value: Forecast Value Function

In verification: Weather Forecast Verification Utilities

Description

Calculates the economic value of a forecast based on a cost/loss ratio.

Usage

value(obs, pred= NULL, baseline = NULL, cl = seq(0.05, 0.95, 0.05),
    plot = TRUE, all = FALSE, thresholds = seq(0.05, 0.95, 0.05),
    ylim = c(-0.05, 1), xlim = c(0,1), ...)

Arguments

obs	A vector of binary observations or a contingency table summary of values in the form c(n11, n01, n10, n00) where in nab a = obs, b = forecast.
pred	A vector of probabilistic predictions.
baseline	Baseline or naive forecast. Typically climatology.
cl	Cost loss ratio. The relative value of being unprepared and taking a loss to that of un-necessarily preparing. For example, cl = 0.1 indicates it would cost \$ 1 to prevent a \$10 loss. This defaults to the sequence 0.05 to 0.95 by 0.05.
plot	Should a plot be created? Default is TRUE
all	In the case of probabilistic forecasts, should value curves for each thresholds be displayed.
thresholds	Thresholds considered for a probabilistic forecast.
ylim, xlim	Plotting options.
...	Options to be passed into the plotting function.

Value

If assigned to an object, the following values are reported.

vmax	Maximum value
V	Vector of values for each cl value
F	Conditional false alarm rate.
H	Conditional hit rate
cl	Vector of cost loss ratios.
s	Base rate

Author(s)

Matt Pocernich

References

Jolliffe, Ian and David B. Stephensen (2003) Forecast Verification: A Practioner's Guide in Atmospheric Science, Chapter 8. Wiley

Examples

## value as a contingency table
## Finley tornado data
obs<- c(28, 72, 23, 2680) 
value(obs)
aa <- value(obs)
aa$Vmax # max value

## probabilistic forecast example
 obs  <- round(runif(100) )
 pred <-  runif(100)

value(obs, pred, main = "Sample Plot",
             thresholds = seq(0.02, 0.98, 0.02) ) 
##########
data(pop)
d <- pop.convert()

value(obs = d$obs_rain, pred = d$p24_rain, all = TRUE)

 

verification documentation built on May 2, 2019, 1:24 a.m.