Calculate categorical statistics in related to a threshold

Calculate traditional statistics related to a threshold

Usage

cate(
  model,
  observation,
  threshold,
  cutoff = NA,
  nobs = 8,
  rname,
  to.plot = FALSE,
  col = "#4444bb",
  pch = 19,
  lty = 3,
  lcol = "#333333",
  lim,
  verbose = TRUE,
  ...
)

Arguments

model: numeric vector with paired model data
observation: numeric vector with paired observation data
threshold: reference value
cutoff: (optionally the maximum) valid value for observation
nobs: minimum number of observations
rname: row name
to.plot: TRUE to plot a scatter-plot
col: color for points
pch: pch of points
lty: lty of threshold lines
lcol: col of threshold lines
lim: limit for x and y
verbose: display additional information
...: arguments passed to plot

Value

a data.frame including: Accuracy (A); Critical Success Index (CSI); Probability of Detection (POD); Bias(B); False Alarm Ratio (FAR); Heidke Skill Score (HSS); Pearce skill Score (PSS) in

References

Yu, S., Mathur, R., Schere, K., Kang, D., Pleim, J., Young, J., ... & Rao, S. T. (2008). Evaluation of real‐time PM2. 5 forecasts and process analysis for PM2. 5 formation over the eastern United States using the Eta‐CMAQ forecast model during the 2004 ICARTT study. Journal of Geophysical Research: Atmospheres, 113(D6).

Examples

data <- 0.02 * 1:100
set.seed(666)
model  <- abs(rnorm(100,0.01))

oldpar <- par(pty="s")
cate(model = model, observation = data, threshold = 1,
     to.plot = TRUE, rname = 'example')

#>           n  Obs       Sim thr  A      CSI POD  B      FAR HSS PSS
#> example 100 1.01 0.8272308   1 43 18.57143  26 66 60.60606 -14 -14
par(oldpar)