Plotting reliability diagram objects — plot.reliabilitydiag • reliabilitydiag

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

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

# S3 method for 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 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.

Value

An object inheriting from class 'ggplot'.

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")`

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))