Add a fan plot to a plotly object — add

A fan plot consist of a set of transparent ribbons each representing a different coverage of the uncertainty around an estimate. The coverages are based on the assumption of a normal distribution with mean link(y) and standard error sd.

add_fan(
  p,
  x = NULL,
  y = NULL,
  ...,
  sd,
  link = c("identity", "log", "logit"),
  max_prob = 0.9,
  step = 0.05,
  fillcolor = coarse_unsigned_palette[2],
  data = NULL,
  inherit = TRUE,
  text = NULL,
  hoverinfo = "text",
  name
)

Arguments

p: a plotly object
x: the x variable.
y: the variable median on the natural scale.
...: Arguments (i.e., attributes) passed along to the trace type. See schema() for a list of acceptable attributes for a given trace type (by going to traces -> type -> attributes). Note that attributes provided at this level may override other arguments (e.g. plot_ly(x = 1:10, y = 1:10, color = I("red"), marker = list(color = "blue"))).
sd: the variable of the standard error on the link scale.
link: the link between the natural scale and the link scale. Defaults to "identity".
max_prob: The coverage of the widest band. Defaults to 0.9.
step: the step size between consecutive bands. The function adds all bands with coverage max_prob - i * step for all positive integer values i resulting in a positive coverage. Defaults to 0.05.
fillcolor: The fill colour of the fan. Defaults to a greyish blue.
data: A data frame (optional) or crosstalk::SharedData object.
inherit: inherit attributes from plot_ly()?
text: textual labels.
hoverinfo: Which hover information to display. Defaults to "text". When no "text" variable is specified, the function displays a formatted confidence interval.
name: Optional name of the trace for the legend.

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
library(plotly)
plot_ly(z, x = ~x, y = ~estimate) |>
  add_fan(sd = ~sd, text = ~display) |>
  add_classification(lcl = ~lcl, ucl = ~ucl, threshold = 1) |>
  layout(
    hovermode = "x unified",
    shapes = reference_shape(threshold = 1),
    annotations = reference_text(threshold = 1)
  )

plot_ly(z, x = ~x, y = ~estimate) |>
  add_fan(sd = ~sd, step = 0.1, text = ~display) |>
  add_classification(
    lcl = ~lcl, ucl = ~ucl, threshold = 1, detailed = FALSE
   ) |>
  layout(
    shapes = reference_shape(threshold = 1, line = TRUE),
    annotations = reference_text(threshold = 1)
   )

plot_ly(z, x = ~x, y = ~estimate) |>
  add_fan(sd = ~sd, step = 0.2, hoverinfo = "none") |>
  add_classification(
    lcl = ~lcl, ucl = ~ucl, threshold = 1, signed = FALSE
  ) |>
  layout(shapes = reference_shape(threshold = 1))

plot_ly(z, x = ~x, y = ~estimate) |>
  add_fan(sd = ~sd, step = 0.3) |>
  add_classification(
    lcl = ~lcl, ucl = ~ucl, threshold = 1, detailed = FALSE, signed = FALSE,
    text = ~display
  ) |>
  layout(
    shapes = reference_shape(threshold = 1, line = TRUE)
  )


# trend
plot_ly(data = trend, x = ~year, y = ~index) |>
  add_fan(sd = ~sd, text = ~display, hoverinfo = "text") |>
  add_classification(sd = ~sd, threshold = th) |>
  layout(
    hovermode = "x unified", hoverdistance = 1,
    shapes = reference_shape(threshold = th, reference = ref),
    annotations = reference_text(threshold = th, reference = ref)
  )

Add a fan plot to a `plotly` object

Arguments

See also

Examples