| is.pconsecutive {plm} | R Documentation |
Check if time periods are consecutive
Description
This function checks for each individual if its associated time periods are consecutive (no "gaps" in time dimension per individual)
Usage
is.pconsecutive(x, ...)
## Default S3 method:
is.pconsecutive(x, id, time, na.rm.tindex = FALSE, ...)
## S3 method for class 'data.frame'
is.pconsecutive(x, index = NULL, na.rm.tindex = FALSE, ...)
## S3 method for class 'pseries'
is.pconsecutive(x, na.rm.tindex = FALSE, ...)
## S3 method for class 'pdata.frame'
is.pconsecutive(x, na.rm.tindex = FALSE, ...)
## S3 method for class 'panelmodel'
is.pconsecutive(x, na.rm.tindex = FALSE, ...)
Arguments
x |
usually, an object of class |
... |
further arguments. |
id, time |
only relevant for default method: vectors specifying the id and time dimensions, i. e., a sequence of individual and time identifiers, each as stacked time series, |
na.rm.tindex |
logical indicating whether any |
index |
only relevant for |
Details
(p)data.frame, pseries and estimated panelmodel objects can be tested if
their time periods are consecutive per individual. For evaluation of
consecutiveness, the time dimension is interpreted to be numeric, and the
data are tested for being a regularly spaced sequence with distance 1
between the time periods for each individual (for each individual the time
dimension can be interpreted as sequence t, t+1, t+2, ... where t is an
integer). As such, the "numerical content" of the time index variable is
considered for consecutiveness, not the "physical position" of the various
observations for an individuals in the (p)data.frame/pseries (it is not
about "neighbouring" rows). If the object to be evaluated is a pseries or a
pdata.frame, the time index is coerced from factor via as.character to
numeric, i.e., the series
as.numeric(as.character(index(<pseries/pdata.frame>)[[2]]))] is
evaluated for gaps.
The default method also works for argument x being an arbitrary
vector (see Examples), provided one can supply arguments id
and time, which need to ordered as stacked time series. As only
id and time are really necessary for the default method to
evaluate the consecutiveness, x = NULL is also possible. However, if
the vector x is also supplied, additional input checking for equality
of the lengths of x, id and time is performed, which is
safer.
For the data.frame interface, the data is ordered in the appropriate way (stacked time series) before the consecutiveness is evaluated. For the pdata.frame and pseries interface, ordering is not performed because both data types are already ordered in the appropriate way when created.
Note: Only the presence of the time period itself in the object is tested,
not if there are any other variables. NA values in individual index
are not examined but silently dropped - In this case, it is not clear which
individual is meant by id value NA, thus no statement about
consecutiveness of time periods for those "NA-individuals" is
possible.
Value
A named logical vector (names are those of the
individuals). The i-th element of the returned vector
corresponds to the i-th individual. The values of the i-th
element can be:
TRUE |
if the i-th individual has consecutive time periods, |
FALSE |
if the i-th individual has non-consecutive time periods, |
"NA" |
if there are any NA values in time index of
the i-th the individual; see also argument |
Author(s)
Kevin Tappe
See Also
make.pconsecutive() to make data consecutive
(and, as an option, balanced at the same time) and
make.pbalanced() to make data balanced.
pdim() to check the dimensions of a 'pdata.frame'
(and other objects), pvar() to check for individual
and time variation of a 'pdata.frame' (and other objects),
lag() for lagged (and leading) values of a
'pseries' object.
pseries(), data.frame(), pdata.frame(),
for class 'panelmodel' see plm() and pgmm().
Examples
data("Grunfeld", package = "plm")
is.pconsecutive(Grunfeld)
is.pconsecutive(Grunfeld, index=c("firm", "year"))
# delete 2nd row (2nd time period for first individual)
# -> non consecutive
Grunfeld_missing_period <- Grunfeld[-2, ]
is.pconsecutive(Grunfeld_missing_period)
all(is.pconsecutive(Grunfeld_missing_period)) # FALSE
# delete rows 1 and 2 (1st and 2nd time period for first individual)
# -> consecutive
Grunfeld_missing_period_other <- Grunfeld[-c(1,2), ]
is.pconsecutive(Grunfeld_missing_period_other) # all TRUE
# delete year 1937 (3rd period) for _all_ individuals
Grunfeld_wo_1937 <- Grunfeld[Grunfeld$year != 1937, ]
is.pconsecutive(Grunfeld_wo_1937) # all FALSE
# pdata.frame interface
pGrunfeld <- pdata.frame(Grunfeld)
pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period)
is.pconsecutive(pGrunfeld) # all TRUE
is.pconsecutive(pGrunfeld_missing_period) # first FALSE, others TRUE
# panelmodel interface (first, estimate some models)
mod_pGrunfeld <- plm(inv ~ value + capital, data = Grunfeld)
mod_pGrunfeld_missing_period <- plm(inv ~ value + capital, data = Grunfeld_missing_period)
is.pconsecutive(mod_pGrunfeld)
is.pconsecutive(mod_pGrunfeld_missing_period)
nobs(mod_pGrunfeld) # 200
nobs(mod_pGrunfeld_missing_period) # 199
# pseries interface
pinv <- pGrunfeld$inv
pinv_missing_period <- pGrunfeld_missing_period$inv
is.pconsecutive(pinv)
is.pconsecutive(pinv_missing_period)
# default method for arbitrary vectors or NULL
inv <- Grunfeld$inv
inv_missing_period <- Grunfeld_missing_period$inv
is.pconsecutive(inv, id = Grunfeld$firm, time = Grunfeld$year)
is.pconsecutive(inv_missing_period, id = Grunfeld_missing_period$firm,
time = Grunfeld_missing_period$year)
# (not run) demonstrate mismatch lengths of x, id, time
# is.pconsecutive(x = inv_missing_period, id = Grunfeld$firm, time = Grunfeld$year)
# only id and time are needed for evaluation
is.pconsecutive(NULL, id = Grunfeld$firm, time = Grunfeld$year)