regul.adj {pastecs} | 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, tol=deltat, tol.type="both", nclass=50, col=c(4, 5, 2), 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 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) |
plotit |
if plotit=TRUE then the histogram is plotted. Otherwise, it is only calculated |
... |
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 tol |
Philippe Grosjean (phgrosjean@sciviews.org), Frédéric Ibanez (ibanez@obs-vlfr.fr)
# 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!