R/stat-smooth-methods.R

In tidyverse/ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics

# Prediction data frame
# Get predictions with standard errors into data frame
#
# @keyword internal
# @alias predictdf.default
# @alias predictdf.glm
# @alias predictdf.loess
# @alias predictdf.locfit
predictdf <- function(model, xseq, se, level) UseMethod("predictdf")

#' @export
predictdf.default <- function(model, xseq, se, level) {
  pred <- stats::predict(
    model,
    newdata = data_frame0(x = xseq),
    se.fit = se,
    level = level,
    interval = if (se) "confidence" else "none"
  )

  if (se) {
    fit <- as.data.frame(pred$fit)
    names(fit) <- c("y", "ymin", "ymax")
    base::data.frame(x = xseq, fit, se = pred$se.fit)
  } else {
    base::data.frame(x = xseq, y = as.vector(pred))
  }
}

#' @export
predictdf.glm <- function(model, xseq, se, level) {
  pred <- stats::predict(
    model,
    newdata = data_frame0(x = xseq),
    se.fit = se,
    type = "link"
  )

  if (se) {
    std <- stats::qnorm(level / 2 + 0.5)
    base::data.frame(
      x = xseq,
      y = model$family$linkinv(as.vector(pred$fit)),
      ymin = model$family$linkinv(as.vector(pred$fit - std * pred$se.fit)),
      ymax = model$family$linkinv(as.vector(pred$fit + std * pred$se.fit)),
      se = as.vector(pred$se.fit)
    )
  } else {
    base::data.frame(x = xseq, y = model$family$linkinv(as.vector(pred)))
  }
}

#' @export
predictdf.loess <- function(model, xseq, se, level) {
  pred <- stats::predict(
    model,
    newdata = data_frame0(x = xseq),
    se = se
  )

  if (se) {
    y <- pred$fit
    ci <- pred$se.fit * stats::qt(level / 2 + .5, pred$df)
    ymin <- y - ci
    ymax <- y + ci
    base::data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
  } else {
    base::data.frame(x = xseq, y = as.vector(pred))
  }
}

#' @export
predictdf.locfit <- function(model, xseq, se, level) {
  pred <- stats::predict(
    model,
    newdata = data_frame0(x = xseq),
    se.fit = se
  )

  if (se) {
    y <- pred$fit
    ci <- pred$se.fit * stats::qt(level / 2 + .5, model$dp["df2"])
    ymin <- y - ci
    ymax <- y + ci
    base::data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
  } else {
    base::data.frame(x = xseq, y = as.vector(pred))
  }
}