scale_effect: A scale for effect points

In inbo/effectclass: Classification and Visualisation of Effects

A scale for effect points

Description

A scale for effect points

Usage

scale_effect(
  ...,
  detailed = TRUE,
  signed = TRUE,
  fill = TRUE,
  colour = TRUE,
  drop = FALSE,
  labels = class_labels(lang = "en", detailed = detailed, signed = signed)
)

Arguments

...	Arguments passed on to ggplot2::scale_shape_manual values a set of aesthetic values to map data values to. The values will be matched in order (usually alphabetical) with the limits of the scale, or with breaks if provided. If this is a named vector, then the values will be matched based on the names instead. Data values that don't match will be given na.value. breaks One of: NULL for no breaks waiver() for the default breaks (the scale limits) A character vector of breaks A function that takes the limits as input and returns breaks as output na.value The aesthetic value to use for missing (NA) values
detailed	TRUE indicates a detailed classification(); FALSE a coarse_classification(). Defaults to TRUE.
signed	TRUE indicates a signed classification; FALSE a classification with remove_sign(). Defaults to TRUE.
fill	return ggplot2::scale_fill_manual()
colour	return ggplot2::scale_colour_manual()
drop	Drop unused levels. This is always FALSE. Changing this argument has no effect. We provide the argument to avoid errors in case the user sets the argument.
labels	the labels for the legend.

See Also

Other ggplot2 add-ons: stat_effect(), stat_fan()

Examples

# All possible classes
z <- data.frame(
  estimate = c(-0.5, 0, 0.5, 1.5, 1, 0.5, 0, -0.5, -1, -1.5),
  sd = c(rep(0.8, 3), rep(0.3, 7))
)
z$lcl <- qnorm(0.05, z$estimate, z$sd)
z$ucl <- qnorm(0.95, z$estimate, z$sd)
classification(z$lcl, z$ucl, threshold = 1) -> z$effect
c(
  "?" = "unknown\neffect", "?+" = "potential\npositive\neffect",
  "?-" = "potential\nnegative\neffect", "~" = "no effect",
  "+" = "positive\neffect", "-" = "negative\neffect",
  "+~" = "moderate\npositive\neffect", "-~" = "moderate\nnegative\neffect",
  "++" = "strong\npositive\neffect", "--" = "strong\nnegative\neffect"
)[as.character(z$effect)] -> z$x
z$x <- factor(z$x, z$x)
z$display <- paste(
  "estimate:", format_ci(z$estimate, lcl = z$lcl, ucl = z$ucl)
)

# Simulated trend
set.seed(20190521)
base_year <- 2000
n_year <- 20
trend <- data.frame(
  dt = seq_len(n_year),
  change = rnorm(n_year, sd = 0.2),
  sd = rnorm(n_year, mean = 0.1, sd = 0.01)
)
trend$index <- cumsum(trend$change)
trend$lcl <- qnorm(0.025, trend$index, trend$sd)
trend$ucl <- qnorm(0.975, trend$index, trend$sd)
trend$year <- base_year + trend$dt
trend$display <- paste(
  "index:", format_ci(trend$index, lcl = trend$lcl, ucl = trend$ucl)
)
th <- 0.25
ref <- 0
oldw <- getOption("warn")
options(warn = -1)
library(ggplot2)
theme_set(theme_grey(base_family = "Helvetica"))
update_geom_defaults("point", list(size = 5))
ggplot(z, aes(x = effect, y = estimate, ymin = lcl, ymax = ucl)) +
  stat_effect(threshold = 1) +
  coord_flip()
ggplot(z[3:5, ], aes(x = effect, y = estimate, ymin = lcl, ymax = ucl)) +
  stat_effect(threshold = 1, ref_line = "none") +
  coord_flip()
ggplot(z[3:5, ], aes(x = effect, y = estimate, ymin = lcl, ymax = ucl)) +
  stat_effect(threshold = 1, errorbar = FALSE) +
  coord_flip()

# plot indices
ggplot(trend, aes(x = year, y = index, ymin = lcl, ymax = ucl, sd = sd)) +
  geom_line() +
  stat_effect(threshold = th, reference = ref)

# plot pairwise differences
change_set <- function(z, base_year) {
  n_year <- max(z$dt)
  total_change <- lapply(
    seq_len(n_year) - 1,
    function(i) {
      if (i > 0) {
        y <- tail(z, -i)
      } else {
        y <- z
      }
      data.frame(
        from = base_year + i, to = base_year + y$dt,
        total = cumsum(y$change), sd = sqrt(cumsum(y$sd ^ 2))
      )
    }
  )
  total_change <- do.call(rbind, total_change)
  total_change <- rbind(
    total_change,
    data.frame(
      from = total_change$to, to = total_change$from,
      total = -total_change$total, sd = total_change$sd
    )
  )
  total_change$lcl <- qnorm(0.025, total_change$total, total_change$sd)
  total_change$ucl <- qnorm(0.975, total_change$total, total_change$sd)
  return(total_change)
}
head(trend, 10) |>
  change_set(base_year) |>
  ggplot(aes(x = from, y = to, ymin = lcl, ymax = ucl)) +
  stat_effect(
    threshold = th, reference = ref, aes(colour = total), ref_line = "none",
    errorbar = FALSE, shape_colour = FALSE
  ) +
  scale_colour_gradient2()
head(trend, 10) |>
  change_set(base_year) |>
  ggplot(aes(x = from, y = to, ymin = lcl, ymax = ucl)) +
  stat_effect(
    threshold = th, reference = ref, ref_line = "none", errorbar = FALSE
  )
options(warn = oldw)

inbo/effectclass documentation built on June 8, 2024, 2:43 a.m.