R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for a HTML browser interface to help.
Type 'q()' to quit R.

> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("verification-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('verification')
Loading required package: fields
fields is loaded use help(fields) for an overview of this library
Loading required package: waveslim

 Package verify: 
Please send comments or suggestions to pocernic@(Remove this)ucar.edu 
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "attribute"
> 
> ### * attribute
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: attribute
> ### Title: Attribute plot
> ### Aliases: attribute attribute.default attribute.prob.bin
> ### Keywords: file
> 
> ### ** Examples
> 
> ## 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, titl = "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")
If baseline is not included, baseline values  will be calculated from the  sample obs. 
> 
> attribute(A, titl = "Alternative plot", xlab = "Alternate x label" )
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "brier"
> 
> ### * brier
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: brier
> ### Title: Brier Score
> ### Aliases: brier
> ### Keywords: file
> 
> ### ** Examples
> 
> 
> #  probabilistic/ binary example
> 
> pred<- runif(100)
> obs<- round(runif(100))
> 
> brier(obs, pred)
$baseline.tf
[1] FALSE

$bs
[1] 0.3391037

$bs.baseline
[1] 0.2484

$ss
[1] -0.3651516

$bs.reliability
[1] 0.1030801

$bs.resol
[1] 0.01798009

$bs.uncert
[1] 0.2484

$y.i
 [1] 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

$obar.i
 [1] 0.5714286 0.8333333 0.6363636 0.5714286 0.3571429 0.2000000 0.5454545
 [8] 0.5333333 0.6363636 0.5000000

$prob.y
 [1] 0.07 0.06 0.11 0.14 0.14 0.05 0.11 0.15 0.11 0.06

$obar
[1] 0.54

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "conditional.quantile"
> 
> ### * conditional.quantile
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: conditional.quantile
> ### Title: Conditional Quantile Plot
> ### Aliases: conditional.quantile
> ### Keywords: file
> 
> ### ** 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)
> 
> 
> 
> cleanEx(); ..nameEx <- "crps"
> 
> ### * crps
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: crps
> ### Title: Continuous Ranked Probability Score
> ### Aliases: crps
> ### Keywords: file
> 
> ### ** Examples
> 
> 
> #  probabilistic/ binary example
> 
> x <- runif(100) ## simulated observation.
> crps(x, c(0,1))
Warning in return(crps = crps, CRPS = mean(crps), ign = ign, IGN = mean(ign),  : 
	 multi-argument returns are deprecated
$crps
  [1] 0.2616543 0.2883102 0.3611462 0.5418769 0.2498674 0.5356559 0.5654260
  [8] 0.4018222 0.3865815 0.2352175 0.2505607 0.2460987 0.4149248 0.2918401
 [15] 0.4591104 0.3305244 0.4307581 0.5969316 0.2906293 0.4633751 0.5589194
 [22] 0.2515821 0.3973667 0.2399757 0.2620139 0.2924429 0.2337665 0.2913280
 [29] 0.5177676 0.2794664 0.3246550 0.3729599 0.3289450 0.2474893 0.4922153
 [36] 0.4056085 0.4729150 0.2383389 0.4339803 0.3002394 0.4884224 0.3951342
 [43] 0.4664740 0.3526938 0.3430933 0.4701239 0.2339121 0.3228676 0.4385690
 [50] 0.4178346 0.3230105 0.5125668 0.3090621 0.2574832 0.2356871 0.2376387
 [57] 0.2732710 0.3386609 0.4024158 0.2988284 0.5448527 0.2678400 0.3163227
 [64] 0.2773712 0.3969770 0.2601073 0.3233506 0.4571418 0.2365248 0.5212420
 [71] 0.2791271 0.4993998 0.2811691 0.2777314 0.3225455 0.5317592 0.5144815
 [78] 0.2936134 0.4633049 0.5759359 0.3079021 0.4280764 0.2966863 0.2755560
 [85] 0.4520342 0.2500293 0.4273470 0.2395956 0.2576171 0.2418737 0.2564941
 [92] 0.2350802 0.3928396 0.5218286 0.4642032 0.4746761 0.3149864 0.2998601
 [99] 0.4825234 0.3753913

