plm (version 2.6-4)

is.pconsecutive: 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, ...)
# S3 method for default
is.pconsecutive(x, id, time, na.rm.tindex = FALSE, ...)
# S3 method for data.frame
is.pconsecutive(x, index = NULL, na.rm.tindex = FALSE, ...)
# S3 method for pseries
is.pconsecutive(x, na.rm.tindex = FALSE, ...)
# S3 method for pdata.frame
is.pconsecutive(x, na.rm.tindex = FALSE, ...)
# S3 method for panelmodel
is.pconsecutive(x, na.rm.tindex = FALSE, ...)

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 na.rm.tindex to remove those.

Arguments

x

usually, an object of class pdata.frame, data.frame, pseries, or an estimated panelmodel; for the default method x can also be an arbitrary vector or NULL, see Details,

...

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 NA values in the time index are removed before consecutiveness is evaluated (defaults to FALSE),

index

only relevant for data.frame interface; if NULL, the first two columns of the data.frame are assumed to be the index variables; if not NULL, both dimensions ('individual', 'time') need to be specified by index for is.pconsecutive on data frames, for further details see pdata.frame(),

Author

Kevin Tappe

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.

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)