holiglm (version 1.0.1)

lower: Lower Bound

Description

Set a lower bound on the coefficients of specific covariates.

Usage

lower(kvars)

Value

A holistic generalized model constraint, object inheriting from class "hglmc".

Arguments

kvars

a named vector giving the lower bounds. The names should correspond to the names of the covariates in the model matrix.

References

McDonald, J. W., & Diamond, I. D. (1990). On the Fitting of Generalized Linear Models with Nonnegativity Parameter Constraints. Biometrics, 46 (1): 201–206. tools:::Rd_expr_doi("10.2307/2531643")

Slawski, M., & Hein, M. (2013). Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization. Electronic Journal of Statistics, 7: 3004-3056. tools:::Rd_expr_doi("10.1214/13-EJS868")

See Also

Other Constraint-Constructors: group_equal(), group_inout(), group_sparsity(), include(), k_max(), linear(), pairwise_sign_coherence(), rho_max(), sign_coherence(), upper()

Examples

set.seed(0)
dat <- rhglm(100, c(1, 2, -3, 4, 5, -6))
constraints <- lower(c(x2 = 0, x5 = 1))
hglm(y ~ ., constraints = constraints, data = dat)

# non-negative least squares
dat <- rhglm(100, c(1, 2, -3, 4, 5, -6))
constraints <- lower(setNames(double(5), paste0("x", 1:5)))
hglm(y ~ ., constraints = constraints, data = dat)