$CRPS
[1] 0.3629744

$ign
  [1] 0.9541860 0.9881766 1.0830190 1.3313592 0.9392763 1.3224905 1.3651442
  [8] 1.1372654 1.1168308 0.9208473 0.9401513 0.9345247 1.1549387 0.9927064
 [15] 1.2152664 1.0427908 1.1764267 1.4108774 0.9911519 1.2211491 1.3557755
 [22] 0.9414408 1.1312779 0.9268206 0.9546420 0.9934806 0.9190282 0.9920488
 [29] 1.2971196 0.9768573 1.0351392 1.0986781 1.0407300 0.9362770 1.2612118
 [36] 1.1423624 1.2343470 0.9247644 1.1808173 1.0035119 1.2559149 1.1282820
 [43] 1.2254304 1.0718631 1.0592400 1.2304802 0.9192107 1.0328128 1.1870802
 [50] 1.1588770 1.0329988 1.2897794 1.0149031 0.9489014 0.9214363 0.9238853
 [57] 0.9689524 1.0534293 1.1380639 1.0016939 1.3356098 0.9620400 1.0243092
 [64] 0.9741816 1.1307547 0.9522249 1.0334413 1.2125546 0.9224873 1.3020322
 [71] 0.9764238 1.2712686 0.9790333 0.9746414 1.0323938 1.3169475 1.2924799
 [78] 0.9949845 1.2210523 1.3803320 1.0134030 1.1727771 0.9989363 0.9718655
 [85] 1.2055290 0.9394806 1.1717852 0.9263430 0.9490708 0.9292066 0.9476497
 [92] 0.9206752 1.1252056 1.3028624 1.2222926 1.2367892 1.0225759 1.0030230
 [99] 1.2476938 1.1019107

$IGN
[1] 1.088464

$pit
  [1] 0.6046912 0.6450997 0.7166280 0.8181158 0.5799173 0.8155111 0.8275877
  [8] 0.7456290 0.7353628 0.5246335 0.5815946 0.5700717 0.7539658 0.6495492
 [15] 0.7793030 0.6906520 0.7635037 0.8393783 0.6480403 0.7815519 0.8250299
 [22] 0.5840021 0.7426942 0.5499579 0.6053504 0.6502939 0.5053418 0.6489132
 [29] 0.8077653 0.6332031 0.6851255 0.7256022 0.6891849 0.5738629 0.7959873
 [36] 0.7480821 0.7864721 0.5429798 0.7653784 0.6595643 0.7941616 0.7412035
 [43] 0.7831667 0.7098807 0.7018468 0.7850481 0.5093070 0.6834008 0.7680115
 [50] 0.7557610 0.6835395 0.8054387 0.6693421 0.5966933 0.5281734 0.5396159
 [57] 0.6241019 0.6979921 0.7460160 0.6579336 0.8193461 0.6154695 0.6769065
 [64] 0.6302044 0.7424349 0.6018030 0.6838689 0.7782543 0.5335699 0.8093005
 [71] 0.6327226 0.7993889 0.6355854 0.6307253 0.6830879 0.8138567 0.8062993
 [78] 0.6517279 0.7815152 0.8316279 0.6680952 0.7619269 0.6554197 0.6275427
 [85] 0.7755012 0.5803122 0.7614954 0.5484285 0.5969609 0.5569751 0.5946912
 [92] 0.5234978 0.7396570 0.8095582 0.7819850 0.7873642 0.6755441 0.6591279
 [99] 0.7912799 0.7273883

