plot.reliabilitydiag {reliabilitydiag}

Plotting reliability diagram objects

Description

Using the ggplot2 package to visually diagnose the reliability of prediction methods that issue probability forecasts.

Usage

## S3 method for class 'reliabilitydiag'
plot(x, ...)

## S3 method for class 'reliabilitydiag'
autoplot(
  object,
  ...,
  type = c("miscalibration", "discrimination"),
  colour = "red",
  params_histogram = NULL,
  params_ggMarginal = NULL,
  params_ribbon = NULL,
  params_diagonal = NULL,
  params_vsegment = NULL,
  params_hsegment = NULL,
  params_CEPline = NULL,
  params_CEPsegment = NULL,
  params_CEPpoint = NULL
)

## S3 method for class 'reliabilitydiag'
autolayer(
  object,
  ...,
  type = c("miscalibration", "discrimination"),
  colour = "red",
  params_histogram = NA,
  params_ggMarginal = NA,
  params_ribbon = NA,
  params_diagonal = NA,
  params_vsegment = NA,
  params_hsegment = NA,
  params_CEPline = NA,
  params_CEPsegment = NA,
  params_CEPpoint = NA
)

Arguments

x

an object inheriting from the class 'reliabilitydiag'.

...

further arguments to be passed to or from methods.

object

an object inheriting from the class 'reliabilitydiag'.

type

one of "miscalibration", "discrimination"; determines which layers are added by default, including default parameter values.

colour

a colour to be used to draw focus; used for the CEP layers when type is "miscalibration", and for the horizontal segment layer and CEP margin histogram when type is "discrimination".

params_histogram

a list of arguments for ggplot2::geom_histogram; this layer shows a histogram of the forecast values in the main plotting region.

params_ggMarginal

a list of arguments for ggExtra::ggMarginal; used to show the marginal distributions of the forecast values and estimated CEP values by adding plots to the top and right of the main plotting region. If this is anything other than NA, the autoplot output cannot be customized by with additional layers.

params_ribbon

a list of arguments for ggplot2::geom_ribbon; this layer shows the uncertainty quantification results.

params_diagonal

a list of arguments for ggplot2::geom_line; this background layer illustrates perfect reliability.

params_vsegment

a list of arguments for ggplot2::geom_segment; this layer shows a vertical segment illustrating the average forecast value.

params_hsegment

a list of arguments for ggplot2::geom_segment; this layer shows a horizontal segment illustrating the average event frequency.

params_CEPline

a list of arguments for ggplot2::geom_line; this layer shows a linear interpolation of the CEP estimates.

params_CEPsegment

a list of arguments for ggplot2::geom_segment; this layer highlights the pieces where the CEP estimate remains constant.

params_CEPpoint

a list of arguments for ggplot2::geom_point; this layer highlights the CEP estimate only for actually observed forecast values.

Details

plot always sends a plot to a graphics device, wheres autoplot behaves as any ggplot() + layer() combination. That means, customized plots should be created using autoplot and autolayer.

Three sets of default parameter values are used:

• If multiple predictions methods are compared, then only the most necessary information to determine reliability are displayed.

• For a single prediction method and type = "miscalibration", the focus lies on the deviation from the diagonal including uncertainty quantification.

• For a single prediction method and type = "discrimination", the focus lies on the PAV transformation and the resulting marginal distribution. A concentration of CEP values near 0 or 1 suggest a high potential predictive ability of a prediction method.

Setting any of the params_* arguments to NA disables that layer.

Default parameter values if length(object) > 1, where the internal variable forecast is used as grouping variable:

params_histogram	NA
params_ggMarginal	NA
params_ribbon	NA
params_diagonal	list(size = 0.3, colour = "black")
params_vsegment	NA
params_hsegment	NA
params_CEPline	list(size = 0.2)
params_CEPsegment	NA
params_CEPpoint	NA

Default parameter values for type = "miscalibration" if length(object) == 1:

params_histogram	list(yscale = 0.2, colour = "black", fill = NA)
params_ggMarginal	NA
params_ribbon	list(fill = "blue", alpha = 0.15)
params_diagonal	list(size = 0.3, colour = "black")
params_vsegment	NA
params_hsegment	NA
params_CEPline	list(size = 0.2, colour = colour)
params_CEPsegment	list(size = 2, colour = colour) if xtype == "continuous"; NA otherwise.
params_CEPpoint	list(size = 2, colour = colour) if xtype == "discrete"; NA otherwise.

Default parameter values for type = "discrimination" if length(object) == 1:

params_histogram	NA
params_ggMarginal	list(type = "histogram", xparams = list(bins = 100, fill = "grey"), yparams = list(bins = 100, fill = colour))
params_ribbon	NA
params_diagonal	list(size = 0.3, colour = "lightgrey")
params_vsegment	list(size = 1.5, colour = "grey")
params_hsegment	list(size = 1.5, colour = colour)
params_CEPline	list(size = 0.2, colour = "black")
params_CEPsegment	NA
params_CEPpoint	list(colour = "black")

Value

An object inheriting from class 'ggplot'.

Examples

data("precip_Niamey_2016", package = "reliabilitydiag")
r <- reliabilitydiag(
  precip_Niamey_2016[c("Logistic", "EMOS", "ENS", "EPC")],
  y = precip_Niamey_2016$obs,
  region.level = NA
)

# simple plotting
plot(r)

# faceting using the internal grouping variable 'forecast'
autoplot(r, params_histogram = list(colour = "black", fill = NA)) +
  ggplot2::facet_wrap("forecast")

# custom color scale for multiple prediction methods
cols <- c(Logistic = "red", EMOS = "blue", ENS = "darkgreen", EPC = "orange")
autoplot(r) +
  ggplot2::scale_color_manual(values = cols)

# default reliability diagram type with a title
rr <- reliabilitydiag(
  EMOS = precip_Niamey_2016$EMOS,
  r = r,
  region.level = 0.9
)
autoplot(rr) +
  ggplot2::ggtitle("Reliability diagram for EMOS method")

# using defaults for discrimination diagrams
p <- autoplot(r["EMOS"], type = "discrimination")
print(p, newpage = TRUE)

# ggMarginal needs to be called after adding all custom layers
p <- autoplot(r["EMOS"], type = "discrimination", params_ggMarginal = NA) +
  ggplot2::ggtitle("Discrimination diagram for EMOS method")
p <- ggExtra::ggMarginal(p, type = "histogram")
print(p, newpage = TRUE)

# the order of the layers can be changed
autoplot(rr, colour = "black", params_ribbon = NA) +
  autolayer(rr, params_ribbon = list(fill = "red", alpha = .5))