Package 'effectclass' reference manual

Title:	Classification and Visualisation of Effects
Description:	Classify effects by comparing the confidence intervals with thresholds.
Authors:	Thierry Onkelinx [aut, cre] (<https://orcid.org/0000-0001-8804-4216>, Research Institute for Nature and Forest (INBO)), Research Institute for Nature and Forest (INBO) [cph, fnd]
Maintainer:	Thierry Onkelinx <[email protected]>
License:	GPL-3
Version:	0.1.5
Built:	2024-12-01 04:26:04 UTC
Source:	https://github.com/inbo/effectclass

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"
)
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)
  )
# 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

Description

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.

Usage

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
)
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)
  )
# 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)
  )

Logarithmic breaks for changes

Description

Breaks a set of pretty breaks for changes.

Usage

change_breaks(n = 2, extra = NULL)
change_breaks(n = 2, extra = NULL)

Arguments

`n`	the number of breaks on either side of the reference
`extra`	An optional vector of additional breaks. The function always appends these breaks. Use this option when you want to force this values to be a part of the breaks.

Display logarithmic changes as percentage

Description

Display logarithmic changes as percentage

Usage

change_labels(x)
change_labels(x)

Arguments

`x`	the logarithmic changes

Return a standardised set of labels for the classification

Description

Return a standardised set of labels for the classification

Usage

class_labels(
  type = c("trend", "effect"),
  lang = c("en", "nl"),
  detailed = TRUE,
  signed = TRUE
)
class_labels(
  type = c("trend", "effect"),
  lang = c("en", "nl"),
  detailed = TRUE,
  signed = TRUE
)

Arguments

`type`	What type of effect. Currently available are `"trend"` and `"effect"`.
`lang`	The language. Currently available are `"en"` (English) and `"nl"` (Dutch). Defaults to `"en"`. Please contact the maintainer if you have suggestions for more languages.
`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`.

Classify Effects by Comparing the Confidence Intervals with a Reference and Thresholds

Description

⁠++⁠ strong positive effect: max(threshold) < lcl
+ positive effect: ⁠reference < lcl < max(threshold)⁠ and max(threshold) < ucl
⁠+~⁠ moderate positive effect: reference < lcl and ucl < max(threshold)
~ no effect: ⁠min(threshold) < lcl < reference⁠ and ⁠reference < ucl < max(threshold)⁠
⁠-~⁠ moderate negative effect: min(threshold) < lcl and ucl < reference
- negative effect: lcl < min(threshold) and ⁠min(threshold) < ucl < reference⁠
⁠--⁠ strong negative effect: ucl < min(threshold)
⁠?+⁠ potential positive effect: ⁠min(threshold) < lcl < reference⁠ and max(threshold) < ucl
⁠?-⁠ potential negative effect: lcl < min(threshold) and ⁠reference < ucl < max(threshold)⁠
⁠?⁠ unknown effect: lcl < min(threshold) and max(threshold) < ucl

Usage

classification(lcl, ucl, threshold, reference = 0)
classification(lcl, ucl, threshold, reference = 0)

Arguments

`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.

Use a Lower Scale Classification

Description

coarse_classification(y) reduces the 10 scales from y <- classification(x) to the 4 scales below.

+ positive effect: reference < lcl
~ no effect: ⁠min(threshold) < lcl < reference⁠ and ⁠reference < ucl < max(threshold)⁠
- negative effect: ucl < reference
⁠?⁠ unknown effect: lcl < min(threshold) or max(threshold) < ucl

coarse_classification(y) reduces the 6 scales from y <- remove_sign(classification(x)) into 3 scales.

Usage

coarse_classification(classification)
coarse_classification(classification)

Arguments

classification

The classification

Format an Estimate and Confidence Interval as Text

Description

The function rounds the estimate, lower and upper confidence interval to the same magnitude. The magnitude shows the width of the confidence interval with two significant digits.

