fits model with B_kj constrained to equal g(B_k) for constraint fn g, for a symmetric constraint with a discrete design

fits model with B_kj constrained to equal g(B_k) for constraint fn g, for a symmetric constraint with a discrete design

Usage

fit_null_discrete(
  Y,
  X,
  k_constr,
  j_constr,
  j_ref,
  trackB = FALSE,
  maxit = 5000,
  tol = 0.1,
  verbose = FALSE,
  ls_max = 20,
  ls_rho = 0.5,
  max_step = 1,
  constraint = "pseudohuber"
)

Arguments

Y

Y (with augmentations)

X

design matrix

k_constr

row index of B to constrain

j_constr

col index of B to constrain

j_ref

column index of convenience constraint

trackB

track value of beta across iterations and return? Default is FALSE.

maxit

maximum iterations. Default is 5000.

tol

tolerance for stopping criteria. Algorithm stops when the root mean of the norm of the score vector is less than the tolerance. Default is 0.01.

verbose

should the algorithm print updates for you? Default is FALSE.

ls_max

maximum number of iterations in the line search. Default is 20.

ls_rho

scaling factor in the line search. Default is 0.5.

max_step

step capping after the line sesarch. Default is 1.

constraint

What type of symmetric constraint do we have? Options are "mean" and "pseudohuber.

Value

A list containing elements B, k_constr, j_constr, niter and Bs. B is a matrix containing parameter estimates under the null (obtained by maximum likelihood on augmented observations Y), k_constr, and j_constr give row and column indexes of the parameter fixed to be equal to the constraint function \(g()\) under the null. niter is a scalar giving total number of outer iterations used to fit the null model, and Bs is a data frame containing values of B by iteration if trackB was set equal to TRUE (otherwise it contains a NULL value).