#' Confidence intervals around predicted values
#'
#' @inheritParams get_predicted
#' @param predictions A vector of predicted values (as obtained by
#'   `stats::fitted()`, `stats::predict()` or [get_predicted()]).
#' @param se Numeric vector of standard error of predicted values. If `NULL`,
#'   standard errors are calculated based on the variance-covariance matrix.
#' @inheritParams get_predicted
#'
#' @details
#' Typically, `get_predicted()` returns confidence intervals based on the standard
#' errors as returned by the `predict()`-function, assuming normal distribution
#' (`+/- 1.96 * SE`) resp. a Student's t-distribution (if degrees of freedom are
#' available). If `predict()` for a certain class does _not_ return standard
#' errors (for example, *merMod*-objects), these are calculated manually, based
#' on following steps: matrix-multiply `X` by the parameter vector `B` to get the
#' predictions, then extract the variance-covariance matrix `V` of the parameters
#' and compute `XVX'` to get the variance-covariance matrix of the predictions.
#' The square-root of the diagonal of this matrix represent the standard errors
#' of the predictions, which are then multiplied by the critical test-statistic
#' value (e.g., ~1.96 for normal distribution) for the confidence intervals.
#'
#' If `ci_type = "prediction"`, prediction intervals are calculated. These are
#' wider than confidence intervals, because they also take into account the
#' uncertainty of the model itself. Before taking the square-root of the
#' diagonal of the variance-covariance matrix, `get_predicted_ci()` adds the
#' residual variance to these values. For mixed models, `get_variance_residual()`
#' is used, while `get_sigma()^2` is used for non-mixed models.
#'
#' It is preferred to rely on standard errors returned by `get_predicted()` (i.e.
#' returned by the `predict()`-function), because these are more accurate than
#' manually calculated standard errors. Use `get_predicted_ci()` only if standard
#' errors are not available otherwise. An exception are Bayesian models or
#' bootstrapped predictions, where `get_predicted_ci()` returns quantiles of the
#' posterior distribution or bootstrapped samples of the predictions. These are
#' actually accurate standard errors resp. confidence (or uncertainty) intervals.
#'
#' @examplesIf require("boot") && require("datawizard") && require("bayestestR")
#' # Confidence Intervals for Model Predictions
#' # ------------------------------------------
#'
#' data(mtcars)
#'
#' # Linear model
#' # ------------
#' x <- lm(mpg ~ cyl + hp, data = mtcars)
#' predictions <- predict(x)
#' ci_vals <- get_predicted_ci(x, predictions, ci_type = "prediction")
#' head(ci_vals)
#' ci_vals <- get_predicted_ci(x, predictions, ci_type = "confidence")
#' head(ci_vals)
#' ci_vals <- get_predicted_ci(x, predictions, ci = c(0.8, 0.9, 0.95))
#' head(ci_vals)
#'
#' # Bootstrapped
#' # ------------
#' predictions <- get_predicted(x, iterations = 500)
#' get_predicted_ci(x, predictions)
#'
#' ci_vals <- get_predicted_ci(x, predictions, ci = c(0.80, 0.95))
#' head(ci_vals)
#' datawizard::reshape_ci(ci_vals)
#'
#' ci_vals <- get_predicted_ci(x,
#'   predictions,
#'   dispersion_method = "MAD",
#'   ci_method = "HDI"
#' )
#' head(ci_vals)
#'
#'
#' # Logistic model
#' # --------------
#' x <- glm(vs ~ wt, data = mtcars, family = "binomial")
#' predictions <- predict(x, type = "link")
#' ci_vals <- get_predicted_ci(x, predictions, ci_type = "prediction")
#' head(ci_vals)
#' ci_vals <- get_predicted_ci(x, predictions, ci_type = "confidence")
#' head(ci_vals)
#' @export
get_predicted_ci <- function(x, ...) {
  UseMethod("get_predicted_ci")
}