Usage

format_ci(
  estimate,
  se,
  lcl,
  ucl,
  interval = 0.95,
  link = c("identity", "log", "logit"),
  max_digit = 4,
  percent = FALSE,
  sign = FALSE,
  change = FALSE
)
format_ci(
  estimate,
  se,
  lcl,
  ucl,
  interval = 0.95,
  link = c("identity", "log", "logit"),
  max_digit = 4,
  percent = FALSE,
  sign = FALSE,
  change = FALSE
)

Arguments

`estimate`	The estimate in the `link` scale.
`se`	The standard error in the `link` scale. If missing, you must provide values for `lcl` and `ucl`.
`lcl`	The lower confidence limit. Ignored when `se` is given.
`ucl`	The upper confidence limit. Ignored when `se` is given.
`interval`	The coverage of the confidence interval. Only used when `se` is given. Defaults to `0.95` (`⁠95%⁠`).
`link`	The transformation of `estimate`, `se`, `lcl` and `ucl`. The appropriate back transformation is applied before formatting.
`max_digit`	The maximum number of significant digits to display. Defaults to `4`.
`percent`	Display the interval as a percentage (= multiply by 100 and append `⁠%⁠`). Defaults to `FALSE`.
`sign`	Always add the sign to the text. (e.g. `+1` instead of `1`). Defaults to `FALSE`.
`change`	Display interval as a change. Subtract `1` after applying the `link` and before applying `percent`. Use it to display `⁠0.9 (0.85; 0.95)⁠` as `⁠-10% (-15%; -5%)⁠`. Defaults to `FALSE`. Implies `sign == TRUE`.

Examples

format_ci(0.512345, 1)
format_ci(0.512345, 1, interval = 0.9)
format_ci(0.512345, 1, link = "log")
format_ci(0.512345, 1, link = "logit")
format_ci(0.512345, 10)
format_ci(0.512345, 0.1)
format_ci(0.512345, 0.01)
format_ci(0.512345, 0.001)
format_ci(0.512345, 0.0001)
format_ci(0.512345, 0.00001)
format_ci(0.512345, 0.00001, max_digit = 10)
format_ci(0.512345, 0.5)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, percent = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, sign = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, percent = TRUE, sign = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234)
format_ci(0.512345e-6, 1e-6)
format_ci(0.512345e-7, 1e-7)
format_ci(0.512345e-7, 1e-8)
format_ci(0.512345e-7, 1e-9)
format_ci(0.512345, 0.1, link = "log", percent = TRUE, change = FALSE)
format_ci(0.512345, 0.1, link = "log", percent = TRUE, change = TRUE)
format_ci(0, lcl = 0, ucl = 0)
format_ci(1, lcl = 1, ucl = 1)
format_ci(0.512345, 1)
format_ci(0.512345, 1, interval = 0.9)
format_ci(0.512345, 1, link = "log")
format_ci(0.512345, 1, link = "logit")
format_ci(0.512345, 10)
format_ci(0.512345, 0.1)
format_ci(0.512345, 0.01)
format_ci(0.512345, 0.001)
format_ci(0.512345, 0.0001)
format_ci(0.512345, 0.00001)
format_ci(0.512345, 0.00001, max_digit = 10)
format_ci(0.512345, 0.5)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, percent = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, sign = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234, percent = TRUE, sign = TRUE)
format_ci(-0.1, lcl = -0.1999, ucl = 0.1234)
format_ci(0.512345e-6, 1e-6)
format_ci(0.512345e-7, 1e-7)
format_ci(0.512345e-7, 1e-8)
format_ci(0.512345e-7, 1e-9)
format_ci(0.512345, 0.1, link = "log", percent = TRUE, change = FALSE)
format_ci(0.512345, 0.1, link = "log", percent = TRUE, change = TRUE)
format_ci(0, lcl = 0, ucl = 0)
format_ci(1, lcl = 1, ucl = 1)

Check If an Object Is a Valid Effectclass Object

Description

Check If an Object Is a Valid Effectclass Object

Usage

is_effectclass(x, message = c("none", "warning", "error"))
is_effectclass(x, message = c("none", "warning", "error"))

Arguments

`x`	The object to test.
`message`	What to do when the object is not a valid effectclass object. `"none"`: return `FALSE` with a message. `"warning"`: return `FALSE` with a `warning()`. `"error"`: return an error.

Value

A single TRUE or FALSE value.

Create `plotly`references Returns a list shapes you can pass to the `shapes` argument of `plotly::layout()`

Description

Create plotlyreferences Returns a list shapes you can pass to the shapes argument of plotly::layout()

Usage

reference_shape(
  threshold,
  reference = 0,
  colour = "black",
  line = FALSE,
  horizontal = TRUE
)
reference_shape(
  threshold,
  reference = 0,
  colour = "black",
  line = FALSE,
  horizontal = TRUE
)

Arguments

`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.
`colour`	The colour for the references. Defaults to `"black"`.
`line`	display the `threshold` as a line (`TRUE`) or a ribbon (`FALSE`). Defaults to `FALSE`.
`horizontal`	Display horizontal reference when `TRUE` (default). Display vertical reference when `FALSE`.

