Computes scores for the assessment of sharpness of a fitted model for time series of counts.
# S3 method for tsglm
scoring(object, individual=FALSE, cutoff=1000, ...)
# S3 method for default
scoring(response, pred, distr=c("poisson", "nbinom"), distrcoefs,
individual=FALSE, cutoff=1000, ...)an object of class "tsglm".
logical. If FALSE (the default) the average scores are returned. Otherwise a matrix with the individual scores for each observation is returned.
positive integer. Summation over the infinite sample space {0,1,2,...} of a distribution is cut off at this value. This affects the quadratic, spherical and ranked probability score.
integer vector. Vector of observed values \(Y_1,...,Y_n\).
numeric vector. Vector of predicted values \(\mu_{P_1},...,\mu_{P_n}\).
character giving the conditional distribution. Currently implemented are the Poisson ("poisson")and the Negative Binomial ("nbinom") distribution.
numeric vector of additional coefficients specifying the conditional distribution. For distr="poisson" no additional parameters need to be provided. For distr="nbinom" the additional parameter size needs to be specified (e.g. by distrcoefs=2), see tsglm for details.
further arguments are currently ignored. Only for compatibility with generic function.
Returns a named vector of the mean scores (if argument individual=FALSE, the default) or a data frame of the individual scores for each observation (if argument individual=TRUE). The scoring rules are named as follows:
Logarithmic score
Quadratic or Brier score
Spherical score
Ranked probability score
Dawid-Sebastiani score
Normalized squared error score
Squared error score
The scoring rules are penalties that should be minimised for a better forecast, so a smaller scoring value means better sharpness. Different competing forecast models can be ranked via these scoring rules. They are computed as follows: For each score \(s\) and time \(t\) the value \(s(P_{t},Y_{t})\) is computed, where \(P_t\) is the predictive c.d.f. and \(Y_t\) is the observation at time \(t\). To obtain the overall score for one model the average of the score of all observations \((1/n) \sum_{t=1}^{n}s(P_{t},Y_{t})\) is calculated.
For all \(t \geq 1\), let \(p_{y} = P(Y_{t}=y | {\cal{F}}_{t-1} )\) be the density function of the predictive distribution at \(y\) and \(||p||^2=\sum_{y=0}^{\infty} p_y^2\) be a quadratic sum over the whole sample space \(y=0,1,2,...\) of the predictive distribution. \(\mu_{P_t}\) and \(\sigma_{P_t}\) are the mean and the standard deviation of the predictive distribution, respectively.
Then the scores are defined as follows:
Logarithmic score: \(logs(P_{t},Y_{t})= -log p_{y}\)
Quadratic or Brier score: \(qs(P_{t},Y_{t}) = -2p_{y} + ||p||^2 \)
Spherical score: \(sphs(P_{t},Y_{t})=\frac{-p_{y}}{||p||}\)
Ranked probability score: \(rps(P_{t},Y_{t})=\sum_{x=0}^{\infty}(P_{t}(x) - 1(Y_t\leq x))^2\) (sum over the whole sample space \(x=0,1,2,...\))
Dawid-Sebastiani score: \(dss(P_{t},Y_{t})=\left(\frac{Y_t-\mu_{P_t}}{\sigma_{P_t}}\right)^2 + 2log\sigma_{P_t}\)
Normalized squared error score: \(nses(P_{t},Y_{t})=\left(\frac{Y_t-\mu_{P_t}}{\sigma_{P_t}}\right)^2\)
Squared error score: \(ses(P_{t},Y_{t})=(Y_t-\mu_{P_t})^2\)
For more information on scoring rules see the references listed below.
Christou, V. and Fokianos, K. (2013) On count time series prediction. Journal of Statistical Computation and Simulation (published online), http://dx.doi.org/10.1080/00949655.2013.823612.
Czado, C., Gneiting, T. and Held, L. (2009) Predictive model assessment for count data. Biometrics 65, 1254--1261, http://dx.doi.org/10.1111/j.1541-0420.2009.01191.x.
Gneiting, T., Balabdaoui, F. and Raftery, A.E. (2007) Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69, 243--268, http://dx.doi.org/10.1111/j.1467-9868.2007.00587.x.
tsglm for fitting a GLM for time series of counts.
pit and marcal for other predictive model assessment tools.
permutationTest in package surveillance for the Monte Carlo permutation test for paired individual scores by Paul and Held (2011, Statistics in Medicine 30, 1118--1136, http://dx.doi.org/10.1002/sim.4177).
# NOT RUN {
###Campylobacter infections in Canada (see help("campy"))
campyfit <- tsglm(ts=campy, model=list(past_obs=1, past_mean=c(7,13)))
scoring(campyfit)
# }
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