scoringutils (version 0.1.7.2)

plot_predictions: Plot Predictions vs True Values

Description

Make a plot of observed and predicted values

Usage

plot_predictions(
  data = NULL,
  forecasts = NULL,
  truth_data = NULL,
  merge_by = NULL,
  x = "date",
  filter_truth = list(),
  filter_forecasts = list(),
  filter_both = list(),
  range = c(0, 50, 90),
  facet_formula = NULL,
  facet_wrap_or_grid = "facet_wrap",
  ncol = NULL,
  scales = "free_y",
  allow_truth_without_pred = FALSE,
  remove_from_truth = c("model", "forecaster", "quantile", "prediction", "sample",
    "interval"),
  xlab = x,
  ylab = "True and predicted values",
  verbose = TRUE
)

Arguments

data

a data.frame that follows the same specifications outlined in eval_forecasts. The data.frame needs to have columns called "true_value", "prediction" and then either a column called sample, or one called "quantile" or two columns called "range" and "boundary". Internally, these will be separated into a truth and forecast data set in order to be able to apply different filtering to truth data and forecasts. Alternatively you can directly provide a separate truth and forecasts data frame as input. These data sets, however, need to be mergeable, in order to connect forecasts and truth data for plotting.

forecasts

data.frame with forecasts, that should follow the same general guidelines as the `data` input. Argument can be used to supply forecasts and truth data independently. Default is `NULL`.

truth_data

data.frame with a column called `true_value` on the x-axis. Usually, this will be "date", but it can be anything else.

merge_by

character vector with column names that `forecasts` and `truth_data` should be merged on. Default is `NULL` and merge will be attempted automatically.

x

character vector of length one that denotes the name of the variable

filter_truth

a list with character strings that are used to filter the truth data. Every element is parsed as an expression and evaluated in order to filter the truth data.

filter_forecasts

a list with character strings that are used to filter the truth data. Every element is parsed as an expression and evaluated in order to filter the forecasts data.

filter_both

same as `filter_truth` and `filter_forecasts`, but applied to both data sets for convenience.

range

numeric vector indicating the interval ranges to plot. If 0 is included in range, the median prediction will be shown.

facet_formula

formula for facetting in ggplot. If this is NULL (the default), no facetting will take place

facet_wrap_or_grid

Use ggplot2's facet_wrap or facet_grid? Anything other than "facet_wrap" will be interpreted as facet_grid. This only takes effect if facet_formula is not NULL

ncol

Number of columns for facet wrap. Only relevant if facet_formula is given and facet_wrap_or_grid == "facet_wrap"

scales

scales argument that gets passed down to ggplot. Only necessary if you make use of facetting. Default is "free_y"

allow_truth_without_pred

logical, whether or not to allow instances where there is truth data, but no forecast. If `FALSE` (the default), these get filtered out.

remove_from_truth

character vector of columns to remove from the truth data. The reason these columns are removed is that sometimes different models or forecasters don't cover the same periods. Removing these columns from the truth data makes sure that nevertheless all available truth data is plotted (instead of having different true values depending on the period covered by a certain model).

xlab

Label for the x-axis. Default is the variable name on the x-axis

ylab

Label for the y-axis. Default is "True and predicted values"

verbose

print out additional helpful messages (default is TRUE)

Value

ggplot object with a plot of true vs predicted values

Examples

# NOT RUN {
example1 <- scoringutils::continuous_example_data
example2 <- scoringutils::range_example_data_long

scoringutils::plot_predictions(example1, x = "value_date",
                               filter_truth = list('value_date <= "2020-06-22"',
                                                   'value_date > "2020-05-01"'),
                               filter_forecasts = list("model == 'SIRCOVID'",
                                                       'creation_date == "2020-06-22"'),
                               facet_formula = geography ~ value_desc)

scoringutils::plot_predictions(example2, x = "value_date",
                               filter_truth = list('value_date <= "2020-06-22"',
                                                   'value_date > "2020-05-01"'),
                               filter_forecasts = list("model == 'SIRCOVID'",
                                                       'creation_date == "2020-06-22"'),
                               allow_truth_without_pred = TRUE,
                               facet_formula = geography ~ value_desc)
# }