# General method ----------------------------------------------------------

#' @rdname get_predicted_ci
#' @export
get_predicted_ci.default <- function(x,
                                     predictions = NULL,
                                     data = NULL,
                                     se = NULL,
                                     ci = 0.95,
                                     ci_type = "confidence",
                                     ci_method = NULL,
                                     dispersion_method = "sd",
                                     vcov = NULL,
                                     vcov_args = NULL,
                                     verbose = TRUE,
                                     ...) {
  # validation check, if CI should be skipped
  if (is.null(ci)) {
    return(ci)
  }

  # default ci_method depends on the type of predictions and model
  if (is.null(ci_method)) {
    if ("iterations" %in% names(attributes(predictions))) {
      ci_method <- "quantile"
    } else {
      ci_method <- "wald"
    }
  }

  # If draws are present (bootstrapped or Bayesian)
  if ("iterations" %in% names(attributes(predictions))) {
    iter <- attributes(predictions)$iteration
    se <- .get_predicted_se_from_iter(iter = iter, dispersion_method)
    out <- .get_predicted_ci_from_iter(iter = iter, ci = ci, ci_method = ci_method)
    out <- cbind(se, out)
    # outcome is multinomial/ordinal/cumulative
    if (inherits(predictions, "data.frame") &&
      "Response" %in% colnames(predictions) &&
      "Row" %in% colnames(predictions)) {
      out <- cbind(predictions[, c("Row", "Response")], out)
    }
    return(out)
  }

  # Analytical solution
  # 1. Find appropriate interval function
  if (!is.null(se)) {
    ci_function <- .get_predicted_se_to_ci
  } else if (ci_type == "confidence" ||
    identical(get_family(x)$family, "gaussian") ||
    (!is.null(vcov) && is.matrix(vcov))) {
    # gaussian or CI
    se <- get_predicted_se(
      x,
      data = data,
      ci_type = ci_type,
      vcov = vcov,
      vcov_args = vcov_args,
      ci_method = ci_method,
      verbose = verbose,
      ...
    )
    ci_function <- .get_predicted_se_to_ci
  } else {
    se <- rep(NA, length(predictions))
    ci_function <- .get_predicted_pi_glm
  }

  # 2. Run it once or multiple times if multiple CI levels are requested
  if (is.null(ci)) {
    out <- data.frame(SE = se)
  } else if (length(ci) == 1) {
    out <- ci_function(
      x,
      predictions,
      ci = ci,
      se = se,
      ci_method = ci_method,
      data = data,
      verbose = verbose,
      ...
    )
  } else {
    out <- data.frame(SE = se)
    for (ci_val in ci) {
      temp <- ci_function(
        x,
        predictions,
        ci = ci_val,
        se = se,
        ci_method = ci_method,
        data = data,
        verbose = verbose,
        ...
      )
      temp$SE <- NULL
      names(temp) <- paste0(names(temp), "_", ci_val)
      out <- cbind(out, temp)
    }
  }

  out
}

#' @export
get_predicted_ci.mlm <- function(x, verbose = TRUE, ...) {
  if (verbose) {
    format_alert(paste0("Confidence intervals are not yet supported for models of class `", class(x)[1], "`."))
  }
  NULL
}



#' @export
get_predicted_ci.polr <- function(x,
                                  predictions = NULL,
                                  data = NULL,
                                  se = NULL,
                                  ci = 0.95,
                                  type = NULL,
                                  verbose = TRUE,
                                  ...) {
  ci_data <- NULL
  # add CI, if type = "probs"
  if (identical(type, "probs") && !is.null(data)) {
    # standard errors are assumed to be on the link-scale,
    # because they're are based on the vcov of the coefficients
    se <- get_predicted_se(x, data = data, verbose = verbose)
    if (!is.null(se)) {
      # predicted values are probabilities, so we back-transform to "link scale"
      # using qlogis(), and then calculate CIs on the link-scale and transform
      # back to probabilities using link-inverse.
      linv <- link_inverse(x)
      ci_data <- data.frame(
        Row = predictions$Row,
        Response = predictions$Response,
        CI_low = linv(stats::qlogis(predictions$Predicted) - stats::qnorm((1 + ci) / 2) * se),
        CI_high = linv(stats::qlogis(predictions$Predicted) + stats::qnorm((1 + ci) / 2) * se)
      )
    }
  }
  ci_data
}


