verification (version 1.01)

conditional.quantile: Conditional Quantile Plot

Description

This function creates a conditional quantile plot as shown in Murphy, et al (1989) and Wilks (1995).

Usage

conditional.quantile(pred, obs, bins = NULL, thrs = c(10, 20), main,
... )

Arguments

pred

Forecasted value. ([n,1] vector, n = # of forecasts)

obs

Observed value.([n,1] vector, n = # of observations)

bins

Bins for forecast and observed values. A minimum number of values are required to calculate meaningful statistics. So for variables that are continuous, such as temperature, it is frequently convenient to bin these values. ([m,1] vector, m = # of bi

thrs

The minimum number of values in a bin required to calculate the 25th and 75th quantiles and the 10th and 90th percentiles respectively. The median values will always be displayed. ( [2,1] vector)

main

Plot title

...

Plotting options.

Value

• This function produces a conditional.quantile plot. The $y$ axis represents the observed values, while the $x$ axis represents the forecasted values. The histogram along the bottom axis illustrates the frequency of each forecast.

References

Murphy, A. H., B.G. Brown and Y. Chen. (1989) ``Diagnostic Verification of Temperature Forecasts,'' Weather and Forecasting; December, 1989.

Examples

set.seed(10)
m<- seq(10, 25, length = 1000)  
frcst <- round(rnorm(1000, mean = m, sd = 2) )
obs<- round(rnorm(1000, mean = m, sd = 2 ))
bins <- seq(0, 30,1)
thrs<- c( 10, 20) # number of obs needed for a statistic to be printed #1,4 quartile, 2,3 quartiles

conditional.quantile(frcst, obs, bins, thrs, main = "Sample Conditional Quantile Plot")
#### Or plots a ``cont.cont'' class object.

obs<- rnorm(100)
pred<- rnorm(100)
baseline <- rnorm(100, sd = 0.5) 

A<- verify(obs, pred, baseline = baseline,  frcst.type = "cont", obs.type = "cont")
 plot(A)