is.pconsecutive {plm} | R Documentation |
This function checks for each individual if its associated time periods are consecutive (no "gaps" in time dimension per individual)
## 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, ...) ## S3 method for class 'pseries' is.pconsecutive(x, na.rm.tindex = FALSE, ...) ## S3 method for class 'data.frame' is.pconsecutive(x, index = NULL, na.rm.tindex = FALSE, ...) ## Default S3 method: is.pconsecutive(x, id, time, na.rm.tindex = FALSE, ...)
x |
usually, an object of class |
na.rm.tindex |
logical indicating whether any |
index |
only relevant for |
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, |
... |
further arguments. |
(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).
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.
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:
|
if the i-th individual has consecutive time periods, |
|
if the i-th individual has non-consecutive time periods, |
|
if there are any NA values in time index of the i-th the individual;
see also argument |
Kevin Tappe
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
.
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)