May 1, 2019, 9:17 p.m.