Transform an irregular time series in a regular time series, or fill gaps in regular time series using the constant value method

```
regconst(x, y=NULL, xmin=min(x), n=length(x),
deltat=(max(x) - min(x))/(n - 1), rule=1, f=0)
```

An object of type 'regul' is returned. It has methods `print()`

, `summary()`

, `plot()`

, `lines()`

, `identify()`

, `hist()`

, `extract()`

and `specs()`

.

- x
a vector with time in the irregular series. Missing values are allowed

- y
a vector of same length as

`x`

and holding observations at corresponding times- xmin
allows to respecify the origin of time in the calculated regular time series. By default, the origin is not redefined and it is equivalent to the smallest value in

`x`

- n
the number of observations in the regular time series. By default, it is the same number than in the original irregular time series (i.e.,

`length(x)`

- deltat
the time interval between two observations in the regulated time series

- rule
the rule to use for extrapolated values (outside of the range in the initial irregular time series) in the regular time series. With

`rule=1`

(by default), these entries are not calculated and get`NA`

; with`rule=2`

, these entries are extrapolated- f
coefficient giving more weight to the left value (

`f=0`

, by default), to the right value (`f=`

) or to a combination of these two observations (0 < f <1)

Frédéric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean (phgrosjean@sciviews.org)

This is the simplest, but the less powerful regulation method. Interpolated values are calculated according to existing observations at left and at right as: x[reg] = x[right]*f + x[left]*(f-1), with 0 < f < 1.

`regul`

, `regarea`

, `reglin`

, `regspline`

, `regul.screen`

, `regul.adj`

, `tseries`

, `is.tseries`

```
data(releve)
reg <- regconst(releve$Day, releve$Melosul)
plot(releve$Day, releve$Melosul, type="l")
lines(reg$x, reg$y, col=2)
```

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