> 
> ## simulated forecast in which mean and sd differs for each forecast.
> frcs<- data.frame( runif(100, -2, 2), runif(100, 1, 3 ) )
> crps(x, frcs)
Warning in return(crps = crps, CRPS = mean(crps), ign = ign, IGN = mean(ign),  : 
	 multi-argument returns are deprecated
$crps
  [1] 0.3910393 0.5822602 0.8932573 0.6411454 0.3505461 1.2330111 1.4963091
  [8] 0.4665508 0.6420041 0.5874117 1.0661886 0.4618936 0.8360843 0.7361921
 [15] 1.2951406 1.6253753 0.5409311 1.5367795 0.7278515 0.4037688 0.6251538
 [22] 0.3268122 0.5061431 0.8608114 0.7443211 0.5363150 0.3543623 1.0122374
 [29] 1.1892220 0.6331128 0.4038290 1.6135773 1.5094157 0.5548791 0.5888069
 [36] 0.5687876 0.6065425 0.4930527 0.7412574 0.4755369 0.3789173 0.5147350
 [43] 1.1156663 0.9445858 0.4602980 0.5910944 0.7948001 0.3764868 1.4426007
 [50] 0.7549471 0.5898912 0.7252058 0.7058460 0.5494727 0.4613360 0.8464397
 [57] 0.3598968 1.3593021 0.9241992 0.9991816 1.0558330 0.7664599 0.4783442
 [64] 0.6971296 0.6866800 0.4189423 1.6071885 0.9005845 0.6394066 0.9394852
 [71] 0.2745750 0.3728958 0.6670412 0.4840903 0.5942378 0.4577975 0.3755400
 [78] 0.4135788 0.4825978 0.6602369 0.7842439 1.1660162 0.8321952 0.4302417
 [85] 0.4538215 0.8897677 0.4089429 0.4844449 0.9551419 0.3377493 0.4393482
 [92] 0.3741030 1.3369300 0.5504270 0.9793353 0.5840550 1.3836958 0.5677478
 [99] 0.9990038 0.7065931

$CRPS
[1] 0.7338927

$ign
  [1] 1.373980 1.504450 1.897816 1.588025 1.257887 2.134626 2.324946 1.325548
  [9] 1.581699 1.818602 2.160377 1.297035 1.970138 2.008076 2.229386 2.543367
 [17] 1.753461 2.379625 2.007153 1.446053 1.570179 1.219702 1.510621 1.873300
 [25] 2.000401 1.662797 1.334727 2.016329 2.093498 1.915273 1.452109 2.798261
 [33] 2.381719 1.750031 1.619802 1.792459 1.812336 1.665534 1.753223 1.583612
 [41] 1.398514 1.693759 2.165802 1.869577 1.545725 1.507977 1.791751 1.235393
 [49] 2.296455 2.013955 1.844791 1.996154 1.749631 1.729831 1.597573 1.754372
 [57] 1.330111 2.227110 1.870777 2.120074 2.010839 1.720114 1.454871 1.911586
 [65] 1.862133 1.307790 2.979509 2.000076 1.791218 1.859266 1.049397 1.208383
 [73] 1.672995 1.375304 1.565754 1.368272 1.351887 1.317079 1.605734 1.884561
 [81] 1.685144 2.088241 2.020535 1.492301 1.245418 1.804421 1.434573 1.364385
 [89] 1.885075 1.285931 1.379793 1.231721 2.290613 1.548949 1.953008 1.795769
 [97] 2.511949 1.428003 1.992232 1.986199

$IGN
[1] 1.758685

$pit
  [1] 0.4089615 0.7477610 0.7683990 0.2448157 0.4036492 0.8423410 0.8732524
  [8] 0.7239797 0.2402600 0.4455895 0.2805568 0.2658660 0.6910293 0.5898999
 [15] 0.7954485 0.9348206 0.4749181 0.8125454 0.5810618 0.5513980 0.2491490
 [22] 0.5685329 0.6679957 0.7607499 0.3958619 0.6110948 0.4917627 0.9222306
 [29] 0.8565650 0.4937393 0.5429061 0.9651877 0.9105959 0.4324562 0.3091294
 [36] 0.5461909 0.5915268 0.5007247 0.2564310 0.5791815 0.5221988 0.5444677
 [43] 0.7414637 0.8815910 0.4161518 0.7544594 0.7618149 0.3435861 0.8857506
 [50] 0.3954138 0.5030094 0.5873485 0.7185215 0.5776355 0.5139532 0.8707789
 [57] 0.5526003 0.8665899 0.8128482 0.7061900 0.8067777 0.2155358 0.6675874
 [64] 0.3798659 0.3585171 0.6755386 0.9755477 0.7148493 0.3587627 0.8725805
 [71] 0.4355037 0.3338210 0.2705274 0.7170082 0.7249103 0.3092057 0.5751777
 [78] 0.3366825 0.5722819 0.3988097 0.8836467 0.8283972 0.3390518 0.5709047
 [85] 0.2465456 0.8711932 0.4224799 0.2766740 0.1132277 0.4869269 0.3379505
 [92] 0.6548911 0.7750410 0.3072774 0.7931274 0.5730340 0.9535720 0.2209294
 [99] 0.9189416 0.4269372

