regul.adj | R Documentation |
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
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, ...)
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 |
tol |
the tolerance in the difference 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: |
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 |
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, |
xlab |
the label of the x-axis |
ylab |
the label of the y-axis |
main |
the main title of the graph |
plotit |
if |
... |
additional graph parameters for the histogram |
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.
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 |
Philippe Grosjean (phgrosjean@sciviews.org), Frédéric Ibanez (ibanez@obs-vlfr.fr)
regul.screen
, regul
# 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!
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