R/mirt_wrapper.R

In Pflegermeister/wizirt: A United API for IRT

irt_mirt <- function(data,
                     rownames = NULL,
                     item_type = "Rasch",
                     irt_pars = TRUE,
                     tol = 1e-5,
                     abs_fit = T) {
  if (is.null(rownames)) rownames <- seq_len(nrow(data))
  out <- list(
    data = NULL,
    model = list(
      engine = list(
        pkg = NULL,
        ver = NULL,
        func = NULL,
        call = NULL
      ),
      n_factors = NULL,
      item_type = NULL
    ),
    estimation = list(
      convergence = NULL,
      method = NULL,
      criteria = NULL,
      iterations = NULL,
      log_lik = NULL,
      abs_fit = NULL,
      df = get_df(item_type, data)
    ),
    parameters = list(
      coefficients = NULL,
      persons = NULL
    ),
    original_object = NULL
  )
  # Running mirt
  if (item_type == "1PL") {
    model <- mirt::mirt.model(glue::glue(
      "F1 = 1-{ncol(data)} \n",
      "CONSTRAIN =(1-{ncol(data)},a1)"
    ))
    mirt_model <- mirt::mirt(data,
      model = model, itemtype = "2PL",
      verbose = F, TOL = tol, SE = T
    )
  } else if (item_type %in% c("Rasch", "2PL", "3PL")) {
    mirt_model <- mirt::mirt(data,
      model = 1, itemtype = item_type,
      verbose = F, TOL = tol, SE = T
    )
  }

  # Standardizing mirt

  out$data <- tibble::as_tibble(mirt_model@Data$data)

  out$model$engine$pkg <- "mirt"
  out$model$engine$ver <- packageVersion("mirt")
  out$model$engine$func <- "mirt"
  out$model$engine$call <- glue::glue(gsub(
    "tol", "{tol}",
    gsub(
      "item_type", '"{item_type}"',
      paste0(trimws(capture.output(mirt_model@Call)),
        collapse = " "
      )
    )
  ))
  out$model$engine$call <- paste(trimws(capture.output(out$model$engine$call)), collapse = "")
  out$model$n_factors <- 1
  out$model$item_type <- item_type

  out$estimation$convergence <- mirt_model@OptimInfo$converged
  out$estimation$method <- mirt_model@Options$method
  out$estimation$log_lik <- mirt_model@Fit$logLik
  out$estimation$iterations <- mirt::extract.mirt(mirt_model, "iterations")
  out$estimation$criteria <- mirt_model@Options$TOL

  if(abs_fit){
    if (nrow(na.omit(data)) != nrow(data)){
      message ("Missing data removed rowise to estimate absolute fit statistics:")
      out$estimation$abs_fit <- mirt::M2(mirt_model, na.rm = T)
    } else {
      out$estimation$abs_fit <- mirt::M2(mirt_model)
    }
  }

  out$original_object <- mirt_model

  out$parameters$coefficients <- data.frame(mirt::coef(mirt_model, simplify = T, IRTpars = irt_pars)$items)[, c(2, 1, 3)] %>%
    `colnames<-`(c("difficulty", "discrimination", "guessing")) %>%
    tibble::rownames_to_column("item") %>%
    tibble::tibble()

  out$parameters$persons <- dplyr::rename(tibble::as_tibble(mirt::fscores(mirt_model, full.scores.SE = T)),
                                          ability = "F1", std_err = "SE_F1") %>%
    dplyr::mutate(ids = rownames)

  out
}