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "discrimination.plot"
> 
> ### * discrimination.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: discrimination.plot
> ### Title: Discrimination plot
> ### Aliases: discrimination.plot
> ### Keywords: file
> 
> ### ** Examples
> 
>  #  A sample forecast.  
> 
>  a<- rnorm(100, mean = -1); b<- rnorm(100, mean = 1)
> 
>  A<- cbind(rep(0,100), pnorm(a)); B<- cbind(rep(1,100), pnorm(b)) 
> 
>  dat<- rbind(A,B); dat<- as.data.frame(dat)
>  names(dat)<- c("obs", "pred")
> 
>  discrimination.plot(dat$obs, dat$pred)
>  
> 
> 
> cleanEx(); ..nameEx <- "int.scale.verify"
> 
> ### * int.scale.verify
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: int.scale.verify
> ### Title: Intensity-Scale Verification Model
> ### Aliases: int.scale.verify
> ### Keywords: file
> 
> ### ** Examples
> 
> 
> ## simulated example
> n<- 5
> set.seed(10)
> forecast1 <- matrix( log(rlnorm(n = (2^n *2^n) )) , nrow = 2^n)
> obs1      <- matrix(log( rlnorm(n = (2^n *2^n) )) , nrow = 2^n)
> int.scale.verify(forecast1, obs1, main = "Test Case")  
$SSul
          -3.01       -1.29      -0.81      -0.49      -0.28          0
1    -3.5043988 -3.29088668 -3.3520917 -3.2750787 -3.1938798 -3.2421875
2    -0.1260997 -0.06126912 -0.1723142 -0.1729552 -0.1084704 -0.2055664
3     0.7184751  0.78717355  0.7435216  0.6681257  0.6578819  0.6246338
4     0.9296188  0.93009176  0.9347713  0.9412329  0.9317783  0.9425964
5     0.9765396  0.96027399  0.9773502  0.9907345  0.9948677  0.9984436
bias  1.0000000  0.99415443  0.9949917  0.9996609  0.9946995  0.9992676
           0.29       0.59       0.89       1.22
1    -3.2203179 -3.2750787 -3.3887498 -3.2537989
2    -0.3196139 -0.3361791 -0.2778220 -0.2601786
3     0.6383677  0.6802807  0.7061901  0.7387774
4     0.9581409  0.9772637  0.9764450  0.9551096
5     0.9987098  0.9946415  0.9929614  0.9829776
bias  0.9995699  0.9996609  0.9965852  0.9872115

$MSE
            -3.01        -1.29        -0.81        -0.49        -0.28
1    1.464844e-03 0.1372070312 0.2395019531 3.005371e-01 0.3327636719
2    3.662109e-04 0.0339355469 0.0645141602 8.245850e-02 0.0879516602
3    9.155273e-05 0.0068054199 0.0141143799 2.333069e-02 0.0271453857
4    2.288818e-05 0.0022354126 0.0035896301 4.131317e-03 0.0054130554
5    7.629395e-06 0.0012702942 0.0012464523 6.513596e-04 0.0004072189
bias 0.000000e+00 0.0001869202 0.0002756119 2.384186e-05 0.0004205704
                0         0.29         0.59         0.89         1.22
1    3.535156e-01 3.369141e-01 3.005371e-01 0.2402343750 0.1398925781
2    1.004639e-01 1.053467e-01 9.393311e-02 0.0699462891 0.0414428711
3    3.128052e-02 2.886963e-02 2.247620e-02 0.0160827637 0.0085906982
4    4.783630e-03 3.341675e-03 1.598358e-03 0.0012893677 0.0014762878
5    1.296997e-04 1.029968e-04 3.767014e-04 0.0003852844 0.0005598068
bias 6.103516e-05 3.433228e-05 2.384186e-05 0.0001869202 0.0004205704