#' @export
get_predicted_ci.multinom <- get_predicted_ci.mlm

#' @export
get_predicted_ci.bracl <- get_predicted_ci.mlm








## Convert to CI -----------

.get_predicted_se_to_ci <- function(x,
                                    predictions = NULL,
                                    se = NULL,
                                    ci = 0.95,
                                    ci_method = "wald",
                                    data = NULL,
                                    ...) {
  # TODO: Prediction interval for binomial: https://fromthebottomoftheheap.net/2017/05/01/glm-prediction-intervals-i/
  # TODO: Prediction interval for poisson: https://fromthebottomoftheheap.net/2017/05/01/glm-prediction-intervals-ii/

  dots <- list(...)

  # validation checks
  if (!is.null(se)) {
    se <- as.numeric(se)
  }
  if (is.null(predictions)) {
    return(data.frame(SE = se))
  }
  if (is.null(ci)) {
    # Same as predicted
    return(data.frame(CI_low = predictions, CI_high = predictions))
  }
  if (is.null(ci_method)) {
    ci_method <- "wald"
  }

  # data is required for satterthwaite
  if (isTRUE(ci_method %in% c("satterthwaite", "kr", "kenward", "kenward-roger"))) {
    dof <- .satterthwaite_kr_df_per_obs(x, type = ci_method, data = data)
  } else {
    dof <- get_df(x, type = .check_df_type(ci_method))
  }

  # Return NA
  if (is.null(se)) {
    se <- ci_low <- ci_high <- rep(NA, length(predictions))

    # Get CI
    # TODO: Does this cover all the model families?
  } else {
    # use `all()` because `dof` can be a vector with satterthwaite
    if (is.null(dof) || all(is.infinite(dof)) || find_statistic(x) == "z-statistic") {
      crit_val <- stats::qnorm(p = (1 + ci) / 2)
    } else {
      crit_val <- stats::qt(p = (1 + ci) / 2, df = dof)
    }

    if (length(predictions) != length(se)) {
      # multiple length?
      if (length(predictions) %% length(se) == 0) {
        # for multiple length, SE and predictions may match, could be intended?
        # could there be any cases where we have twice or x times the length of
        # predictions as standard errors?
        format_warning("Predictions and standard errors are not of the same length. Please check if you need the `data` argument.") # nolint
      } else {
        format_error("Predictions and standard errors are not of the same length. Please specify the `data` argument.")
      }
    }

    ci_low <- as.numeric(predictions - (se * crit_val))
    ci_high <- as.numeric(predictions + (se * crit_val))
  }

  data.frame(SE = se, CI_low = ci_low, CI_high = ci_high)
}


.satterthwaite_kr_df_per_obs <- function(x, type, data = NULL) {
  if (type %in% c("kr", "kenward")) {
    type <- "kenward-roger"
  }
  check_if_installed("lmerTest")
  type <- tools::toTitleCase(type) # lmerTest wants title case
  if (!inherits(data, "data.frame")) {
    format_error("The `data` argument should be a data frame.")
  }
  mm <- get_modelmatrix(x, data = data)
  out <- sapply( # nolint
    seq_len(nrow(mm)), function(i) {
      suppressMessages(
        lmerTest::contestMD(x, mm[i, , drop = FALSE], ddf = type)[["DenDF"]]
      )
    }
  )
  out
}


.get_predicted_se_to_ci_zeroinfl <- function(x,
                                             predictions = NULL,
                                             se = NULL,
                                             ci = 0.95,
                                             link_inv = NULL,
                                             ...) {
  # validation checks
  if (is.null(predictions)) {
    return(data.frame(SE = se))
  }

  if (is.null(ci)) {
    return(data.frame(CI_low = predictions, CI_high = predictions))
  } # Same as predicted

