dataOyster {extremefit}R Documentation

High-frequency noninvasive valvometry data


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.




A list of 2 elements.

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

Time of measurement (in hours).


opening amplitude between the two shells (in mm).


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.


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 :


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$

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)] <- 
lines(Tgrid, quant0.999, col = "magenta")   

## End(Not run)

[Package extremefit version 1.0.2 Index]