$l.frcs
[1] 32

$thres
          0%          10%          20%          30%          40%          50% 
-3.012163784 -1.286167708 -0.810870055 -0.488905233 -0.275667611 -0.003001333 
         60%          70%          80%          90% 
 0.290458829  0.592367766  0.885523004  1.224207690 

attr(,"class")
[1] "int.scale"
> 
> ## real example.  Data source referenced below.
> 
> data(analysis.dat)
> data(forecast.dat)
> 
> require(waveslim)
[1] TRUE
> require(fields)
[1] TRUE
> 
> A<- int.scale.verify(forecast.dat, analysis.dat,
+ thres = c(0, 2^seq(-5,6)),  main = "NIMROD example" )
> 
> plot(A)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "leps"
> 
> ### * leps
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: leps
> ### Title: Linear Error in Probability Space (LEPS)
> ### Aliases: leps leps.default
> ### Keywords: file
> 
> ### ** Examples
> 
>  obs <- rnorm(100, mean = 1, sd = sqrt(50))
>  pred<-  rnorm(100, mean = 10, sd = sqrt(500))
> 
>  leps(obs, pred) 
> 
>  
> 
> 
> cleanEx(); ..nameEx <- "measurement.error"
> 
> ### * measurement.error
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: measurement.error
> ### Title: Skill score with measurement error.
> ### Aliases: measurement.error
> ### Keywords: file
> 
> ### ** Examples
> 
> DAT<- data.frame( obs = round(runif(50)), frcs = runif(50))
> 
>  measurement.error(DAT$obs, DAT$frcs)
$G
[1] 0.8663916

$p
[1] 0.1759781

$K
[1] 0.2173913

> 
> ### Finley Data
> 
> measurement.error(c(28, 23, 72, 2680)) ## assuming perfect observation,
$G
[1] 20.03877

$p
[1] 3.794388e-06

$K
[1] -0.8627451

>                                        
>          
> 
> 
> cleanEx(); ..nameEx <- "observation.error"
> 
> ### * observation.error
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: observation.error
> ### Title: Observation Error
> ### Aliases: observation.error
> ### Keywords: file
> 
> ### ** Examples
> 
> obs <- round(runif(100))
> gold <- round(runif(100) )
> observation.error(obs, gold)
Warning in return(t = n11/(n11 + n01), u = n10/(n10 + n00)) : 
	 multi-argument returns are deprecated
$t
[1] 0.462963

$u
[1] 0.5

> 
> ## Pirep gold standard
> 
> observation.error(c(43,10,17,4) )
Warning in return(t = n11/(n11 + n01), u = n10/(n10 + n00)) : 
	 multi-argument returns are deprecated
$t
[1] 0.7166667

$u
[1] 0.7142857

> 
> 
> 
> cleanEx(); ..nameEx <- "plot.int.scale"
> 
> ### * plot.int.scale
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.int.scale
> ### Title: Plot Intensity Scale Objects.
> ### Aliases: plot.int.scale
> ### Keywords: file
> 
> ### ** Examples
> 
> data(analysis.dat)
> data(forecast.dat)
> 
> A<- int.scale.verify(forecast.dat, analysis.dat, thres = c(0, 2^seq(-5,6)))
> plot(A)
> plot(A, plot.mse = TRUE)
> plot(A, main = "Test case")
> 
> 
> 
> cleanEx(); ..nameEx <- "reliability.plot"
> 
> ### * reliability.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: reliability.plot
> ### Title: Reliability Plot
> ### Aliases: reliability.plot reliability.plot.default
> ###   reliability.plot.verify
> ### Keywords: file
> 
> ### ** Examples
> 
> ## 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
> 
> reliability.plot(y.i, obar.i, prob.y, titl = "Test 1", legend.names =
+ c("Model A") )
> 
> ## Function will work with a ``prob.bin'' class object as well.
> ## Note this is a very bad forecast.
> obs<- round(runif(100))
> pred<- runif(100)
> 
> A<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")
If baseline is not included, baseline values  will be calculated from the  sample obs. 
> 
> reliability.plot(A, titl = "Alternative plot")
>  
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "roc.area"
> 
> ### * roc.area
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: roc.area
> ### Title: Area under curve (AUC) calculation for Response Operating
> ###   Characteristic curve.
> ### Aliases: roc.area
> ### Keywords: file
> 
> ### ** Examples
> # Data used from Mason and Graham (2002).
> a<- c(1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
+  1991, 1992, 1993, 1994, 1995)
> b<- c(0,0,0,1,1,1,0,1,1,0,0,0,0,1,1)
> c<- c(.8, .8, 0, 1,1,.6, .4, .8, 0, 0, .2, 0, 0, 1,1)
> d<- c(.928,.576, .008, .944, .832, .816, .136, .584, .032, .016, .28, .024, 0, .984, .952)
> 
> A<- data.frame(a,b,c, d)
> names(A)<- c("year", "event", "p1", "p2")
> 
> ## for model with ties
> roc.area(A$event, A$p1)
$A.tilda
[1] 0.8392857

