library(Renext)

##=======================================================================
## In this test, a sample of independent exceedances Over a Threshold
## is drawn at random from an exponential distribution with mean
## theta. The corresponding model is fitted.
##
## We the add the information that the (high) level 'X.U'
## was never observed over aperiod of duration 'w.U'. We control the
## theoretical relation between estimates
##
##      theta.hat.new = theta.hat.old
##                    - y.U * (lambda.hat.new / lambda.hat.old - 1)
## 
## where 'old' is for OT only and 'new' is for OT AND Unobserved.
##
##
## Note that the predicted return level curve will be far away
## from the observed points, since the unobserved level has a high
## leverage on the estimated parameters. Actually the unobserved
## duaration is more than the double of that of the main sample.
## 
##=========================================================================

set.seed(123)

n <- 20
theta <- 100
x <- 35 + theta*rexp(n)

x.U <- 100
w.U <- 100 + 100*runif(1)

fitExp <- list()

opar <- par(mfrow = c(2, 1), mar = c(5, 5, 3, 1))

fitExp[[1]] <- 
  Renouv(x = x,
         effDuration = 50,
         distname.y = "exponential",
         threshold = 35,
         conf.pct = c(70, 95),
         prob.max = 0.99995,
         pred.period = c(10, 100, 1000),
         trace = 1,
         main = "\"exponential\" without x.U")

fitExp[[2]] <- 
  Renouv(x = x,
         effDuration = 50,
         OTS.data = numeric(0),    ## Unobserved
         OTS.effDuration = w.U,
         OTS.threshold = x.U,        
         distname.y = "exponential",
         threshold = 35,
         conf.pct = c(70, 95),
         prob.max = 0.99995,
         pred.period = c(10, 100, 1000),
         control.H = list(fnscale = -1, trace = 3),
         trace = 1,
         main = "\"exponential\" plus unobserved lev. x.U ")

par(opar)

estimates <- 
  array(NA,
        dim = c(2, length(fitExp[[1]]$estimate)),
        dimnames = list(c("sans", "US"),
          names(fitExp[[1]]$estimate)))

for (i in 1:2) estimates[i, ] <- fitExp[[i]]$estimate

Test <- 1/estimates["US", "rate"] -
  1/estimates["sans", "rate"] -
  (x.U - 35) * (estimates["US", "lambda"]/estimates["sans", "lambda"] -1) 

## L'erruer doit être peite, disons ne pas dépasser 1%
RelError <- abs(Test)*estimates["sans", "rate"]

stopifnot(RelError < 1.0)