verification (version 1.38)

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 = No. of forecasts)

obs

Observed value.([n,1] vector, n = No. 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 = No. of

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, 4, 485--501.

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)