Skill score with measurement error.
Skill score that incorporates measurement error. This function allows the user to incorporate measurement error in an observation in a skill score.
measurement.error( obs, frcs = NULL, theta = 0.5, CI =
FALSE, t = 1, u = 0, h = NULL, ...)obs |
Information about a forecast and observation can be done in one of two ways. First, the results of a contingency table can be entered as a vector containing c(n11, n10, n01, n00), where n11 are the number of correctly predicted events and n01 is the number of incorrectly predicted non-events. Actual forecasts and observations can be used. In this case, obs is a vector of binary outcomes [0,1]. |
frcs |
If obs is entered as a contingency table, this argument is null. If obs is a vector of outcomes, this column is a vector of probabilistic forecasts. |
theta |
Loss value (cost) of making a incorrect forecast by a non-event. Defaults to 0.5. |
CI |
Calculate confidence intervals for skill score. |
t |
Probability of forecasting an event, when an event occurs. A perfect value is 1. |
u |
Probability of forecasting that no event will occur, when and event occurs. A perfect value is 0. |
h |
Threshold for converting a probabilistic forecast into a binary forecast. By default, this value is NULL and the theta is used as this threshold. |
... |
Optional arguments. |
z |
Error code |
k |
Skill score |
G |
Likelihood ratio statistic |
p |
p-value for the null hypothesis that the forecast contains skill. |
theta |
Loss value. Loss associated with an incorrect forecast of a non-event. |
ciLO |
Lower confidence interval |
ciHI |
Upper confidence interval |
Matt Pocernich (R - code)
W.M Briggs <wib2004(at)med.cornell.edu> (Method questions)
W.M. Briggs, 2004. Incorporating Cost in the Skill Score Technical Report, wm-briggs.com/public/skillocst.pdf.
W.M. Briggs and D. Ruppert, 2004. Assessing the skill of yes/no forecasts. Submitting to Biometrics.
J.P. Finley, 1884. Tornado forecasts. Amer. Meteor. J. 85-88. (Tornado data used in example.)
DAT<- data.frame( obs = round(runif(50)), frcs = runif(50)) A<- measurement.error(DAT$obs, DAT$frcs, CI = TRUE) A ### Finley Data measurement.error(c(28, 23, 72, 2680)) ## assuming perfect observation,
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