add_classification: Add a point symbol with classification to a 'plotly' object
In inbo/effectclass: Classification and Visualisation of Effects

add_classification

R Documentation

Add a point symbol with classification to a `plotly` object

Description

See classification() for an explication on how the classification is done.

Usage

add_classification(
  p,
  x = NULL,
  y = NULL,
  ...,
  data = NULL,
  inherit = TRUE,
  sd,
  lcl = NULL,
  ucl = NULL,
  threshold,
  reference = 0,
  prob = 0.9,
  size = 20,
  link = c("identity", "log", "logit"),
  detailed = TRUE,
  signed = TRUE,
  labels = class_labels(lang = "en", detailed = detailed, signed = signed),
  text = NULL,
  hoverinfo = "text",
  ref_label = "reference",
  ref_colour = "#C04384"
)

Arguments

`p`	a plotly object
`x`	the x variable.
`y`	the y variable.
`...`	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"))`).
`data`	A data frame (optional) or crosstalk::SharedData object.
`inherit`	inherit attributes from `plot_ly()`?
`sd`	the variable of the standard error on the link scale.
`lcl`	A vector of lower confidence limits.
`ucl`	A vector of upper confidence limits.
`threshold`	A vector of either 1 or 2 thresholds. A single threshold will be transformed into `reference + c(-abs(threshold), abs(threshold))`.
`reference`	The null hypothesis. Defaults to 0.
`prob`	The coverage of the confidence interval when calculated from the mean `y` and standard error `sd`. Note that the function assumes a normal distribution at the `link` scale.
`size`	Size of the point symbol.
`link`	the link between the natural scale and the link scale. Defaults to `"identity"`.
`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`.
`labels`	a vector of labels for the classification hover information. See `class_labels()` for inspiration.
`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.
`ref_label`	The label for the reference point. Will be used for the points where `is.na(sd)` or both `is.na(lcl)` and `is.na(ucl)`.
`ref_colour`	The colour for the reference point.

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)
  )

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