Fit a probabilistic index model

pim_fit(
  y,
  X,
  link = "logit",
  w = NULL,
  init = NULL,
  tol = sqrt(.Machine$double.eps),
  max.iter = 100,
  nleqslv.global = "none",
  trace = FALSE,
  test.nleqslv = FALSE,
  keep.data = FALSE
)

Arguments

y: numeric The outcome vector.
X: matrix The design matrix.
link: character The link function: "logit", or "probit".
w: numeric The weights, default is NULL.
init: numeric The initial guess of Newton's method.
tol: numeric The numeric tolerance of nleqslv().
max.iter: numeric The maximum iteration of Newton's method.
nleqslv.global: character The global strategy for Newton's method. See ?nleqslv::nleqslv.
trace: logical Show Newton's method iteration report if TRUE.
test.nleqslv: logical Test different global strategies for Newton's method if TRUE. See ?nleqslv::testnslv.
keep.data: logical Should the returned object keep original data?

Value

A list containing the estimated coefficients and their covaraince matrix. It also contains the diagnostics of nleqlsv() procedure. If keep.data is TRUE, then the inputs y, X, wwill also be returned.