sprintr (version 0.9.0)

sprinter: Sparse Reluctant Interaction Modeling

Description

This is the main function that fits interaction models with a path of tuning parameters (for Step 3).

Usage

sprinter(x, y, num_keep = NULL, square = FALSE, lambda = NULL,
  nlam = 100, lam_min_ratio = ifelse(nrow(x) < ncol(x), 0.01, 1e-04))

Arguments

x

An n by p design matrix of main effects. Each row is an observation of p main effects.

y

A response vector of size n.

num_keep

Number of candidate interactions to keep in Step 2. If num_keep is not specified (as default), it will be set to [n / log n].

square

Indicator of whether squared effects should be fitted in Step 1. Default to be FALSE.

lambda

A user specified list of tuning parameter. Default to be NULL, and the program will compute its own lambda path based on nlam and lam_min_ratio.

nlam

The number of lambda values. Default value is 100.

lam_min_ratio

The ratio of the smallest and the largest values in lambda. The largest value in lambda is usually the smallest value for which all coefficients are set to zero. Default to be 1e-2 in the n < p setting.

Value

An object of S3 class "sprinter".

n

The sample size.

p

The number of main effects.

a0

Estimate of intercept.

coef

Estimate of regression coefficients.

idx

Indices of all main effects and interactions in Step 3.

fitted

Fitted response value. It is a n-by-nlam matrix, with each column representing a fitted response vector for a value of lambda.

lambda

The sequence of lambda values used.

call

Function call.

See Also

cv.sprinter

Examples

# NOT RUN {
set.seed(123)
n <- 100
p <- 200
x <- matrix(rnorm(n * p), n, p)
y <- x[, 1] - 2 * x[, 2] + 3 * x[, 1] * x[, 3] - 4 * x[, 4] * x[, 5] + rnorm(n)
mod <- sprinter(x = x, y = y)

# }