hierNet.logistic.path: Fit a path of logistic hierNet models- lasso models with...
In pejovic/hierNet: A Lasso for Hierarchical Interactions

Description Usage Arguments Value Author(s) References See Also Examples

One of the main functions in the hierNet package. Fits a logistic path of hierNet models over different values of the regularization parameter. Calls hierNet.logistic, which builds a regression model with hierarchically constrained pairwise interactions. Required inputs are an x matrix of features (the columns are the features) and a y vector of values. Reasonably fast for moderate sized problems (100-200 variables). We are currently working on a alternate algorithm for large scale problems.

hierNet.logistic.path(x, y,
           lamlist = NULL, delta=1e-8, minlam = NULL, maxlam = NULL, flmin=.01, nlam = 20,
           diagonal = TRUE, strong = FALSE, aa = NULL, zz = NULL,
           stand.main = TRUE, stand.int = FALSE,
           rho = nrow(x), niter = 100, sym.eps = 0.001, 
           step = 1, maxiter = 2000, backtrack = 0.2, tol = 1e-05, trace = 0)

`x`	A matrix of predictors, where the rows are the samples and the columns are the predictors
`y`	A vector of observations equal to 0 or 1, where length(y) equals nrow(x)
`lamlist`	Optional vector of values of lambda (the regularization parameter). L1 penalty param is `lambda * (1-delta)`.
`delta`	Elastic Net parameter. Squared L2 penalty param is `lambda * delta`. Not a tuning parameter: Think of as fixed and small. Default 1e-8.
`minlam`	Optional minimum value for lambda
`maxlam`	Optional maximum value for lambda
`flmin`	Fraction of maxlam; minlam= flmin*maxlam. If computation is slow, try increasing flmin to focus on the sparser part of the path
`nlam`	Number of values of lambda to be tried
`diagonal`	Flag specifying whether to include "pure" quadratic terms, th_jjX_j^2, in the model. Default TRUE.
`stand.main`	Should main effects be standardized? Default TRUE
`stand.int`	Should interactions be standardized? Default FALSE
`strong`	Flag specifying strong hierarchy (TRUE) or weak hierarchy (FALSE). Default FALSE
`aa`	An optional argument, a list with results from a previous call
`zz`	An optional argument, a matrix whose columns are products of features, computed by the function compute.interactions.c
`rho`	ADMM parameter: tuning parameter (>0) for ADMM. If there are convergence problems, try decreasing `rho`. Default n.
`niter`	ADMM parameter: number of iterations
`sym.eps`	ADMM parameter Thresholding for symmetrizing with strong=TRUE
`step`	Stepsize for generalized gradient descent
`maxiter`	Maximum number of iterations for generalized gradient descent
`backtrack`	Backtrack parameter for generalized gradient descent
`tol`	Error tolerance parameter for generalized gradient descent
`trace`	Output option; trace=1 gives verbose output

`bp`	p by nlam matrix of estimated "positive part" main effects (p=#features)
`bn`	p by nlam matrix of estimated "negative part" main effects
`th`	p by p by nlam array of estimated interaction coefficients
`obj`	nlam values of objective function, one per lambda value
`lamlist`	Vector of values of lambda used
`mx`	p-vector of column means of x
`sx`	p-vector of column standard deviations of x
`my`	mean of y
`mzz`	column means of feature product matrix
`szz`	column standard deviations of feature product matrix

Jacob Bien and Robert Tibshirani

Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

hierNet,predict.hierNet, hierNet.cv

set.seed(12)
x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)
y=x[,1]+2*x[,2]+ x[,1]*x[,2]+3*rnorm(100)
y=1*(y>0)
fit=hierNet.logistic.path(x,y)
print(fit)