barplotRenouv: Barplot for Renouv "Over Threshold" counts

In Renext: Renewal Method for Extreme Values Extrapolation

Barplot for Renouv "Over Threshold" counts

Description

Barplot for "Over Threshold" counts in time blocks (usually years)

Usage

 barplotRenouv(data,
               blockname = colnames(data)[1],
               varname = colnames(data)[2],
               threshold = quantile(data[, varname], 0.2),
               na.block = NULL,
               plot = TRUE,
               main = NULL, xlab = NULL, ylab = NULL,
               mono = FALSE,
               prob.theo = 0.999,
               ...)

Arguments

data	A dataframe object containing the variables.
blockname	Name of the "block" variable (column in data). This variable should contain integers, or be of class "factor", but with integer values such as year numbers.
varname	Name of the variable (e.g. "Surge").
threshold	Only obs for which the variable exceeds threshold will be taken into account.
na.block	Values of blocks containing missing values. See the Details section.
plot	If FALSE tests are computed without producing any plot.
main	Character for main title or NULL in which case a default main title is used.
xlab	Character for x axis label or NULL in which case a default lab is used.
ylab	Character for y axis or NULL in which case a default lab is used.
mono	If FALSE barplot will have colors, else greyscale will be used.
prob.theo	The total theoretical probability corresponding to the plotted (theoretical) bars.
...	Further args to be passed to barplot.

Details

Blocks described in the na.block are omitted in the determination of counts. The object given in the na.block is coerced to character and the same is done for values of block before comparing them to the na.block values. If block variable is of class factor with levels representing years (e.g. 1980, 1981, etc.) missing blocks can be specified either as c("1980", "1981") or as numeric c(1980, 1981).

For the chi-square test, counts for neighbouring frequency classes are grouped in order to reach a minimum frequency of 5 in each group. E.g. if we expect respectively 1.0, 3.8 and 7.0 blocks with frequency 0, 1 and 2 for events, the three counts are grouped in one group with frequency 1.0+3.8+7.0=11.8. Note that this strategy of grouping is not unique and is likely to weaken the power of the test. Before grouping, the higher class theoretical probability is computed as the probability to obtain a count equal to or greater than the max value.

Value

A list with the following objects.

freq	frequency table (matrix) giving observed and theoretical (Poisson) frequencies as well as a group number for the chi-square test.
overdispersion	the overdispersion coefficient (variance/mean ratio).
disp.test	a list giving results of the (over)dispersion test. See the reference Yagouti and al. in the References section.
chisq.test	a list giving results for the chis-square test of goodness-of-fit to the Poisson distribution.
tests	a matrix with the two tests displayed in two rows.

For both tests, the statistic follows a chi-square distribution under the null hypothesis . The list of results contains the statistic statistic, the number of degrees of freedom df and the p-value p.value.

Note

The two tests: (over-)dispersion and chi-square have one-sided (upper tail) p-value. In other words, we do not intend to reject when statistics take "abnormally small" values, but only when abnormally large values are met.

Author(s)

Yves Deville

References

See Yagouti A., Abi-Zeid I., Ouarda, T.B.M.J. and B. Bobée (2001), Revue de processus ponctuels et synthèse de tests statistiques pour le choix d'un type de processus Revue des Sciences de l'Eau, 1, pp. 323-361.

See Also

plot.Rendata

Examples

## na.block influence for Brest data
opar <- par(mfrow = c(2, 2))

bp1 <- barplotRenouv(data = Brest.years, threshold = 30,
         main = "missing periods ignored")
bp2 <- barplotRenouv(data = Brest.years, threshold = 30,
         na.block = 1992, main = "1992 missing")
bp3 <- barplotRenouv(data = Brest.years, threshold = 30,
         na.block = 1991:1993, main ="1991:1993 missing")
bp4 <- barplotRenouv(data = Brest.years, threshold = 30,
         na.block = Brest.years.missing, main = "all missing periods")

par(opar)

## threshold influence
opar <- par(mfrow = c(2,2))

thresh <- c(30, 35, 40, 50)

for (i in 1:length(thresh)) {
  bp  <- barplotRenouv(data = Brest.years, threshold = thresh[i],
                   na.block = Brest.years.missing,
                   main = paste("threshold =", thresh[i], "cm at Brest"))
}
par(opar)

Renext documentation built on Aug. 30, 2023, 1:06 a.m.