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)
  )
# 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)
  )

Create `plotly`reference text Returns a list text you can pass to the `annotations` argument of `plotly::layout()`

Description

Create plotlyreference text Returns a list text you can pass to the annotations argument of plotly::layout()

Usage

reference_text(
  threshold,
  reference = 0,
  offset,
  text = c("reference", "important decrease", "important increase")
)
reference_text(
  threshold,
  reference = 0,
  offset,
  text = c("reference", "important decrease", "important increase")
)

Arguments

`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.
`offset`	An numeric vector with the offset between `text` and the lines. In units of the `y` variable. Defaults to 10% of the difference between reference and threshold.
`text`	A character vector with three elements with the text to display on the reference line, bottom threshold line and upper threshold line. Defaults to `c("reference", "important decrease", "important increase")`.

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)
  )
# 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)
  )

Remove the sign of a classification

Description

⁠**⁠ strong effect: ⁠++⁠ or ⁠--⁠
* effect: + or -
⁠*~⁠ moderate effect: ⁠+~⁠ or ⁠-~⁠
~ no effect: ~
⁠?+⁠ potential effect: ⁠?+⁠ or ⁠?-⁠
⁠?⁠ unknown effect: ⁠?⁠

Usage

remove_sign(classification)
remove_sign(classification)

Arguments

classification

The classification

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)
)
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.

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)
# 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)

Display points with classified effect

Description

Display points with classified effect

Usage

stat_effect(
  mapping = NULL,
  data = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  threshold,
  reference = 0,
  detailed = TRUE,
  signed = TRUE,
  shape_colour = TRUE,
  errorbar = TRUE,
  error_colour = TRUE,
  size = 6,
  labels = class_labels(lang = "en", detailed = detailed, signed = signed),
  ref_line = c("all", "ref", "none")
)
stat_effect(
  mapping = NULL,
  data = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...,
  threshold,
  reference = 0,
  detailed = TRUE,
  signed = TRUE,
  shape_colour = TRUE,
  errorbar = TRUE,
  error_colour = TRUE,
  size = 6,
  labels = class_labels(lang = "en", detailed = detailed, signed = signed),
  ref_line = c("all", "ref", "none")
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`...`	Other arguments passed on to `layer()`'s `params` argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the `position` argument, or aesthetics that are required can not be passed through `...`. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, `colour = "red"` or `linewidth = 3`. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the `params`. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a `⁠stat_()⁠` function, the `...` argument can be used to pass on parameters to the `geom` part of the layer. An example of this is `stat_density(geom = "area", outline.type = "both")`. The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a `⁠geom_()⁠` function, the `...` argument can be used to pass on parameters to the `stat` part of the layer. An example of this is `geom_area(stat = "density", adjust = 0.5)`. The stat's documentation lists which parameters it can accept. The `key_glyph` argument of `layer()` may also be passed on through `...`. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
`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.
`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`.
`shape_colour`	Colour the background of the labels according to the classification. Defaults to `TRUE`.
`errorbar`	Display the uncertainty as error bars. Defaults to `TRUE`.
`error_colour`	Colour the error bars according to the classification. Defaults to `TRUE`.
`size`	Size of the symbols.
`labels`	the labels for the legend.
`ref_line`	Which reference lines to display. `"all"` displays a dashed horizontal line at the `reference` and a dotted horizontal line at the `threshold`. `"ref"` displays a dashed horizontal line at the `reference`. `"none"` displays no horizontal lines.

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)
# 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)

Display a fan plot

Description

Usage

stat_fan(
  mapping = NULL,
  data = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  geom = "ribbon",
  ...,
  link = c("identity", "log", "logit"),
  max_prob = 0.9,
  step = 0.05
)
stat_fan(
  mapping = NULL,
  data = NULL,
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  geom = "ribbon",
  ...,
  link = c("identity", "log", "logit"),
  max_prob = 0.9,
  step = 0.05
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`geom`	Use a different `geom` than the default `"ribbon"`.
`...`	Other arguments passed on to `layer()`'s `params` argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the `position` argument, or aesthetics that are required can not be passed through `...`. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, `colour = "red"` or `linewidth = 3`. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the `params`. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a `⁠stat_()⁠` function, the `...` argument can be used to pass on parameters to the `geom` part of the layer. An example of this is `stat_density(geom = "area", outline.type = "both")`. The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a `⁠geom_()⁠` function, the `...` argument can be used to pass on parameters to the `stat` part of the layer. An example of this is `geom_area(stat = "density", adjust = 0.5)`. The stat's documentation lists which parameters it can accept. The `key_glyph` argument of `layer()` may also be passed on through `...`. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
`link`	the link function to apply on the `y` before calculating the coverage intervals. Note that `link_sd` is the standard error on the link scale, while `y` is on the natural scale. Defaults to `'identify'` which implies no transformation (`link(y) == y`). Other options are `'log'` and `'logit'`.
`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`.

