R/get_predictions_gam2.R

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

get_predictions_Gam <- function(model, fitfram, ci.lvl, linv, value_adjustment, terms, model_class, condition, ...) {
  se <- !is.null(ci.lvl) && !is.na(ci.lvl)

  # 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)

  prdat <- stats::predict(
    model,
    newdata = fitfram,
    type = "link",
    ## TODO currently not supported
    se.fit = FALSE
  )

  # copy predictions
  fitfram$predicted <- linv(as.vector(prdat))

  # did user request standard errors? if yes, compute CI
  if (se) {
    se.pred <- .standard_error_predictions(
      model = model,
      prediction_data = fitfram,
      value_adjustment = value_adjustment,
      terms = terms,
      model_class = model_class,
      condition = condition
    )

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

      # calculate CI
      fitfram$conf.low <- linv(as.vector(prdat) - tcrit * se.fit)
      fitfram$conf.high <- linv(as.vector(prdat) + tcrit * se.fit)

      # copy standard errors
      attr(fitfram, "std.error") <- se.fit
    } else {
      # no CI
      fitfram$conf.low <- NA
      fitfram$conf.high <- NA
    }
  } else {
    # no CI
    fitfram$conf.low <- NA
    fitfram$conf.high <- NA
  }

  fitfram
}