## File Name: select_weight.R
## File version: 1.0

#' Compute confounding weights for the PCBSM.
#'
#' This function computes weights to be included in a PCBSM as a covariate accounting for unobserved confounding. Obtained by extracting response residuals from a probit model with grp as dependent variable and confounders and instruments as independent variables.
#'
#' @param df data.frame containing the data
#' @param grp string containing the name of the column where the group membership variable is stored in df
#' @param X vector of strings containing the name of confounders to be included in the model
#' @param instr vector of strings containing the name of instrumental variables to be included in the model
#' @param type.res string containing the type of glm residuals to be used. Default is "deviance"
#' @param std boolean indicating whether residuals should be standardized. Default is TRUE
#' @return A vector of weights to be included in a PCBSM
#' @export


select_weight <- function(df=NULL,grp=NULL,X=NULL,instr=NULL,type.res="deviance",std=F) {
  formu <- paste0(grp,"~")
  formu2 <- paste(X,sep="+",collapse="+")
  formu3 <- paste(instr,sep="+",collapse="+")
  if (!is.null(instr)) {
    formu2 <- paste(formu2,formu3,sep="+")
  }
  if (is.null(X)) {
    formu2 <- formu3
  }
  formu <- paste(formu,formu2)
  logit_mod <- glm(formula = formu,data = df,family = binomial(link = "probit"))
  res <- residuals(logit_mod,type=type.res)
  if (std) {
    res <- rstandard(logit_mod,type='deviance')
  }
  return(res)
}