Examples

set.seed(20191218)
z <- data.frame(
  year = 1990:2019,
  dx = rnorm(30, sd = 0.2),
  s = rnorm(30, 0.5, 0.01)
 )
z$index <- 3 + cumsum(z$dx)
library(ggplot2)
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan() + geom_line()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(step = 0.3)
ggplot(z, aes(x = year, y = exp(index), link_sd = s)) +
  stat_fan(link = "log") + geom_line()
ggplot(z, aes(x = year, y = plogis(index), link_sd = s)) +
  stat_fan(link = "logit") + geom_line()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(geom = "rect")
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(geom = "bar")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "errorbar")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "linerange")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "pointrange")

z <- expand.grid(year = 1990:2019, category = c("A", "B"))
z$dx <- rnorm(60, sd = 0.1)
z$index <- rep(c(0, 2), each = 30) + cumsum(z$dx)
z$s <- rnorm(60, rep(c(0.5, 1), each = 30), 0.05)
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan() + geom_line() +
  facet_wrap(~category)
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(aes(fill = category)) + geom_line(aes(colour = category))
set.seed(20191218)
z <- data.frame(
  year = 1990:2019,
  dx = rnorm(30, sd = 0.2),
  s = rnorm(30, 0.5, 0.01)
 )
z$index <- 3 + cumsum(z$dx)
library(ggplot2)
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan() + geom_line()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(step = 0.3)
ggplot(z, aes(x = year, y = exp(index), link_sd = s)) +
  stat_fan(link = "log") + geom_line()
ggplot(z, aes(x = year, y = plogis(index), link_sd = s)) +
  stat_fan(link = "logit") + geom_line()
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(geom = "rect")
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan(geom = "bar")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "errorbar")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "linerange")
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(geom = "pointrange")

z <- expand.grid(year = 1990:2019, category = c("A", "B"))
z$dx <- rnorm(60, sd = 0.1)
z$index <- rep(c(0, 2), each = 30) + cumsum(z$dx)
z$s <- rnorm(60, rep(c(0.5, 1), each = 30), 0.05)
ggplot(z, aes(x = year, y = index, link_sd = s)) + stat_fan() + geom_line() +
  facet_wrap(~category)
ggplot(z, aes(x = year, y = index, link_sd = s)) +
  stat_fan(aes(fill = category)) + geom_line(aes(colour = category))

Flatten Lists

Description

Flatten Lists

Usage

unlist(x, recursive = TRUE, use.names = TRUE)
unlist(x, recursive = TRUE, use.names = TRUE)

Arguments

`x`	an R object, typically a list or vector.
`recursive`	logical. Should unlisting be applied to list components of `x`?
`use.names`	logical. Should names be preserved?

Package 'effectclass'

Help Index

Add a point symbol with classification to a plotly object

Description

Usage

Arguments

See Also

Examples

Add a fan plot to a plotly object

Description

Usage

Arguments

See Also

Examples

Logarithmic breaks for changes

Description

Usage

Arguments

See Also

Display logarithmic changes as percentage

Description

Usage

Arguments

See Also

Return a standardised set of labels for the classification

Description

Usage

Arguments

See Also

Classify Effects by Comparing the Confidence Intervals with a Reference and Thresholds

Description

Usage

Arguments

See Also

Use a Lower Scale Classification

Description

Usage

Arguments

See Also

Format an Estimate and Confidence Interval as Text

Description

Usage

Arguments

See Also

Examples

Check If an Object Is a Valid Effectclass Object

Description

Usage

Arguments

Value

See Also

Create plotlyreferences Returns a list shapes you can pass to the shapes argument of plotly::layout()

Description

Usage

Arguments

See Also

Examples

Create plotlyreference text Returns a list text you can pass to the annotations argument of plotly::layout()

Description

Usage

Arguments

See Also

Examples

Remove the sign of a classification

Description

Usage

Arguments

See Also

A scale for effect points

Description

Usage

Arguments

See Also

Examples

Display points with classified effect

Description

Usage

Arguments

See Also

Examples

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

Add a fan plot to a `plotly` object

Create `plotly`references Returns a list shapes you can pass to the `shapes` argument of `plotly::layout()`

Create `plotly`reference text Returns a list text you can pass to the `annotations` argument of `plotly::layout()`