surveillance: scores – R documentation

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scores

Proper Scoring Rules for Poisson or Negative Binomial Predictions

Description

Proper scoring rules for Poisson or negative binomial predictions of count data are described in Czado et al. (2009). The following scores are implemented: logarithmic score (logs), ranked probability score (rps), Dawid-Sebastiani score (dss), squared error score (ses).

Usage

scores(x, ...)

## Default S3 method:
scores(x, mu, size = NULL,
       which = c("logs", "rps", "dss", "ses"),
       sign = FALSE, ...)

logs(x, mu, size = NULL)
rps(x, mu, size = NULL, k = 40, tolerance = sqrt(.Machine$double.eps))
dss(x, mu, size = NULL)
ses(x, mu, size = NULL)

Arguments

`x`	the observed counts. All functions are vectorized and also accept matrices or arrays. Dimensions are preserved.
`mu`	the means of the predictive distributions for the observations `x`.
`size`	either `NULL` (default), indicating Poisson predictions with mean `mu`, or dispersion parameters of negative binomial forecasts for the observations `x`, parametrized as in `dnbinom` with variance `mu*(1+mu/size)`.
`which`	a character vector specifying which scoring rules to apply. By default, all four proper scores are calculated. The normalized squared error score (`"nses"`) is also available but it is improper and hence not computed by default.
`sign`	a logical indicating if the function should also return `sign(x-mu)`, i.e., the sign of the difference between the observed counts and corresponding predictions.
`...`	unused (argument of the generic).
`k`	scalar argument controlling the finite sum approximation for the `rps` with truncation at `ceiling(mu + k*sd)`.
`tolerance`	absolute tolerance for the finite sum approximation employed in the `rps` calculation. A warning is produced if the approximation with `k` summands is insufficient for the specified `tolerance`. In this case, increase `k` for higher precision (or use a larger tolerance).

Value

The scoring functions return the individual scores for the predictions of the observations in x (maintaining their dimension attributes).

The default scores-method applies the selected (which) scoring functions (and calculates sign(x-mu)) and returns the results in an array (via simplify2array), where the last dimension corresponds to the different scores.

Author(s)

Sebastian Meyer and Michaela Paul

References

Czado, C., Gneiting, T. and Held, L. (2009): Predictive model assessment for count data. Biometrics, 65 (4), 1254-1261. doi: 10.1111/j.1541-0420.2009.01191.x

Examples

mu <- c(0.1, 1, 3, 6, pi, 100)
size <- 0.1
set.seed(1)
y <- rnbinom(length(mu), mu = mu, size = size)
scores(y, mu = mu, size = size)
scores(y, mu = mu, size = 1)  # ses ignores the variance
scores(y, mu = 1, size = size)

## apply a specific scoring rule
scores(y, mu = mu, size = size, which = "rps")
rps(y, mu = mu, size = size)

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30