extremefit: dataOyster – R documentation

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dataOyster

High-frequency noninvasive valvometry data

Description

The data frame provides the opening amplitude of one oyster's shells (in mm) with respect to the time (in hours). The opening velocity of the oyster's shells is also given.

Usage

data("dataOyster")

Format

A list of 2 elements.

$data : a data frame with 54000 observations for 3 variables

time: Time of measurement (in hours).
opening: opening amplitude between the two shells (in mm).
velocity: a numeric vector (in mm/s). Negative values correspond to the opening velocity of the shells and positive values to the closing velocity of the shells.

$Tgrid : A grid of time to perform the procedure.

References

Durrieu, G., Grama, I., Pham, Q. & Tricot, J.- M (2015). Nonparametric adaptive estimator of extreme conditional tail probabilities quantiles. Extremes, 18, 437-478.

Azais, R., Coudret R. & Durrieu G. (2014). A hidden renewal model for monitoring aquatic systems biosensors. Environmetrics, 25.3, 189-199.

Schmitt, F. G., De Rosa, M., Durrieu, G., Sow, M., Ciret, P., Tran, D., & Massabuau, J. C. (2011). Statistical study of bivalve high frequency microclosing behavior: Scaling properties and shot noise analysis. International Journal of Bifurcation and Chaos, 21(12), 3565-3576.

Sow, M., Durrieu, G., Briollais, L., Ciret, P., & Massabuau, J. C. (2011). Water quality assessment by means of HFNI valvometry and high-frequency data modeling. Environmental monitoring and assessment, 182(1-4), 155-170.

website : http://molluscan-eye.epoc.u-bordeaux1.fr/

Examples

data("dataOyster")
Velocity <- dataOyster$data[, 3]
time <- dataOyster$data[, 1]
plot(time, Velocity, type = "l", xlab = "time (hour)",
      ylab = "Velocity (mm/s)")

Tgrid <- seq(0, 24, 0.05) 
#Grid with positive velocity
new.Tgrid <- dataOyster$Tgrid

X <- Velocity + (-min(Velocity)) #We shift the data to be positive

## Not run:  #For computing time purpose
#We find the h by minimizing the cross validation function 

hgrid <- bandwidth.grid(0.05, 0.5, 50, type = "geometric")


#H <- bandwidth.CV(X, time, new.Tgrid, hgrid,
#                 TruncGauss.kernel, kpar = c(sigma = 1),
#                 pcv = 0.99, CritVal = 3.4, plot = TRUE)
#hcv <- H$h.cv

hcv <- 0.2981812
#we use our method with the h found previously
TS.Oyster <- hill.ts(X, t = time, new.Tgrid, h = hcv, 
                   TruncGauss.kernel, kpar = c(sigma = 1),
                   CritVal = 3.4)
          
plot(time, Velocity, type = "l", ylim = c(-0.6, 1),
    main = "Extreme quantiles estimator",
    xlab = "Time (hour)", ylab = "Velocity (mm/s)")
pgrid <- c(0.999)
pred.quant.Oyster <- predict(TS.Oyster, newdata = pgrid, type = "quantile")

quant0.999 <- rep(0, length(Tgrid))
quant0.999[match(new.Tgrid, Tgrid)] <- 
          as.numeric(pred.quant.Oyster$y)-
          (-min(Velocity))
lines(Tgrid, quant0.999, col = "magenta")   



## End(Not run)

extremefit

Estimation of Extreme Conditional Quantiles and Probabilities

v1.0.2

GPL-2

Authors

Gilles Durrieu, Ion Grama, Kevin Jaunatre, Quang-Khoai Pham, Jean-Marie Tricot

Initial release

2019-05-03