regul.screen | R Documentation |
Seek for the best combination of the number of observation, the interval between two successive observation and the position of the first observation in the regulated time series to match as much observations of the initial series as possible
regul.screen(x, weight=NULL, xmin=min(x), frequency=NULL,
deltat=(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))/(length(x) - 1),
tol=deltat/5, tol.type="both")
x |
a vector with times corresponding to the observations in the irregular initial time series |
weight |
a vector of the same length as |
xmin |
a vector with all time values for the first observation in the regulated time series to be tested |
frequency |
a vector with all the frequencies to be screened |
deltat |
a vector with all time intervals to screen. |
tol |
it is possible to tolerate some differences in the time between two matching observations (in the original irregular series and in the regulated series). If |
tol.type |
the type of window to use for the time-tolerance: |
Whatever the efficiency of the interpolation procedure used to regulate an irregular time series, a matching, non-interpolated observation is always better than an interpolated one! With very irregular time series, it is often difficult to decide which is the better regular time-scale in order to interpolate as less observations as possible. regul.screen()
tests various combinations of number of observation, interval between two observations and position of the first observation and allows to choose the combination that best matches the original irregular time series. To choose also an optimal value for tol
, use regul.adj()
concurrently.
A list containing:
tol |
a vector with the adjusted values of |
n |
a table indicating the maximum value of |
nbr.match |
a table indicating the number of matching observations (in the tolerance window) for all combinations of |
nbr.exact.match |
a table indicating the number of exactly matching observations (with a tolerance window equal to zero) for all combinations of |
Philippe Grosjean (phgrosjean@sciviews.org), Frédéric Ibanez (ibanez@obs-vlfr.fr)
regul.adj
, regul
data(releve)
# This series is very irregular, and it is difficult
# to choose the best regular time-scale
releve$Day
length(releve$Day)
intervals <- releve$Day[2:61]-releve$Day[1:60]
intervals
range(intervals)
mean(intervals)
# A combination of xmin=1, deltat=22 and n=61 seems correct
# But is it the best one?
regul.screen(releve$Day, xmin=0:11, deltat=16:27, tol=1.05)
# Now we can tell that xmin=9, deltat=21, n=63, with tol=1.05
# is a much better choice!
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