regul.adj: Adjust regulation parameters

In phgrosjean/pastecs: Package for Analysis of Space-Time Ecological Series

Adjust regulation parameters

Description

Calculate and plot an histogram of the distances between interpolated observations in a regulated time series and closest observations in the initial irregular time series. This allows to optimise the tol parameter

Usage

regul.adj(x, xmin=min(x), frequency=NULL,
     deltat=(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))/(length(x) - 1),
     tol=deltat, tol.type="both", nclass=50, col=c(4, 5, 2),
     xlab=paste("Time distance"), ylab=paste("Frequency"),
     main="Number of matching observations", plotit=TRUE, ...)

Arguments

x	a vector with times corresponding to the observations in the irregular initial time series
xmin	the time corresponding to the first observation in the regular time series
frequency	the frequency of observations in the regular time series
deltat	the interval between two successive observations in the regular time series. This is the inverse of frequency. Only one of both parameters need to be given. If both are provided, frequency supersedes deltat
tol	the tolerance in the difference between two matching observations (in the original irregular series and in the regulated series). If tol=0 both values must be strictly identical; a higher value for tol allows some fuzzy matching. tol must be a round fraction of deltat and cannot be higher than it, otherwise, it is adjusted to the closest acceptable value. By default, tol=deltat
tol.type	the type of window to use for the time-tolerance: "left", "right", "both" (by default) or "none". If tol.type="left", corresponding x values are seeked in a window ]xregul-tol, xregul]. If tol.type="right", they are seeked in the window [xregul, xregul+tol[. If tol.type="both", then they are seeked in the window ]xregul-tol, xregul+tol]. If several observations are in this window, the closest one is used. Finally, if tol.type="none", then all observations in the regulated time series are interpolated (even if exactly matching observations exist!)
nclass	the number of classes to compute in the histogram. This is indicative, and will be adjusted by the algorithm to produce a nicely-formatted histogram. The default value is nclass=50. It is acceptable in many cases, but if the histogram is not correct, try a larger value
col	the three colors to use to represent respectively the fist bar (exact coincidence), the middle bars (coincidence in a certain tolerance window) and the last bar (values always interpolated). By default, col=c(4,5,2)
xlab	the label of the x-axis
ylab	the label of the y-axis
main	the main title of the graph
plotit	if plotit=TRUE then the histogram is plotted. Otherwise, it is only calculated
...	additional graph parameters for the histogram

Details

This function is complementary to regul.screen(). While the later look for the best combination of the number of observations, the interval between observations and the position of the first observation on the time-scale for the regular time series, regul.adj() look for the optimal value for tol, the tolerance window.

Value

A list with components:

params	the parameters used for the regular time-scale
match	the number of matching observations in the tolerance window
exact.match	the number of exact matching observations
match.counts	a vector with the number of matching observations for increasing values of tol

Author(s)

Philippe Grosjean ([e-mail]), Frédéric Ibanez ([e-mail])

See Also

regul.screen, regul

Examples

# This example follows the example for regul.screen()
# where we determined that xmin=9, deltat=21, n=63, with tol=1.05
# is a good choice to regulate the irregular time series in 'releve' 
data(releve)
regul.adj(releve$Day, xmin=9, deltat=21)
# The histogram indicates that it is not useful to increase tol
# more than 1.05, because few observations will be added
# except if we increase it to 5-7, but this value could be
# considered to be too large in comparison with deltat=22
# On the other hand, with tol <= 1, the number of matching
# observations will be almost divided by two!

phgrosjean/pastecs documentation built on Feb. 12, 2024, 2:26 a.m.