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)A matrix of predictors, where the rows are the samples and the columns are the predictors
A vector of observations equal to 0 or 1, where length(y) equals nrow(x)
Optional vector of values of lambda (the regularization parameter). L1 penalty param is lambda * (1-delta).
Elastic Net parameter. Squared L2 penalty param is lambda * delta. Not a tuning parameter: Think of as fixed and small. Default 1e-8.
Optional minimum value for lambda
Optional maximum value for lambda
Fraction of maxlam; minlam= flmin*maxlam. If computation is slow, try increasing flmin to focus on the sparser part of the path
Number of values of lambda to be tried
Flag specifying whether to include "pure" quadratic terms, th_jjX_j^2, in the model. Default TRUE.
Should main effects be standardized? Default TRUE
Should interactions be standardized? Default FALSE
Flag specifying strong hierarchy (TRUE) or weak hierarchy (FALSE). Default FALSE
An *optional* argument, a list with results from a previous call
An *optional* argument, a matrix whose columns are products of features, computed by the function compute.interactions.c
ADMM parameter: tuning parameter (>0) for ADMM. If there are convergence
problems, try decreasing rho. Default n.
ADMM parameter: number of iterations
ADMM parameter Thresholding for symmetrizing with strong=TRUE
Stepsize for generalized gradient descent
Maximum number of iterations for generalized gradient descent
Backtrack parameter for generalized gradient descent
Error tolerance parameter for generalized gradient descent
Output option; trace=1 gives verbose output
p by nlam matrix of estimated "positive part" main effects (p=#features)
p by nlam matrix of estimated "negative part" main effects
p by p by nlam array of estimated interaction coefficients
nlam values of objective function, one per lambda value
Vector of values of lambda used
p-vector of column means of x
p-vector of column standard deviations of x
mean of y
column means of feature product matrix
column standard deviations of feature product matrix
Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
# NOT RUN {
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)
# }
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