R/lqa.control.R

In CHEMIST: Causal Inference with High-Dimensional Error-Prone Covariates and Misclassified Treatments

lqa.control <-
  function (x = NULL, var.eps = .Machine$double.eps, max.steps = 5000, conv.eps = 1e-03, conv.stop = TRUE, c1 = 1e-08, digits = 5, ...)
  {
    ###  x = NULL  ... object of class 'lqa'. This optional argument is just included to be in line with the S3 class concept
    ###  var.eps   ... tolerance in checking for zero variance of some regressors
    ###  max.steps ... maximum number of steps in the P-IRLS respective Boosting algorithms
    ###  conv.eps  ... tolerance for convergence break in parameter updating
    ###  conv.stop ... whether or not to stop the iterations when estimated coefficients are converged
    ###  digits    ... number of digits to round the tuning parameter candidates as names in the 'loss.array' (in function 'cv.lqa')
    ###  ...       .... further arguments

    if (!is.numeric (var.eps) || var.eps < 0)
      stop ("value of var.eps must be >= 0")

    max.steps <- as.integer (max.steps)
    digits <- as.integer (digits)

    if (max.steps < 0)
      stop ("max.steps must be positive integer")

    if (!is.numeric (conv.eps) || conv.eps <= 0)
      stop ("value of conv.eps must be > 0")

    if (!is.logical (conv.stop))
      stop ("conv.stop must be 'TRUE' or 'FALSE'")

    if (!is.numeric (c1) || c1 < 0)
      stop ("value of 'c1' must be >= 0")

    if (!is.numeric (digits) || digits < 0)
      stop ("value of 'digits' must be >= 0")

    list (var.eps = var.eps, max.steps = max.steps, conv.eps = conv.eps, conv.stop = conv.stop, digits = digits, c1 = c1)
  }