$n.total
[1] 15

$n.events
[1] 7

$n.noevents
[1] 8

$U
[1] 9

$p
[1] 0.01394526

$p.adj
[1] 0.01164102

> 
> ## for model without ties
> roc.area(A$event, A$p2)
$A.tilda
[1] 0.875

$n.total
[1] 15

$n.events
[1] 7

$n.noevents
[1] 8

$U
[1] 7

$p
[1] 0.007543628

$p.adj
[1] 0.007543628

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "roc.plot"
> 
> ### * roc.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: roc.plot
> ### Title: Relative operating characteristic curve.
> ### Aliases: roc.plot roc.plot.default roc.plot.prob.bin
> ### Keywords: file
> 
> ### ** Examples
> 
> # Data from Mason and Graham article.
> 
> a<- c(0,0,0,1,1,1,0,1,1,0,0,0,0,1,1)
> b<- c(.8, .8, 0, 1,1,.6, .4, .8, 0, 0, .2, 0, 0, 1,1)
> c<- c(.928,.576, .008, .944, .832, .816, .136, .584, .032, .016, .28, .024, 0, .984, .952)
> 
> A<- data.frame(a,b,c)
> names(A)<- c("event", "p1", "p2")
> 
> ## for model with ties
> roc.plot(A$event, A$p1)
> 
> ## for model without ties
> roc.plot(A$event, A$p2)
> 
> ### show binormal curve fit.
> 
> roc.plot(A$event, A$p2, binormal = TRUE)
> 
> # icing forecast
> 
> data(prob.frcs.dat)
> A <- verify(prob.frcs.dat$obs, prob.frcs.dat$frcst/100)
If baseline is not included, baseline values  will be calculated from the  sample obs. 
> plot(A, titl = "AWG Forecast")
> 
> # plotting a ``prob.bin'' class object.
> obs<- round(runif(100))
> pred<- runif(100)
> 
> A<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")
If baseline is not included, baseline values  will be calculated from the  sample obs. 
> 
> roc.plot(A, main = "Test 1", binormal = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "verify"
> 
> ### * verify
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: verify
> ### Title: Verification function
> ### Aliases: verify
> ### Keywords: file
> 
> ### ** Examples
> 
> # binary/binary example
> obs<- round(runif(100))
> pred<- round(runif(100))
> 
> A<- verify(obs, pred, frcst.type = "binary", obs.type = "binary")
> summary(A)

The forecasts are binary, the observations are binary.
The contingency table for the forecast 
   pred
obs  0  1
  0 23 29
  1 23 25

PODy =    0.5 
TS   =  0.3067 
ETS  =  -0.01801 
FAR  =  0.5577 
HSS  =  -0.03668 
PC   =   0.48 
BIAS =   1.13 
> #  probabilistic/ binary example
> pred<- runif(100)
> A<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")
If baseline is not included, baseline values  will be calculated from the  sample obs. 
> summary(A)

The forecasts are probablistic, the observations are binary.
Sample baseline calcluated from observations.
Brier Score (BS)           =  0.2819 
Brier Score - Baseline     =  0.2496 
Skill Score                =  -0.1295 
Reliability                =  0.04926 
Resolution                 =  0.01796 
Uncertaintity              =  0.2496 
> # continuous/ continuous example
> obs<- rnorm(100)
> pred<- rnorm(100)
> baseline <- rnorm(100, sd = 0.5) 
> 
> A<- verify(obs, pred, baseline = baseline,  frcst.type = "cont", obs.type = "cont")
> summary(A)

The forecasts are continuous, the observations are continous.
Baseline data provided. 
MAE               =  1.122 
ME                =  0.07728 
MSE               =  2.077 
MSE - baseline    =  1.092 
MSE - persistence =  2.296 
SS  - baseline     =  0.015 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  5.05 1.26 6.34 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')