  # Return NA
  if (is.null(se)) {
    se <- ci_low <- ci_high <- rep(NA, length(predictions))

    # Get CI
    # TODO: Does this cover all the model families?
  } else {
    crit_val <- stats::qnorm(p = (1 + ci) / 2)

    if (length(predictions) != length(se)) {
      # multiple length?
      if (length(predictions) %% length(se) == 0) {
        # for multiple length, SE and predictions may match, could be intended?
        # could there be any cases where we have twice or x times the length of
        # predictions as standard errors?
        format_warning("Predictions and standard errors are not of the same length. Please check if you need the `data` argument.") # nolint
      } else {
        format_error("Predictions and standard errors are not of the same length. Please specify the `data` argument.")
      }
    }

    ci_low <- link_inv(predictions - (se * crit_val))
    ci_high <- link_inv(predictions + (se * crit_val))
  }

  data.frame(SE = se, CI_low = ci_low, CI_high = ci_high)
}




# Get PI ------------------------------------------------------------------

.get_predicted_pi_glm <- function(x, predictions, ci = 0.95, ...) {
  info <- model_info(x)
  linkfun <- link_function(x)
  linkinv <- link_inverse(x)
  alpha <- 1 - ci
  prob <- c(alpha / 2, 1 - alpha / 2)

  if (info$is_binomial) {
    p <- linkinv(predictions)
    ci_low <- stats::qbinom(prob[1], size = 1, prob = p)
    ci_high <- stats::qbinom(prob[2], size = 1, prob = p)
  } else if (info$is_poisson) {
    rate <- linkinv(predictions)
    ci_low <- stats::qpois(prob[1], lambda = rate)
    ci_high <- stats::qpois(prob[2], lambda = rate)
  }

  data.frame(
    CI_low = linkfun(ci_low),
    CI_high = linkfun(ci_high)
  )
}




# Interval helpers --------------------------------------------------------

.get_predicted_se_from_iter <- function(iter, dispersion_method = "SD") {
  reshaped_data <- as.data.frame(t(iter)) # Reshape

  # Dispersion
  if (is.character(dispersion_method)) {
    dispersion_method <- match.arg(tolower(dispersion_method), c("sd", "mad"))
    if (dispersion_method == "sd") {
      se <- apply(reshaped_data, 2, stats::sd)
    } else if (dispersion_method == "mad") {
      se <- apply(reshaped_data, 2, stats::mad)
    } else {
      format_error("`dispersion_method` argument not recognized.")
    }
  } else {
    se <- apply(reshaped_data, 2, dispersion_method)
  }
  data.frame(SE = se, row.names = seq_along(se))
}




.get_predicted_ci_from_iter <- function(iter, ci = 0.95, ci_method = "quantile") {
  # Interval
  ci_method <- match.arg(
    tolower(ci_method),
    c("quantile", "hdi", "eti", "spi", "bci", "satterthwaite", "normal", "wald")
  )

  if (ci_method == "quantile") {
    out <- data.frame(Parameter = seq_len(nrow(iter)))
    for (i in ci) {
      temp <- data.frame(
        CI_low = apply(iter, 1, stats::quantile, probs = (1 - i) / 2, na.rm = TRUE),
        CI_high = apply(iter, 1, stats::quantile, probs = (1 + i) / 2, na.rm = TRUE)
      )
      names(temp) <- paste0(c("CI_low_", "CI_high_"), i)
      out <- cbind(out, temp)
    }
    if (length(ci) == 1) names(out) <- c("Parameter", "CI_low", "CI_high")
  } else {
    check_if_installed(c("bayestestR", "datawizard"))
    out <- as.data.frame(bayestestR::ci(as.data.frame(t(iter)), ci = ci, method = ci_method))
    if (length(ci) > 1L) out <- datawizard::reshape_ci(out)
  }
  out$Parameter <- out$CI <- NULL
  row.names(out) <- NULL
  out
}
