attribute: Attribute plot

Description

An attribute plot illustrates the reliability, resolution and uncertainty of a forecast with respect to the observation. The frequency of binned forecast probabilities are plotted against proportions of binned observations. A perfect forecast would be indicated by a line plotted along the 1:1 line. Uncertainty is described as the vertical distance between this point and the 1:1 line. The relative frequency for each forecast value is displayed in parenthesis.

Usage

# S3 method for default
attribute(x, obar.i,  prob.y = NULL, obar = NULL,
    class = "none", main = NULL, CI = FALSE, n.boot = 100, alpha = 0.05,
    tck = 0.01, freq = TRUE, pred = NULL, obs = NULL, thres = thres,
    bins = FALSE, ...)
# S3 method for prob.bin
attribute(x, ...)

Arguments

x: A vector of forecast probabilities or a “prob.bin” class object produced by the verify function.
obar.i: A vector of observed relative frequency of forecast bins.
prob.y: Relative frequency of forecasts of forecast bins.
obar: Climatological or sample mean of observed events.
class: Class of object. If prob.bin, the function will use the data to estimate confidence intervals.
main: Plot title.
CI: Confidence Intervals. This is only an option if the data is accessible by using the verify command first. Calculated by bootstrapping the observations and prediction, then calculating PODy and PODn values.
n.boot: Number of bootstrap samples.
alpha: Confidence interval. By default = 0.05
tck: Tick width on confidence interval whiskers.
freq: Should the frequecies be plotted. Default = TRUE
pred: Required to create confidence intervals
obs: Required to create confidence intervals
thres: thresholds used to create bins for plotting confidence intervals.
bins: Should probabilities be binned or treated as unique predictions?
...: Graphical parameters

Author

Matt Pocernich

References

Hsu, W. R., and A. H. Murphy, 1986: The attributes diagram: A geometrical framework for assessing the quality of probability forecasts. Int. J. Forecasting 2, 285--293.

Wilks, D. S. (2005) Statistical Methods in the Atmospheric Sciences Chapter 7, San Diego: Academic Press.

Examples

Run this code

## Data from Wilks, table 7.3 page 246.
 y.i   <- c(0,0.05, seq(0.1, 1, 0.1))
 obar.i <- c(0.006, 0.019, 0.059, 0.15, 0.277, 0.377, 0.511, 
             0.587, 0.723, 0.779, 0.934, 0.933)
 prob.y<- c(0.4112, 0.0671, 0.1833, 0.0986, 0.0616, 0.0366,
            0.0303,  0.0275, 0.245, 0.022, 0.017, 0.203) 
 obar<- 0.162
 
attribute(y.i, obar.i, prob.y, obar, main = "Sample Attribute Plot")  

## Function will work with a ``prob.bin'' class objects as well.
## Note this is a random forecast.
obs<- round(runif(100))
pred<- runif(100)

A<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")
attribute(A, main = "Alternative plot", xlab = "Alternate x label" )
## to add a line from another model
obs<- round(runif(100))
pred<- runif(100)

B<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")
lines.attrib(B, col = "green")


## Same with confidence intervals
attribute(A, main = "Alternative plot", xlab = "Alternate x label", CI =
TRUE)

#### add lines to plot
data(pop)
d <- pop.convert()
## internal function used to
## make binary observations for
## the pop figure.

### note the use of bins = FALSE
mod24 <- verify(d$obs_rain, d$p24_rain,
    bins = FALSE)

mod48 <- verify(d$obs_rain, d$p48_rain,
    bins = FALSE)
plot(mod24, freq = FALSE)

lines.attrib(mod48, col = "green",
    lwd = 2, type = "b")