R/get_predictions_mclogit.R

In ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs

get_predictions_mclogit <- function(model, fitfram, ci.lvl, model_class, value_adjustment, terms, vcov.fun, vcov.type, vcov.args, condition, ...) {
  # does user want standard errors?
  se <- !is.null(ci.lvl) && !is.na(ci.lvl) && is.null(vcov.fun)

  # compute ci, two-ways
  if (!is.null(ci.lvl) && !is.na(ci.lvl))
    ci <- (1 + ci.lvl) / 2
  else
    ci <- 0.975

  # degrees of freedom
  dof <- .get_df(model)
  tcrit <- stats::qt(ci, df = dof)

  # add response to new data
  resp <- insight::find_response(model, combine = FALSE)
  cn <- c(colnames(fitfram), resp)
  for (r in resp) {
    fitfram <- cbind(fitfram, 1)
  }
  colnames(fitfram) <- cn

  prdat <- stats::predict(
    model,
    newdata = fitfram,
    type = "response",
    se.fit = se,
    ...
  )

  # did user request standard errors? if yes, compute CI
  if (!is.null(vcov.fun)) {

    # copy predictions
    if ("fit" %in% names(prdat))
      fitfram$predicted <- as.vector(prdat$fit)
    else
      fitfram$predicted <- as.vector(prdat)

    se.pred <- .standard_error_predictions(
      model = model,
      prediction_data = fitfram,
      value_adjustment = value_adjustment,
      terms = terms,
      model_class = model_class,
      vcov.fun = vcov.fun,
      vcov.type = vcov.type,
      vcov.args = vcov.args,
      condition = condition
    )

    if (.check_returned_se(se.pred)) {
      se.fit <- se.pred$se.fit
      fitfram <- se.pred$prediction_data

      # CI
      fitfram$conf.low <- fitfram$predicted - tcrit * se.fit
      fitfram$conf.high <- fitfram$predicted + tcrit * se.fit

      # copy standard errors
      attr(fitfram, "std.error") <- se.fit
      attr(fitfram, "prediction.interval") <- attr(se.pred, "prediction_interval")
    } else {
      # CI
      fitfram$conf.low <- NA
      fitfram$conf.high <- NA
    }
  } else if (se) {
    # copy predictions
    fitfram$predicted <- prdat$fit

    # calculate CI
    fitfram$conf.low <- prdat$fit - tcrit * prdat$se.fit
    fitfram$conf.high <- prdat$fit + tcrit * prdat$se.fit

    # copy standard errors
    attr(fitfram, "std.error") <- prdat$se.fit

  } else {
    # copy predictions
    fitfram$predicted <- as.vector(prdat)

    # no CI
    fitfram$conf.low <- NA
    fitfram$conf.high <- NA
  }

  fitfram
}